diff --git a/docs/source/benchmarking/agriculture/agro.rst b/docs/source/benchmarking/agriculture/agro.rst index 6f6710e..3ceb221 100644 --- a/docs/source/benchmarking/agriculture/agro.rst +++ b/docs/source/benchmarking/agriculture/agro.rst @@ -25,29 +25,29 @@ Agronomy Ontology (AgrO) ======================================================================================================== + The Agronomy Ontology (AgrO) provides terms from the agronomy domain -that are semantically organized and facilitate the collection, storage, -and use of agronomic data, enabling easier interpretation and reuse by -both humans and machines [#cgiar]_ [#obo]_. To analyze the effects of -varying practices within cropping systems, it is often necessary to -integrate data from multiple disciplinary domains. For example, data on -field management, soil, weather, and crop phenotypes may need to be -combined to assess crop performance under different management -interventions. However, agronomic data are often collected, described, -and stored in inconsistent ways, which impedes data comparison, mining, -interpretation, and reuse [#cgiar]_. The use of standards for metadata -and data annotation plays a key role in addressing these challenges. -While the CG Core Metadata Schema provides a metadata standard to -describe agricultural datasets, the Agronomy Ontology enables the -description of agronomic variables using standardized and semantically -defined terms [#cgiar]_ [#agrofims]_. AgrO specifically covers agronomic -practices, techniques, and variables used in agronomic experiments and -reuses terms from other ontologies to support interoperability [#obo]_. - -**Example Usage**: Annotate agronomic field experiment data with AgrO terms for management -practices, treatments, and measured variables to enable standardized -description, interoperable storage, and cross-study comparison of -agricultural data [#obo]_ [#agrofims]_. +that are semantically organized to support the collection, integration, +and reuse of agronomic data across disciplinary domains [#agro]_. +To analyze the effects of varying practices within cropping systems, it +is often necessary to integrate data from multiple disciplinary domains, +including field management, soil, weather, and crop phenotype data. +AgrO was developed to address the inconsistent description and storage +of agronomic data, which can hinder comparison, interpretation, and +reuse across studies and information systems [#agro]_. The use of +standards for metadata and data annotation plays a key role in +addressing these challenges. AgrO enables the description of agronomic +variables using standardized and semantically defined terms and covers +agronomic practices, techniques, and variables used in agronomic +experiments [#agro]_. The ontology is available through AgroPortal for +browsing and access [#agroportal]_. + +**Example Usage**: Annotate agronomic field experiment data with AgrO +terms for management practices, treatments, and measured variables to +support standardized description, improved interoperability, and +cross-study comparison across agricultural datasets [#agro]_ +[#agroportal]_. + Metrics & Statistics -------------------------- @@ -160,11 +160,20 @@ Use the following code to import this ontology programmatically: References ---------- -.. [#cgiar] CGIAR. n.d. "Agronomy Ontology." - Available at: https://bigdata.cgiar.org/resources/agronomy-ontology/ +.. [#agro] Devare, M., Aubert, C., Laporte, M.-A., Valette, L., + Arnaud, E., and Buttigieg, P. L. 2016. + "Data-driven Agricultural Research for Development: + A Need for Data Harmonization Via Semantics." + In *Proceedings of the 7th International Conference on + Biomedical Ontology (ICBO 2016)*. + CEUR Workshop Proceedings, Vol. 1747. + Available at: + https://ceur-ws.org/Vol-1747/IT205_ICBO2016.pdf -.. [#obo] OBO Foundry. n.d. "Agronomy Ontology (AGRO)." - Available at: https://obofoundry.org/ontology/agro.html +.. [#agroportal] Devare, M., Aubert, C., Laporte, M.-A., Valette, L., + Arnaud, E., and Buttigieg, P. L. n.d. "Agronomy Ontology (AgrO)." + Available at: + https://agroportal.lirmm.fr/ontologies/AGRO?p=summary .. [#agrofims] Devare, M., Aubert, C., Benites Alfaro, O. E., Perez Masias, I. O., and Laporte, M.-A. 2021. diff --git a/docs/source/benchmarking/agriculture/agrovoc.rst b/docs/source/benchmarking/agriculture/agrovoc.rst index ec4e6e1..dd7dcdc 100644 --- a/docs/source/benchmarking/agriculture/agrovoc.rst +++ b/docs/source/benchmarking/agriculture/agrovoc.rst @@ -25,26 +25,26 @@ AGROVOC Multilingual Thesaurus (AGROVOC) AGROVOC is a multilingual thesaurus and Linked Open Data resource developed and maintained by the Food and Agriculture Organization (FAO) -of the United Nations [#fao-home]_ [#agrovoc-paper]_. It provides a +of the United Nations [#fao-home]_ [#linked-dataset]_. It provides a structured collection of agricultural concepts, terms, definitions, and relationships that support unambiguous resource identification, -standardized indexing, and more efficient search [#fao-home]_. As a -multilingual knowledge organization system, AGROVOC facilitates access -to agricultural information across domains and languages [#fao-home]_ -[#agrovoc-paper]_. It covers concepts relevant to food, agriculture, -fisheries, forestry, environment, and related domains, and supports -semantic interoperability through hierarchical and associative +standardized indexing, and more efficient search [#fao-home]_. +As a multilingual knowledge organization system, AGROVOC facilitates +access to agricultural information across domains and languages +[#fao-home]_ [#linked-dataset]_. It covers concepts relevant to food, +agriculture, fisheries, forestry, environment, and related domains, and +supports semantic interoperability through hierarchical and associative relationships as well as links to other vocabularies and datasets -[#fao-home]_ [#linked-dataset]_. With over 41,000 concepts and extensive -multilingual term coverage, AGROVOC is widely used for data annotation, -knowledge organization, and information retrieval in agricultural and -food-related information systems [#fao-dpg]_ [#agrovoc-paper]_. +[#fao-home]_ [#linked-dataset]_. AGROVOC is widely used for data +annotation, knowledge organization, and information retrieval in +agricultural and food-related information systems [#fao-home]_ +[#linked-dataset]_. -**Example Usage**: Annotate a multilingual agricultural dataset with AGROVOC concepts for -crops, soil types, pests, livestock, and farming practices to enable -standardized indexing, semantic interoperability, and cross-language -search across international agricultural databases and repositories -[#fao-home]_ [#agrovoc-paper]_. +**Example Usage**: Annotate a multilingual agricultural dataset with +AGROVOC concepts for crops, soil types, pests, livestock, and farming +practices to enable standardized indexing, semantic interoperability, +and cross-language search across agricultural databases and repositories +[#fao-home]_ [#linked-dataset]_. Metrics & Statistics -------------------------- @@ -158,22 +158,12 @@ References ---------- .. [#fao-home] Food and Agriculture Organization of the United Nations - (FAO). n.d. "AGROVOC." Available at: + (FAO). n.d. "AGROVOC." + Available at: https://www.fao.org/agrovoc/ -.. [#agrovoc-paper] Subirats-Coll, I., Kolshus, K., Turbati, A., - Stellato, A., Mietzsch, E., Martini, D., and Zeng, M. 2022. - "AGROVOC: The linked data concept hub for food and agriculture." - *Computers and Electronics in Agriculture* 196:105965. - doi:10.1016/j.compag.2020.105965 - .. [#linked-dataset] Caracciolo, C., Stellato, A., Morshed, A., Johannsen, G., Rajbhandari, S., Jaques, Y., and Keizer, J. 2013. "The AGROVOC Linked Dataset." *Semantic Web* 4(3):341-348. Available at: https://www.fao.org/agrovoc/publications/agrovoc-linked-dataset - -.. [#fao-dpg] Food and Agriculture Organization of the United Nations - (FAO). 2024. "AGROVOC is now a certified Digital Public Good!" - Available at: - https://www.fao.org/agora/news/agrovoc-now-certified-digital-public-good diff --git a/docs/source/benchmarking/agriculture/atol.rst b/docs/source/benchmarking/agriculture/atol.rst index 360d42c..90dd4f6 100644 --- a/docs/source/benchmarking/agriculture/atol.rst +++ b/docs/source/benchmarking/agriculture/atol.rst @@ -161,8 +161,9 @@ References .. [#inra] INRAE Open Data. n.d. "Animal Trait Ontology for Livestock." Available at: https://opendata.inra.fr/ATOL/page/ -.. [#agroportal] AgroPortal. n.d. "ATOL | Summary." - Available at: https://agroportal.lirmm.fr/ontologies/ATOL +.. [#agroportal] AgroPortal. n.d. "Animal Trait Ontology for Livestock (ATOL)." + Available at: + https://agroportal.lirmm.fr/ontologies/ATOL .. [#atol-paper] Golik, W., Dameron, O., Bugeon, J., Fatet, A., Hue, I., Hurtaud, C., Reichstadt, M., Salaün, M.-C., Vernet, J., Joret, L., diff --git a/docs/source/benchmarking/agriculture/po.rst b/docs/source/benchmarking/agriculture/po.rst index 621c770..ed4a992 100644 --- a/docs/source/benchmarking/agriculture/po.rst +++ b/docs/source/benchmarking/agriculture/po.rst @@ -30,25 +30,22 @@ resource that links plant anatomy, morphology, growth, and development to plant genomics and phenomics data [#obo]_ [#po-paper]_. Developed as a community resource, PO provides a framework for describing plant structures and developmental stages across plant species [#obo]_ -[#po-dev-paper]_. The ontology integrates anatomical and developmental +[#po-paper]_. The ontology integrates anatomical and developmental terms that can be associated with plant genes and phenotypes, enabling researchers to annotate data and support comparative genomics and -comparative plant biology [#po-paper]_ [#po-dev-paper]_. PO is designed -to facilitate data integration and interoperability in plant science -research [#obo]_ [#po-paper]_. With its hierarchical organization of -plant structures and developmental stages, including whole plants, -organs, tissues, and cell types, PO supports applications such as -literature curation, genome annotation, and phenotypic data annotation -[#po-paper]_. The ontology is under active development and is integrated -with the Planteome project and other biological ontologies to support -semantic compatibility in the plant science community [#obo]_ -[#planteome]_. - -**Example Usage**: Annotate a plant genomics or phenomics dataset with PO terms for plant -structures and developmental stages, such as leaf, root, flower, seed, -or senescent stage, to enable standardized annotation, cross-species -comparison, and integration with plant science databases and analysis -platforms [#obo]_ [#po-paper]_. +comparative plant biology [#po-paper]_. PO is designed to facilitate +data integration and interoperability in plant science research +[#obo]_ [#po-paper]_. With its hierarchical organization of plant +structures and developmental stages, including whole plants, organs, +tissues, and cell types, PO supports applications such as literature +curation, genome annotation, and phenotypic data annotation +[#po-paper]_. + +**Example Usage**: Annotate a plant genomics or phenomics dataset with +PO terms for plant structures and developmental stages, such as leaf, +root, flower, seed, or senescent stage, to enable standardized +annotation, cross-species comparison, and integration with plant +science databases and analysis platforms [#obo]_ [#po-paper]_. Metrics & Statistics -------------------------- @@ -171,18 +168,3 @@ References *Plant and Cell Physiology* 54(2): e1. doi:10.1093/pcp/pcs163 Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC3583023/ `_ - -.. [#po-dev-paper] Walls, R. L., Cooper, L., Elser, J., Gandolfo, M. A., - Mungall, C. J., Smith, B., Stevenson, D. W., and Jaiswal, P. 2019. - "The Plant Ontology Facilitates Comparisons of Plant Development - Stages Across Species." - *Frontiers in Plant Science* 10:631. - doi:10.3389/fpls.2019.00631 - Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC6558174/ `_ - -.. [#planteome] Cooper, L., Elser, J., Laporte, M.-A., Arnaud, E., - and Jaiswal, P. 2024. "Planteome 2024 Update: Reference Ontologies - and Knowledgebase for Plant Biology." - *Nucleic Acids Research* 52(D1): D1548-D1555. - doi:10.1093/nar/gkad1028 - Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC10767901/ `_ diff --git a/docs/source/benchmarking/arts_and_humanities/chordontology.rst b/docs/source/benchmarking/arts_and_humanities/chordontology.rst index 1ad1bb1..dc4a9c2 100644 --- a/docs/source/benchmarking/arts_and_humanities/chordontology.rst +++ b/docs/source/benchmarking/arts_and_humanities/chordontology.rst @@ -25,28 +25,32 @@ Chord Ontology (ChordOntology) ======================================================================================================== -The Chord Ontology is a formal representation for describing and classifying -chords and chord sequences in musical resources. It provides a structured -vocabulary for representing harmonic concepts and chord structures, enabling -semantic annotation and analysis of music data. The ontology captures core -chord properties including chord type (for example major, minor, diminished, -and augmented), root note, constituent intervals, and bass note. It supports -the annotation of audio files, musical scores, and symbolic music files by -linking chord events to temporal structures and music resources. The ontology -was developed within the OMRAS2 project and is designed to interoperate with -related Semantic Web resources such as the Music Ontology, Timeline Ontology, -and Event Ontology. By formalizing chord relationships and structures, the -Chord Ontology supports computational music analysis, harmonic annotation, -music information retrieval, and digital musicology applications. It provides -a common framework for music annotation across datasets and tools, supporting +The Chord Ontology is a formal representation for describing and +classifying chords and chord sequences in musical resources +[#chord-spec]_. It provides a structured vocabulary for representing +harmonic concepts and chord structures, enabling semantic annotation and +analysis of music data [#chord-spec]_. The ontology captures core chord +properties including chord type, such as major, minor, diminished, and +augmented, as well as root note, constituent intervals, and bass note +[#chord-spec]_. It supports the annotation of audio files, musical +scores, and symbolic music files by linking chord events to temporal +structures and music resources [#chord-spec]_. The ontology was +developed within the OMRAS2 project and is designed to interoperate with +related Semantic Web resources such as the Music Ontology, Timeline +Ontology, and Event Ontology [#chord-spec]_. By formalizing chord +relationships and structures, the Chord Ontology supports computational +music analysis, harmonic annotation, music information retrieval, and +digital musicology applications [#chord-spec]_. It provides a common +framework for music annotation across datasets and tools, supporting harmonic analysis, corpus annotation, and music information systems -development [#chord-spec]_ [#omras2]_ [#music-ontology]_. +development [#chord-spec]_. **Example Usage**: Annotate the harmonic timeline of an audio recording, -musical score, or symbolic music file with Chord Ontology terms for chord -events, root notes, intervals, and bass notes to enable semantic search, -computational harmonic analysis, and integration with music information -retrieval datasets and tools [#chord-spec]_ [#omras2]_. +musical score, or symbolic music file with Chord Ontology terms for +chord events, root notes, intervals, and bass notes to enable semantic +search, computational harmonic analysis, and integration with music +information retrieval datasets and tools [#chord-spec]_. + Metrics & Statistics -------------------------- @@ -162,17 +166,4 @@ References .. [#chord-spec] Sutton, C., Raimond, Y., and Mauch, M. 2007. "Chord Ontology Specification." OMRAS2 Project, Centre for Digital Music, Queen Mary University of London. - Available at: http://purl.org/ontology/chord/ - Also available at: https://motools.sourceforge.net/chord_draft_1/chord.html - -.. [#omras2] Fazekas, G., Raimond, Y., Jacobson, K., and Sandler, M. 2010. - "An Overview of Semantic Web Activities in the OMRAS2 Project." - *Journal of New Music Research* 39(4): 295-311. - doi:10.1080/09298215.2010.536555 - -.. [#music-ontology] Raimond, Y., Abdallah, S. A., Sandler, M. B., - and Giasson, F. 2007. - "The Music Ontology." - In *Proceedings of the 8th International Conference on Music Information - Retrieval (ISMIR 2007)*, Vienna, Austria, pp. 417-422. - Available at: https://ismir2007.ismir.net/proceedings/ISMIR2007_p417_raimond.pdf + Available at: https://motools.sourceforge.net/chord_draft_1/chord.html diff --git a/docs/source/benchmarking/arts_and_humanities/icon.rst b/docs/source/benchmarking/arts_and_humanities/icon.rst index fcdb9b9..8f357f3 100644 --- a/docs/source/benchmarking/arts_and_humanities/icon.rst +++ b/docs/source/benchmarking/arts_and_humanities/icon.rst @@ -164,6 +164,10 @@ Use the following code to import this ontology programmatically: References ---------- +.. [#icon-doc] ICON Ontology Documentation. n.d. + "ICON Ontology Documentation 2.0." + Available at: https://br0ast.github.io/ICON/ICONOntologyDocumentation2.0/index-en.html + .. [#icon-paper] Sartini, B., Baroncini, S., van Erp, M., Tomasi, F., and Gangemi, A. 2023. "ICON: An Ontology for Comprehensive Artistic Interpretations." @@ -176,8 +180,4 @@ References In *Proceedings of the Semantic Web and Ontology Design for Cultural Heritage Workshop (SWODCH 2023)*. CEUR Workshop Proceedings 3540. - Available at: `https://ceur-ws.org/Vol-3540/paper4.pdf `_ - -.. [#icon-doc] ICON Ontology Documentation. n.d. - "ICON Ontology Documentation 2.0." - Available at: `https://br0ast.github.io/ICON/ICONOntologyDocumentation2.0/index-en.html `_ + Available at: https://ceur-ws.org/Vol-3540/paper4.pdf diff --git a/docs/source/benchmarking/arts_and_humanities/nomisma.rst b/docs/source/benchmarking/arts_and_humanities/nomisma.rst index 4b94637..6a88077 100644 --- a/docs/source/benchmarking/arts_and_humanities/nomisma.rst +++ b/docs/source/benchmarking/arts_and_humanities/nomisma.rst @@ -27,23 +27,24 @@ Nomisma Ontology (Nomisma) The Nomisma Ontology is a collaborative framework that provides stable, standardized digital representations of numismatic concepts according to -the principles of Linked Open Data. It offers HTTP URIs that provide -persistent access to reusable information about numismatic entities and -related concepts, enabling integration with other linked data resources -[#nomisma-project]_ [#numismatics-lod]_. The Nomisma community maintains -a formalized RDF ontology and a data model for encoding concepts, coins, -typologies, hoards, and other kinds of numismatic objects as linked open -data [#nomisma-project]_. The ontology and controlled vocabulary support -the description of entities such as coins, denominations, mints, rulers, -regions, and related numismatic concepts across different periods and -cultures [#nomisma-project]_ [#diginuma]_. It facilitates semantic -annotation of numismatic data and supports interoperability across -digital coin collections, archaeological datasets, and historical -research resources [#numismatics-lod]_ [#diginuma]_. By providing -standardized semantic representations, Nomisma enables querying, -integration, and comparative analysis of monetary and material culture -data within broader cultural heritage and digital humanities ecosystems -[#numismatics-lod]_ [#semantic-solutions]_. +the principles of Linked Open Data [#nomisma-project]_ [#numismatics-lod]_. +It offers HTTP URIs that provide persistent access to reusable +information about numismatic entities and related concepts, enabling +integration with other linked data resources [#nomisma-project]_ +[#numismatics-lod]_. The Nomisma community maintains a formalized RDF +ontology and data model for encoding concepts, coins, typologies, +hoards, and other kinds of numismatic objects as linked open data +[#nomisma-project]_ [#numismatics-lod]_. The ontology and controlled +vocabulary support the description of entities such as coins, +denominations, mints, rulers, regions, and related numismatic concepts +across different periods and cultures [#nomisma-project]_. It +facilitates semantic annotation of numismatic data and supports +interoperability across digital coin collections, archaeological +datasets, and historical research resources [#numismatics-lod]_. +By providing standardized semantic representations, Nomisma enables +querying, integration, and comparative analysis of monetary and +material culture data within broader cultural heritage and digital +humanities ecosystems [#numismatics-lod]_. **Example Usage**: Annotate a digital coin collection, hoard dataset, or archaeological numismatic catalog with Nomisma terms for coin types, @@ -169,15 +170,3 @@ References .. [#numismatics-lod] Gruber, E. 2021. "Numismatics and Linked Open Data." *ISAW Papers* 20(6). Available at: `https://dlib.nyu.edu/awdl/isaw/isaw-papers/20-6/ `_ - -.. [#diginuma] Rantala, H., Oksanen, E., Ehrnsten, F., and Hyvönen, E. 2022. - "Harmonizing and Using Numismatic Linked Data in Digital Humanities - Research and Application Development: Case DigiNUMA." - In *The Semantic Web: ESWC 2022 Satellite Events*, pp. 141-146. - Available at: `https://2022.eswc-conferences.org/wp-content/uploads/2022/05/pd_Rantala_et_al_paper_238.pdf `_ - -.. [#semantic-solutions] Hyvönen, E., Rantala, H., Oksanen, E., and - Ehrnsten, F. 2023. "Semantic Solutions for Democratizing - Archaeological and Numismatic Data." - *Journal on Computing and Cultural Heritage* 16(4). - doi:10.1145/3625302 diff --git a/docs/source/benchmarking/arts_and_humanities/timelineontology.rst b/docs/source/benchmarking/arts_and_humanities/timelineontology.rst index 3af397a..12e8683 100644 --- a/docs/source/benchmarking/arts_and_humanities/timelineontology.rst +++ b/docs/source/benchmarking/arts_and_humanities/timelineontology.rst @@ -26,32 +26,33 @@ Timeline Ontology (TimelineOntology) ======================================================================================================== The Timeline Ontology provides a formal framework for representing and -managing temporal information in multimedia and music contexts. It is -centered around the notion of timelines as temporal backbones that can -support various types of media and temporal objects, including signals, -videos, performances, scores, and musical works [#timeline-spec]_. The -ontology enables precise temporal annotation by allowing instants and -intervals to be defined on a timeline, supporting structured -representation of time-based relationships between different media -components [#timeline-spec]_. It supports temporal modelling of -durations, intervals, and temporal positions within multimedia and music -resources [#timeline-spec]_ [#mo-paper]_. The Timeline Ontology -facilitates synchronization across different representations, such as -aligning audio signals with musical notation or linking performances and -recordings to temporal metadata [#timeline-spec]_ [#mo-paper]_. It is -particularly useful in music information retrieval, multimedia -annotation, and Semantic Web applications that require machine-readable -temporal descriptions [#timeline-spec]_ [#omras2]_. By providing a -common temporal framework, the Timeline Ontology supports interoperability -across music and media analysis systems and enables temporal querying and -integration of complex time-based data [#timeline-spec]_ [#omras2]_. +managing temporal information in multimedia and music contexts +[#timeline-spec]_. It is centered around the notion of timelines as +temporal backbones that can support various types of media and temporal +objects, including signals, videos, performances, scores, and musical +works [#timeline-spec]_. The ontology enables precise temporal +annotation by allowing instants and intervals to be defined on a +timeline, supporting structured representation of time-based +relationships between different media components [#timeline-spec]_. +It supports temporal modelling of durations, intervals, and temporal +positions within multimedia and music resources [#timeline-spec]_. +The Timeline Ontology facilitates synchronization across different +representations, such as aligning audio signals with musical notation +or linking performances and recordings to temporal metadata +[#timeline-spec]_. It is particularly useful in music information +retrieval, multimedia annotation, and Semantic Web applications that +require machine-readable temporal descriptions [#timeline-spec]_. +By providing a common temporal framework, the Timeline Ontology +supports interoperability across music and media analysis systems and +enables temporal querying and integration of complex time-based data +[#timeline-spec]_. **Example Usage**: Annotate an audio recording, video, or symbolic music file with Timeline Ontology terms for timelines, instants, and intervals in order to align chord events, note events, subtitles, or performance segments with precise temporal positions, enabling temporal querying, cross-media synchronization, and interoperable multimedia -annotation [#timeline-spec]_ [#omras2]_. +annotation [#timeline-spec]_. Metrics & Statistics -------------------------- @@ -168,15 +169,3 @@ References "The Timeline Ontology." OWL ontology specification. Available at: `https://motools.sourceforge.net/timeline/timeline.html `_ - -.. [#mo-paper] Raimond, Y., Abdallah, S. A., Sandler, M. B., - and Giasson, F. 2007. "The Music Ontology." - In *Proceedings of the 8th International Conference on Music - Information Retrieval (ISMIR 2007)*, Vienna, Austria, - pp. 417-422. - Available at: `https://ismir2007.ismir.net/proceedings/ISMIR2007_p417_raimond.pdf `_ - -.. [#omras2] Fazekas, G., Raimond, Y., Jacobson, K., and Sandler, M. 2010. - "An Overview of Semantic Web Activities in the OMRAS2 Project." - *Journal of New Music Research* 39(4): 295-311. - doi:10.1080/09298215.2010.536555 diff --git a/docs/source/benchmarking/biology_and_life_sciences/biopax.rst b/docs/source/benchmarking/biology_and_life_sciences/biopax.rst index a19032e..bd5db3b 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/biopax.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/biopax.rst @@ -28,28 +28,28 @@ Biological Pathways Exchange (BioPAX) BioPAX (Biological Pathway Exchange) is a standard RDF/OWL-based language and ontology for exchanging, integrating, and analyzing biological pathway data [#biopax-paper]_ [#biopax-spec]_. It enables the -representation of molecular interaction networks, including metabolic and -signaling pathways, molecular and genetic interactions, and gene +representation of molecular interaction networks, including metabolic +and signaling pathways, molecular and genetic interactions, and gene regulation processes [#biopax-paper]_ [#biopax-spec]_. BioPAX models -core pathway concepts such as interactions, physical entities -(for example proteins, DNA, RNA, complexes, and small molecules), -pathways, and their associated biological and cellular properties -[#biopax-paper]_ [#biopax-spec]_. The ontology is designed to reduce -complexity in data interchange by providing a unified format that -supports integration across pathway databases, visualization tools, and -computational analysis platforms [#biopax-paper]_. BioPAX is widely used -in pathway informatics and has been adopted by major resources and tools -for pathway data sharing and integration [#biopax-paper]_ [#pathway-commons]_. -By providing a common semantic framework for pathway representation, -BioPAX supports systems biology analysis, pathway visualization, and -interoperable exchange of biological knowledge across diverse resources -[#biopax-paper]_. +core pathway concepts such as interactions, physical entities, including +proteins, DNA, RNA, complexes, and small molecules, pathways, and their +associated biological and cellular properties [#biopax-paper]_ +[#biopax-spec]_. The ontology is designed to reduce complexity in data +interchange by providing a unified format that supports integration +across pathway databases, visualization tools, and computational +analysis platforms [#biopax-paper]_. BioPAX is widely used in pathway +informatics for pathway data sharing and integration [#biopax-paper]_ +[#biopax-spec]_. By providing a common semantic framework for pathway +representation, BioPAX supports systems biology analysis, pathway +visualization, and interoperable exchange of biological knowledge across +diverse resources [#biopax-paper]_ [#biopax-spec]_. **Example Usage**: Represent a phosphorylation event as a BioPAX BiochemicalReaction in which a protein substrate is converted into its phosphorylated form, linked to the relevant catalyst or controller, cellular location, and pathway context to enable pathway exchange, -visualization, and computational analysis [#biopax-spec]_ [#biopax-paper]_. +visualization, and computational analysis [#biopax-spec]_ +[#biopax-paper]_. Metrics & Statistics -------------------------- @@ -189,11 +189,3 @@ References .. [#biopax-spec] BioPAX Editorial Board. n.d. "BioPAX Level 3 Documentation." Available at: `https://biopax.github.io/Paxtools/ `_ - -.. [#pathway-commons] Cerami, E. G., Gross, B. E., Demir, E., - Rodchenkov, I., Babur, O., Anwar, N., Schultz, N., Bader, G. D., - and Sander, C. 2011. "Pathway Commons, a Web Resource for Biological - Pathway Data." - *Nucleic Acids Research* 39(Database issue): D685-D690. - doi:10.1093/nar/gkq1039 - Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC3013641/ `_ diff --git a/docs/source/benchmarking/biology_and_life_sciences/efo.rst b/docs/source/benchmarking/biology_and_life_sciences/efo.rst index 87ceb98..0f2c186 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/efo.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/efo.rst @@ -26,28 +26,24 @@ Experimental Factor Ontology (EFO) The Experimental Factor Ontology (EFO) is a comprehensive ontology developed to provide systematic, standardized descriptions of experimental variables and factors in biological and biomedical research -[#efo-site]_ [#efo-faq]_. EFO integrates terms from multiple biological -ontologies, including UBERON for anatomy, ChEBI for chemical compounds, -and the Cell Ontology, in order to support the annotation, analysis, -and visualization of experimental data [#efo-site]_ [#efo-faq]_. It is -widely used for annotating datasets in EMBL-EBI resources and external -projects such as the NHGRI-EBI GWAS Catalog, and it is also used as the -core ontology for Open Targets [#efo-site]_ [#gwas-2023]_. EFO enables -semantic interoperability, data integration, and ontology-based querying -across diverse datasets, facilitating cross-study comparison and data -reuse [#efo-site]_ [#gwas-2018]_. The ontology is actively maintained at -EMBL-EBI and continues to evolve in response to new data types and -research needs [#efo-team]_ [#efo-site]_. By providing a unified -framework for describing experimental factors, EFO supports data -sharing, discovery, and knowledge integration in genomics, -transcriptomics, and related life science domains [#efo-site]_ -[#gwas-2023]_. +[#efo-site]_ [#efo-paper]_. EFO integrates terms from multiple +biological ontologies in order to support the annotation, analysis, and +visualization of experimental data [#efo-site]_ [#efo-paper]_. It +provides a semantic framework for describing sample variables and +experimental conditions, enabling consistent data annotation and +interoperability across diverse datasets [#efo-paper]_ [#efo-site]_. +EFO is maintained by EMBL-EBI and serves as an important ontology +resource for biological data integration and reuse [#efo-site]_. +By providing a unified framework for describing experimental factors, +EFO supports data sharing, discovery, and knowledge integration in +genomics, transcriptomics, and related life science domains +[#efo-site]_ [#efo-paper]_. **Example Usage**: Annotate a gene expression or association dataset with EFO terms to specify experimental variables such as tissue type, disease or phenotype, treatment, and assay-related factors, enabling semantic search, cross-study comparison, and meta-analysis across -biological datasets [#efo-site]_ [#gwas-2018]_. +biological datasets [#efo-paper]_ [#efo-site]_. Metrics & Statistics -------------------------- @@ -163,30 +159,10 @@ References .. [#efo-site] EMBL-EBI. n.d. "The Experimental Factor Ontology." Available at: `https://www.ebi.ac.uk/efo/ `_ -.. [#efo-faq] EMBL-EBI. n.d. "FAQ EFO." - Available at: `https://www.ebi.ac.uk/efo/faq.html `_ - -.. [#efo-team] EMBL-EBI. n.d. "Samples, Phenotypes and Ontologies." - Available at: `https://www.ebi.ac.uk/about/teams/samples-phenotypes-ontologies/ `_ - -.. [#gwas-2018] Buniello, A., MacArthur, J. A. L., Cerezo, M., - Harris, L. W., Hayhurst, J., Malangone, C., McMahon, A., - Morales, J., Mountjoy, E., Sollis, E., Suveges, D., Vrousgou, O., - Whetzel, P. L., Amode, R., Guillen, J. A., Riat, H. S., - Trevanion, S. J., Hall, P., Junkins, H., Flicek, P., - Burdett, T., Hindorff, L. A., Cunningham, F., and Parkinson, H. - 2019. "The NHGRI-EBI GWAS Catalog of Published Genome-Wide - Association Studies, Targeted Arrays and Summary Statistics 2019." - *Nucleic Acids Research* 47(D1): D1005-D1012. - doi:10.1093/nar/gky1120 - Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC6323933/ `_ - -.. [#gwas-2023] Sollis, E., Mosaku, A., Abid, A., Buniello, A., - Cerezo, M., Gil, L., Groza, T., Güneş, O., Hall, P., - Hayhurst, J. D., McMahon, A., Mountjoy, E., Parton, A., - Paschall, J., Lopes, E. N., Sanseau, P., Shamout, S., - Sheth, T., Riat, H. S., et al. 2023. "NHGRI-EBI GWAS Catalog: - Knowledgebase and Deposition Resource." - *Nucleic Acids Research* 51(D1): D977-D985. - doi:10.1093/nar/gkac1010 - Available at: `https://academic.oup.com/nar/article/51/D1/D977/6814460 `_ +.. [#efo-paper] Malone, J., Holloway, E., Adamusiak, T., + Kapushesky, M., Zheng, J., Kolesnikov, N., Zhukova, A., + Brazma, A., and Parkinson, H. 2010. + "Modeling Sample Variables with an Experimental Factor Ontology." + *Bioinformatics* 26(8): 1112-1118. + Available at: + https://academic.oup.com/bioinformatics/article/26/8/1112/208992 diff --git a/docs/source/benchmarking/biology_and_life_sciences/go.rst b/docs/source/benchmarking/biology_and_life_sciences/go.rst index 79e31d7..17c54ba 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/go.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/go.rst @@ -28,28 +28,28 @@ Gene Ontology (GO) The Gene Ontology (GO) is a comprehensive resource that provides structured controlled vocabularies for the annotation of gene products with respect to their molecular function, cellular component, and -biological process roles [#go-site]_ [#go-paper]_. Developed +biological process roles [#go-site]_ [#go-2026]_. Developed collaboratively by the Gene Ontology Consortium, GO enables consistent annotation of genes and proteins across diverse species and databases -[#go-paper]_ [#go-2026]_. The ontology is organized into three -hierarchical namespaces: Biological Process (BP), describing the larger +[#go-2026]_ [#go-site]_. The ontology is organized into three +hierarchical namespaces: Biological Process, describing the larger biological objectives to which a gene product contributes; Molecular -Function (MF), characterizing its molecular activity; and Cellular -Component (CC), indicating where that activity occurs [#go-overview]_ -[#go-annotations]_. GO supports biological data analysis by enabling +Function, characterizing its molecular activity; and Cellular +Component, indicating where that activity occurs [#go-site]_ +[#go-2026]_. GO supports biological data analysis by enabling researchers to compare gene functions, identify enriched biological processes or functions in genomics datasets, and understand relationships among genes and gene products in biological systems -[#go-paper]_ [#go-2026]_. By providing a shared semantic framework for +[#go-2026]_ [#go-site]_. By providing a shared semantic framework for functional annotation, GO facilitates data integration, comparative genomics, and computational analysis across the life sciences -[#go-paper]_ [#go-2026]_. +[#go-2026]_ [#go-site]_. **Example Usage**: Annotate a protein such as TP53 with GO terms for -biological process, molecular function, and cellular component. For -example, terms related to apoptotic process, DNA binding, and nucleus to +biological process, molecular function, and cellular component, for +example terms related to apoptotic process, DNA binding, and nucleus, to enable standardized functional annotation, enrichment analysis, and -cross-database comparison [#go-annotations]_ [#go-site]_. +cross-database comparison [#go-site]_ [#go-2026]_. Metrics & Statistics -------------------------- @@ -165,19 +165,6 @@ References .. [#go-site] Gene Ontology Consortium. n.d. "The Gene Ontology Resource." Available at: `https://geneontology.org/ `_ -.. [#go-overview] Gene Ontology Consortium. n.d. "Gene Ontology Overview." - Available at: `https://geneontology.org/docs/ontology-documentation/ `_ - -.. [#go-annotations] Gene Ontology Consortium. n.d. - "Introduction to GO Annotations." - Available at: `https://geneontology.org/docs/go-annotations/ `_ - -.. [#go-paper] The Gene Ontology Consortium. 2021. - "The Gene Ontology Resource: Enriching a GOld Mine." - *Nucleic Acids Research* 49(D1): D325-D334. - doi:10.1093/nar/gkaa1113 - Available at: `https://pubmed.ncbi.nlm.nih.gov/33290552/ `_ - .. [#go-2026] The Gene Ontology Consortium. 2026. "The Gene Ontology Knowledgebase in 2026." *Nucleic Acids Research* 54(D1): D1779-D1790. diff --git a/docs/source/benchmarking/biology_and_life_sciences/marinetlo.rst b/docs/source/benchmarking/biology_and_life_sciences/marinetlo.rst index cb705af..f9d43ea 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/marinetlo.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/marinetlo.rst @@ -27,27 +27,28 @@ Marine Taxonomy and Life Ontology (MarineTLO) MarineTLO is a top-level ontology for the marine domain, designed to provide consistent abstractions for concepts appearing across marine -data models and ontologies. It provides the properties needed to make a -distributed marine knowledge base a coherent source of facts, relating -observational data to spatiotemporal context and categorical -(systematic) domain knowledge [#marinetlo-site]_ [#marinetlo-paper]_. -It can be used as a core schema for publishing linked data and for -building integration systems for the marine domain [#marinetlo-site]_ -[#marinetlo-paper]_. MarineTLO is generic enough to be extended to -different levels of detail while preserving monotonicity, and it has -been implemented in OWL 2 and evaluated through competency queries that -capture domain requirements provided by related communities -[#marinetlo-site]_ [#marinetlo-doc]_. By providing a shared top-level -semantic framework, MarineTLO supports semantic interoperability and the -integration of heterogeneous marine biodiversity and observation data -across distributed sources [#marinetlo-paper]_ [#marinetlo-site]_. +data models and ontologies [#marinetlo-paper]_ [#marinetlo-doc]_. It +provides the properties needed to make a distributed marine knowledge +base a coherent source of facts, relating observational data to +spatiotemporal context and categorical, systematic domain knowledge +[#marinetlo-paper]_ [#marinetlo-doc]_. It can be used as a core schema +for publishing linked data and for building integration systems for the +marine domain [#marinetlo-paper]_ [#marinetlo-doc]_. MarineTLO is +generic enough to be extended to different levels of detail while +preserving monotonicity, and it has been implemented in OWL 2 and +evaluated through competency queries that capture domain requirements +provided by related communities [#marinetlo-paper]_ [#marinetlo-doc]_. +By providing a shared top-level semantic framework, MarineTLO supports +semantic interoperability and the integration of heterogeneous marine +biodiversity and observation data across distributed sources +[#marinetlo-paper]_ [#marinetlo-doc]_. **Example Usage**: Use MarineTLO as a core schema to integrate marine species, observations, habitats, and sampling-event data from multiple sources, linking each observation to its taxonomic, spatial, and temporal context to enable semantic querying and interoperable analysis across marine biodiversity datasets [#marinetlo-paper]_ -[#marinetlo-site]_. +[#marinetlo-doc]_. Metrics & Statistics -------------------------- @@ -160,12 +161,10 @@ Use the following code to import this ontology programmatically: References ---------- -.. [#marinetlo-site] Institute of Computer Science, FORTH. 2020. - "MarineTLO | A Top Level Ontology for the Marine/Biodiversity Domain." - Available at: `https://projects.ics.forth.gr/isl/MarineTLO/ `_ - -.. [#marinetlo-doc] Tzitzikas, Y., and collaborators. n.d. - "MarineTLO: A Top Level Ontology for the Marine Domain." +.. [#marinetlo-doc] Tzitzikas, Y., Allocca, C., Bekiari, C., + Marketakis, Y., Fafalios, P., Doerr, M., Minadakis, N., Patkos, T., + and Candela, L. n.d. "MarineTLO: A Top Level Ontology for the Marine + Domain." Documentation. Available at: `https://projects.ics.forth.gr/isl/MarineTLO/files/MarineTLO.pdf `_ @@ -176,4 +175,3 @@ References MarineTLO." *Program* 50(1): 16-40. doi:10.1108/PROG-10-2014-0072 - Available at: `https://www.vliz.be/imisdocs/publications/283055.pdf `_ diff --git a/docs/source/benchmarking/biology_and_life_sciences/mged.rst b/docs/source/benchmarking/biology_and_life_sciences/mged.rst index aec9885..9555e13 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/mged.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/mged.rst @@ -24,23 +24,19 @@ MGED Ontology (MGED) ======================================================================================================== The MGED Ontology (MGED) is a domain-specific ontology developed to -standardize the description of microarray experiments. It provides a -structured vocabulary and semantic framework for representing -experimental designs, protocols, biomaterials, array platforms, and -data-related aspects of microarray gene expression studies -[#mged-paper]_ [#mged-bioportal]_. MGED was developed by the microarray -community to support consistent annotation of experiments and to align -with broader microarray data standards such as MIAME and MAGE -[#mged-paper]_ [#mged-standards]_. The ontology has been described as -including a more stable core aligned with MAGE and an extended part that -adds further terms and associations for richer experimental description -[#mged-fairsharing]_ [#mged-scicrunch]_. MGED facilitates -interoperability between microarray data repositories and tools, -supporting the sharing, comparison, and analysis of experimental data -[#mged-paper]_ [#mged-standards]_. By providing a common framework for -experimental metadata, MGED supports reproducibility, data integration, -and meta-analysis in functional genomics and microarray informatics -[#mged-paper]_ [#mged-standards]_. +standardize the description of microarray experiments [#mged-paper]_ +[#mged-bioportal]_. It provides a structured vocabulary and semantic +framework for representing experimental designs, protocols, +biomaterials, array platforms, and data-related aspects of microarray +gene expression studies [#mged-paper]_ [#mged-bioportal]_. MGED was +developed by the microarray community to support consistent annotation +of experiments and to align with broader microarray data standards +[#mged-paper]_. It facilitates interoperability between microarray data +repositories and tools, supporting the sharing, comparison, and +analysis of experimental data [#mged-paper]_ [#mged-bioportal]_. +By providing a common framework for experimental metadata, MGED +supports reproducibility, data integration, and meta-analysis in +functional genomics and microarray informatics [#mged-paper]_. **Example Usage**: Annotate a microarray experiment with MGED terms to describe the experimental design, sample and biomaterial @@ -162,27 +158,14 @@ References .. [#mged-paper] Whetzel, P. L., Parkinson, H., Causton, H. C., Fan, L., Fostel, J., Fragoso, G., Game, L., Heiskanen, M., - Morrison, N., Rocca-Serra, P., Sansone, S.-A., and Stoeckert, C. J. Jr. - 2006. "The MGED Ontology: a resource for semantics-based description - of microarray experiments." + Morrison, N., Rocca-Serra, P., Sansone, S.-A., Sklyar, N., + Stoeckert, C. J., Jr., Tong, W., and Sarkans, U. 2006. + "The MGED Ontology: A Resource for Semantics-Based Description + of Microarray Experiments." *Bioinformatics* 22(7): 866-873. - doi:10.1093/bioinformatics/btl091 - -.. [#mged-standards] Ball, C. A., Brazma, A., Causton, H., - Chervitz, S., Edgar, R., Hingamp, P., Hermjakob, H., Ikeo, K., - Quackenbush, J., Sherlock, G., Spellman, P., Stoekert, C., - Tateno, Y., and Sarkans, U. 2006. "MGED standards: work in progress." - *OMICS* 10(2): 138-144. - Available at: `https://pubmed.ncbi.nlm.nih.gov/16901218/ `_ + Available at: + `https://academic.oup.com/bioinformatics/article/22/7/866/202362 `_ .. [#mged-bioportal] NCBO BioPortal. n.d. "Microarray and Gene Expression Data Ontology." Available at: `https://bioportal.bioontology.org/ontologies/MO `_ - -.. [#mged-fairsharing] FAIRsharing. n.d. - "Microarray and Gene Expression Data Ontology." - Available at: `https://fairsharing.org/1193 `_ - -.. [#mged-scicrunch] SciCrunch. n.d. - "MGED Ontology." - Available at: `https://scicrunch.org/resolver/SCR_004484 `_ diff --git a/docs/source/benchmarking/biology_and_life_sciences/mo.rst b/docs/source/benchmarking/biology_and_life_sciences/mo.rst index 873fc14..203a437 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/mo.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/mo.rst @@ -25,23 +25,23 @@ Microscopy Ontology (MO) The Microscopy Ontology (MO) is a domain ontology developed to provide a structured framework for describing microscopy and microanalysis -experiments, data, and equipment. It extends the PMD Core Ontology -(PMDco) and was developed within the Platform MaterialDigital ecosystem -to support semantic integration and interoperability of microscopy data -[#mo-repo]_ [#mo-paper]_. The ontology covers microscopy-specific +experiments, data, and equipment [#mo-paper]_ [#mo-repo]_. It was +developed within the Platform MaterialDigital ecosystem to support +semantic integration and interoperability of microscopy data +[#mo-paper]_ [#mo-repo]_. The ontology covers microscopy-specific concepts and relationships needed to describe processes, equipment, and parameters in microscopy and microanalysis workflows [#mo-paper]_ -[#mo-overview]_. MO is intended to improve the semantic representation -of microscopy knowledge and support better query results and logical +[#mo-repo]_. MO is intended to improve the semantic representation of +microscopy knowledge and support better query results and logical linking among related terms and data objects [#mo-paper]_ [#mo-repo]_. -By providing a standardized vocabulary grounded in PMDco, the ontology -supports interoperable data description and integration across +By providing a standardized vocabulary for microscopy data, the +ontology supports interoperable data description and integration across materials-science microscopy datasets and related digital research -infrastructures [#mo-overview]_ [#pmdco-paper]_. +infrastructures [#mo-paper]_ [#mo-repo]_. -**Example Usage**: Annotate a microscopy dataset with MO terms to specify -the imaging modality (for example scanning electron microscopy or -transmission electron microscopy), relevant equipment and parameters, +**Example Usage**: Annotate a microscopy dataset with MO terms to +specify the imaging modality, for example scanning electron microscopy +or transmission electron microscopy, relevant equipment and parameters, sample-related descriptors, and analysis-related concepts, enabling semantic search, interoperable data integration, and improved querying across microscopy data sources [#mo-paper]_ [#mo-repo]_. @@ -166,14 +166,3 @@ References .. [#mo-repo] materialdigital. n.d. "Microscopy Ontology (MO)." GitHub repository. Available at: `https://github.com/materialdigital/microscopy-ontology `_ - -.. [#mo-overview] Bayerlein, B., Schilling, M., Bruns, S., and others. - 2024. "Concepts for a Semantically Accessible Materials Data Space: - Overview over Specific Implementations in Materials Science." - *Advanced Engineering Materials*. - Available at: `https://advanced.onlinelibrary.wiley.com/doi/10.1002/adem.202401092 `_ - -.. [#pmdco-paper] Schilling, M., Bayerlein, B., Birkholz, H., and others. - 2024. "PMD Core Ontology: Achieving Semantic Interoperability in - Materials Science." - *Materials & Design* 237: 112563. diff --git a/docs/source/benchmarking/biology_and_life_sciences/npo.rst b/docs/source/benchmarking/biology_and_life_sciences/npo.rst index 41826a1..0cc7302 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/npo.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/npo.rst @@ -24,29 +24,29 @@ NanoParticle Ontology (NPO) ======================================================================================================== The NanoParticle Ontology (NPO) is a domain ontology developed within -the Basic Formal Ontology (BFO) framework to represent knowledge about -the preparation, chemical composition, and characterization of +the Basic Formal Ontology framework to represent knowledge about the +preparation, chemical composition, and characterization of nanomaterials, especially in cancer research and nanomedicine [#npo-paper]_ [#npo-bioportal]_. NPO provides a structured vocabulary for describing nanoparticle composition, preparation methods, physicochemical characteristics, and related entities relevant to -nanotechnology research [#npo-paper]_ [#enanomapper]_. The ontology +nanotechnology research [#npo-paper]_ [#npo-bioportal]_. The ontology supports semantic annotation of nanomaterial data, enabling data integration, interoperability, and ontology-based querying across -biomedical and nanoinformatics resources [#npo-paper]_ [#enanomapper]_. -NPO is publicly available through NCBO BioPortal and has been used as a -reference ontology in nanomaterial data standardization efforts -[#npo-bioportal]_ [#enanomapper]_. By providing a standardized semantic -framework for nanomaterial representation, NPO supports knowledge -sharing, data reuse, and computational analysis in nanotechnology and -nanomedicine research [#npo-paper]_ [#nanoinformatics]_. +biomedical and nanoinformatics resources [#npo-paper]_ +[#npo-bioportal]_. NPO is publicly available through NCBO BioPortal and +serves as a reference resource for standardized nanomaterial +representation [#npo-bioportal]_ [#npo-paper]_. By providing a +standardized semantic framework for nanomaterial representation, NPO +supports knowledge sharing, data reuse, and computational analysis in +nanotechnology and nanomedicine research [#npo-paper]_. **Example Usage**: Annotate a nanomedicine study with NPO terms to -specify nanoparticle composition (for example, a gold nanoparticle), +specify nanoparticle composition, for example a gold nanoparticle, preparation or formulation characteristics, surface functionalization, and measured physicochemical or biological assay properties, enabling cross-study comparison, semantic search, and integration across -nanomaterial datasets [#npo-paper]_ [#enanomapper]_. +nanomaterial datasets [#npo-paper]_ [#npo-bioportal]_. Metrics & Statistics -------------------------- @@ -167,18 +167,3 @@ References .. [#npo-bioportal] NCBO BioPortal. n.d. "NanoParticle Ontology (NPO)." Available at: `https://bioportal.bioontology.org/ontologies/NPO `_ - -.. [#enanomapper] Hastings, J., Jeliazkova, N., Owen, G., Tsiliki, G., - Munteanu, C. R., Steinbeck, C., Willighagen, E., Del Pozo, A., - Džeroski, S., Jeliazkov, V., and others. 2015. - "eNanoMapper: Harnessing Ontologies to Enable Data Integration for - Nanomaterial Risk Assessment." - *Journal of Biomedical Semantics* 6:10. - doi:10.1186/s13326-015-0005-5 - Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC4374589/ `_ - -.. [#nanoinformatics] Panneerselvam, S., and Choi, S. 2014. - "Nanoinformatics: Emerging Databases and Available Tools." - *International Journal of Molecular Sciences* 15(5): 7158-7182. - doi:10.3390/ijms15057158 - Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC4057665/ `_ diff --git a/docs/source/benchmarking/biology_and_life_sciences/pato.rst b/docs/source/benchmarking/biology_and_life_sciences/pato.rst index dce64c4..0fba9b3 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/pato.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/pato.rst @@ -29,20 +29,19 @@ species-neutral way [#pato-obo]_ [#pato-framework]_. It provides a standardized framework for annotating and analyzing phenotypic data by defining qualities such as size, shape, color, morphology, and other characteristics that can be combined with biological entity ontologies -to describe phenotypes [#pato-framework]_ [#pato-anatomy]_. PATO is -widely used in phenotype annotation and in the logical definition of -phenotype terms across species, supporting data integration and -comparative analysis in genetics, developmental biology, and related -life science domains [#pato-obo]_ [#pato-integration]_. By providing a -common language for phenotypic qualities, PATO facilitates cross-species -interoperability, computational reasoning, and semantic analysis of -phenotype data [#pato-anatomy]_ [#oba-paper]_. +to describe phenotypes [#pato-framework]_ [#pato-obo]_. PATO is widely +used in phenotype annotation and in the logical definition of phenotype +terms, supporting data integration and comparative analysis across +biological datasets [#pato-framework]_ [#pato-obo]_. By providing a +common language for phenotypic qualities, PATO facilitates semantic +interoperability, computational reasoning, and cross-study phenotype +analysis [#pato-framework]_ [#pato-obo]_. **Example Usage**: Annotate a genetic or phenotype study with PATO terms to describe qualities such as red coloration, increased size, abnormal shape, or altered morphology in association with a specific biological entity, enabling cross-study comparison, semantic integration, and -computational phenotype analysis [#pato-framework]_ [#pato-integration]_. +computational phenotype analysis [#pato-framework]_ [#pato-obo]_. Metrics & Statistics -------------------------- @@ -163,25 +162,3 @@ References "Using Ontologies to Describe Mouse Phenotypes." *Genome Biology* 6:R8. Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC545487/ `_ - -.. [#pato-integration] Mungall, C. J., Gkoutos, G. V., Smith, C. L., - Haendel, M. A., Lewis, S. E., and Ashburner, M. 2010. - "Integrating Phenotype Ontologies Across Multiple Species." - *Genome Biology* 11:R2. - doi:10.1186/gb-2010-11-1-r2 - Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC2847714/ `_ - -.. [#pato-anatomy] Gkoutos, G. V., Schofield, P. N., and Hoehndorf, R. - 2018. "The Anatomy of Phenotype Ontologies: Principles, Properties - and Applications." - *Briefings in Bioinformatics* 19(5): 1008-1021. - doi:10.1093/bib/bbx035 - Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC6169674/ `_ - -.. [#oba-paper] Stefancsik, R., Mungall, C. J., Robinson, P. N., - Smith, C. L., Haendel, M. A., and Gkoutos, G. V. 2023. - "The Ontology of Biological Attributes (OBA)—Computational Traits for - the Life Sciences." - *Database* 2023: baad038. - doi:10.1093/database/baad038 - Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC9900877/ `_ diff --git a/docs/source/benchmarking/chemistry/afo.rst b/docs/source/benchmarking/chemistry/afo.rst index 1945d0d..d0aef31 100644 --- a/docs/source/benchmarking/chemistry/afo.rst +++ b/docs/source/benchmarking/chemistry/afo.rst @@ -23,10 +23,28 @@ Allotrope Foundation Ontology (AFO) ======================================================================================================== -The Allotrope Foundation Ontology (AFO) is a comprehensive ontology suite designed to standardize the representation of laboratory analytical processes. It provides a semantic model and controlled vocabulary for describing key domains such as Equipment, Material, Process, and Results. The AFO is aligned with the Basic Formal Ontology (BFO) at its upper layer, ensuring compatibility with other ontological frameworks. This ontology suite is particularly valuable for integrating data from diverse laboratory systems, enabling semantic interoperability and facilitating advanced data analysis. By providing explicit definitions and relationships, the AFO supports the automation of laboratory workflows, enhances data reproducibility, and improves the traceability of analytical processes. Researchers and organizations can use the AFO to annotate experimental data, describe laboratory protocols, and ensure compliance with data standards. - -**Example Usage**: -Annotate a laboratory experiment with AFO terms to specify the equipment used (e.g., "mass spectrometer"), the materials analyzed (e.g., "chemical sample"), and the processes performed (e.g., "chromatography"), along with the results obtained. +The Allotrope Foundation Ontology (AFO) is a comprehensive ontology +suite designed to standardize the representation of laboratory +analytical processes [#afo-bioportal]_ [#afo-paper]_. It provides a +semantic model and controlled vocabulary for describing key domains such +as equipment, material, process, and results [#afo-bioportal]_ +[#afo-paper]_. The ontology is aligned with the Basic Formal Ontology +at its upper layer, supporting interoperable representation of +laboratory data across analytical systems [#afo-bioportal]_ +[#afo-paper]_. Within the Allotrope framework, AFO serves as the +semantic layer for consistent description of experimental context, +analytical workflows, and resulting data [#afo-paper]_ +[#afo-bioportal]_. By providing explicit definitions and relationships, +AFO supports laboratory data integration, reproducible analysis, +workflow automation, and traceable representation of analytical +processes [#afo-bioportal]_ [#afo-paper]_. + +**Example Usage**: Annotate a laboratory experiment with AFO terms to +specify the equipment used, such as a mass spectrometer, the material +analyzed, such as a chemical sample, the analytical process performed, +such as chromatography, and the results obtained, enabling semantic +integration, standardized description, and interoperable analysis across +laboratory data systems [#afo-bioportal]_ [#afo-paper]_. Metrics & Statistics -------------------------- @@ -135,3 +153,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#afo-paper] Millecam, T., Jaeger, M., Oberkampf, H., and + Hennebert, P. 2021. "Coming of Age of Allotrope: Proceedings from + the Fall 2020 Allotrope Connect." + *Drug Discovery Today* 26(11): 2675-2681. + Available at: + `https://www.sciencedirect.com/science/article/pii/S1359644621001653 `_ + +.. [#afo-bioportal] NCBO BioPortal. n.d. "Allotrope Foundation Ontologies (AFO)." + Available at: + `https://bioportal.bioontology.org/ontologies/AFO `_ diff --git a/docs/source/benchmarking/chemistry/chebi.rst b/docs/source/benchmarking/chemistry/chebi.rst index 5af6773..61169e5 100644 --- a/docs/source/benchmarking/chemistry/chebi.rst +++ b/docs/source/benchmarking/chemistry/chebi.rst @@ -23,10 +23,30 @@ Chemical Entities of Biological Interest (ChEBI) ======================================================================================================== -Chemical Entities of Biological Interest (ChEBI) is a comprehensive ontology and dictionary of molecular entities, focusing on small chemical compounds. It provides a structured vocabulary for describing constitutionally or isotopically distinct atoms, molecules, ions, radicals, complexes, and other molecular entities. ChEBI includes both naturally occurring substances and synthetic products used in biological systems. The ontology incorporates an ontological classification system, specifying relationships between molecular entities and their parent or child classes. ChEBI is widely used in bioinformatics, cheminformatics, and systems biology to annotate chemical data, enabling interoperability between databases and facilitating advanced queries. By providing a standardized framework for describing chemical entities, ChEBI supports data integration, analysis, and sharing across diverse scientific domains. - -**Example Usage**: -Annotate a dataset of metabolites with ChEBI terms to specify their molecular structures and roles in metabolic pathways, such as "ChEBI:15377 (glucose)" or "ChEBI:15378 (ATP)." +Chemical Entities of Biological Interest (ChEBI) is a comprehensive +database and ontology of molecular entities, with a particular focus on +small chemical compounds [#chebi-site]_ [#chebi-paper]_. It provides a +structured vocabulary for describing constitutionally or isotopically +distinct atoms, molecules, ions, radicals, complexes, and related +chemical entities, including both naturally occurring substances and +synthetic compounds relevant to biological systems [#chebi-site]_ +[#chebi-paper]_. ChEBI incorporates an ontological classification system +that organizes entities into parent-child relationships and supports the +representation of chemical roles, structural classes, and molecular +relationships [#chebi-paper]_ [#chebi-site]_. Widely used in +bioinformatics, cheminformatics, and systems biology, ChEBI enables +consistent chemical annotation, interoperability between databases, and +integration of chemical knowledge across diverse scientific resources +[#chebi-paper]_ [#chebi-site]_. By providing a standardized semantic +framework for chemical entities, ChEBI supports data sharing, advanced +querying, and computational analysis across the life sciences +[#chebi-site]_ [#chebi-paper]_. + +**Example Usage**: Annotate a metabolomics dataset with ChEBI terms to +identify compounds such as glucose or ATP, specify their chemical +classification and biological roles, and support standardized +annotation, pathway analysis, and cross-database integration +[#chebi-site]_ [#chebi-paper]_. Metrics & Statistics -------------------------- @@ -135,3 +155,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#chebi-site] EMBL-EBI. n.d. "ChEBI - Chemical Entities of Biological Interest." + Available at: `https://www.ebi.ac.uk/chebi/ `_ + +.. [#chebi-paper] Degtyarenko, K., de Matos, P., Ennis, M., Hastings, J., + Zbinden, M., McNaught, A., Alcántara, R., Darsow, M., Guedj, M., + and Ashburner, M. 2008. "ChEBI: A Database and Ontology for Chemical + Entities of Biological Interest." + *Nucleic Acids Research* 36(Database issue): D344-D350. + doi:10.1093/nar/gkm791 + Available at: `https://pubmed.ncbi.nlm.nih.gov/17932057/ `_ diff --git a/docs/source/benchmarking/chemistry/cheminf.rst b/docs/source/benchmarking/chemistry/cheminf.rst index 736f842..140e41a 100644 --- a/docs/source/benchmarking/chemistry/cheminf.rst +++ b/docs/source/benchmarking/chemistry/cheminf.rst @@ -25,9 +25,28 @@ Chemical Information Ontology (CHEMINF) ======================================================================================================== -The Chemical Information Ontology (CHEMINF) provides a comprehensive vocabulary for representing and describing information entities and computational descriptors about chemical compounds and substances. It formalizes chemical descriptors, properties, and computational methods used in cheminformatics, enabling standardized representation of molecular attributes derived from chemical informatics tools and algorithms. CHEMINF captures qualitative and quantitative descriptors including molecular weight, logP, HOMO/LUMO energies, and topological indices used for chemical structure analysis and prediction. The ontology is designed to support semantic interoperability in chemistry databases, computational chemistry platforms, and drug discovery systems by providing unambiguous definitions of chemical information concepts. CHEMINF integrates with other chemistry ontologies (ChEBI, MOP, RXNO) to link chemical structures with their calculated properties and computational methods. - -**Example Usage**: Represent a computed molecular descriptor as a CHEMINF information entity linked to a ChEBI chemical structure, specifying the calculation method (e.g., "Wildman-Crippen LogP") and the resulting numeric value. +The Chemical Information Ontology (CHEMINF) provides a comprehensive +vocabulary for representing chemical information entities, including +chemical descriptors, properties, algorithms, and computational methods +used in cheminformatics [#cheminf-paper]_ [#cheminf-obo]_. It supports +standardized representation of molecular attributes and calculated or +reported chemical information, enabling unambiguous description of +qualitative and quantitative descriptors derived from chemical +informatics tools and workflows [#cheminf-paper]_ [#cheminf-obo]_. +CHEMINF is designed to improve semantic interoperability across chemistry +databases, computational chemistry platforms, and drug discovery +systems by providing explicit definitions for chemical information +concepts and their provenance [#cheminf-paper]_ [#cheminf-obo]_. It can +be used alongside related chemistry ontologies to link chemical +structures with their calculated properties, descriptors, and generating +methods [#cheminf-paper]_ [#cheminf-obo]_. + +**Example Usage**: Represent a computed molecular descriptor as a +CHEMINF information entity linked to a chemical structure, specifying +the descriptor type, the calculation method, and the resulting numeric +value, so that descriptor data can be queried, compared, and integrated +across cheminformatics datasets and software environments +[#cheminf-paper]_ [#cheminf-obo]_. Metrics & Statistics -------------------------- @@ -136,3 +155,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#cheminf-paper] Hastings, J., Chepelev, L., Willighagen, E., + Adams, N., Steinbeck, C., and Dumontier, M. 2011. + "The Chemical Information Ontology: Provenance and Disambiguation + for Chemical Data on the Biological Semantic Web." + *PLoS ONE* 6(10): e25513. + doi:10.1371/journal.pone.0025513 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC3184996/ `_ + +.. [#cheminf-obo] OBO Foundry. n.d. "Chemical Information Ontology." + Available at: `https://obofoundry.org/ontology/cheminf.html `_ diff --git a/docs/source/benchmarking/chemistry/chiro.rst b/docs/source/benchmarking/chemistry/chiro.rst index 23919bb..6259702 100644 --- a/docs/source/benchmarking/chemistry/chiro.rst +++ b/docs/source/benchmarking/chemistry/chiro.rst @@ -23,10 +23,24 @@ CHEBI Integrated Role Ontology (CHIRO) ======================================================================================================== -The CHEBI Integrated Role Ontology (CHIRO) is a specialized ontology designed to provide a structured role hierarchy for chemicals. It connects chemicals in the structural hierarchy via a 'has role' relation, linking them to relevant classes in other ontologies. This enables the formalization of relationships between chemical structures (e.g., small molecules, drugs) and their functional roles, such as their biological or chemical activities. CHIRO facilitates the integration of chemical data with biological and biomedical ontologies, supporting applications in drug discovery, chemical informatics, and systems biology. By providing a standardized framework for describing chemical roles, CHIRO enhances data interoperability and enables advanced semantic queries across chemical and biological datasets. The ontology is particularly useful for linking chemical entities to their roles in biological processes, such as enzyme inhibitors, signaling molecules, or structural components. - -**Example Usage**: -Annotate a dataset of small molecules with CHIRO terms to specify their roles, such as "enzyme inhibitor" or "neurotransmitter," and link these roles to relevant biological processes or pathways. +The CHEBI Integrated Role Ontology (CHIRO) is a specialized ontology +designed to provide a structured role hierarchy for chemicals +[#chiro-obo]_ [#chiro-paper]_. It extends the role branch associated +with ChEBI by connecting chemicals in the structural hierarchy through +a `has role` relation and by linking chemical roles to relevant classes +in other ontologies [#chiro-obo]_ [#chiro-paper]_. This enables formal +representation of relationships between chemical structures, such as +small molecules and drugs, and the biological or chemical roles they +play [#chiro-obo]_ [#chiro-paper]_. By providing a standardized +framework for describing chemical roles, CHIRO supports semantic +interoperability, ontology-based integration, and advanced querying +across chemical and biomedical datasets [#chiro-paper]_ [#chiro-obo]_. + +**Example Usage**: Annotate a dataset of small molecules with CHIRO +terms to specify roles such as enzyme inhibitor or neurotransmitter, +and link those roles to related biological processes or target classes, +enabling semantic search, cross-dataset integration, and role-based +analysis of chemical entities [#chiro-obo]_ [#chiro-paper]_. Metrics & Statistics -------------------------- @@ -135,3 +149,16 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#chiro-obo] OBO Foundry. n.d. "CHEBI Integrated Role Ontology." + Available at: `https://obofoundry.org/ontology/chiro.html `_ + +.. [#chiro-paper] Hoyt, C. T., Mungall, C., Vasilevsky, N., + Domingo-Fernández, D., Healy, M., and Colluru, V. 2020. + "Extension of Roles in the ChEBI Ontology." + ChemRxiv. + doi:10.26434/chemrxiv.12591221 + Available at: `https://chemrxiv.org/doi/10.26434/chemrxiv.12591221 `_ diff --git a/docs/source/benchmarking/chemistry/chmo.rst b/docs/source/benchmarking/chemistry/chmo.rst index 11141b0..27d9e2e 100644 --- a/docs/source/benchmarking/chemistry/chmo.rst +++ b/docs/source/benchmarking/chemistry/chmo.rst @@ -23,10 +23,27 @@ Chemical Methods Ontology (ChMO) ======================================================================================================== -The Chemical Methods Ontology (ChMO) is a comprehensive ontology that provides a structured vocabulary for describing chemical methods, experimental techniques, and analytical procedures used in chemistry and related sciences. ChMO contains over 3000 classes covering methods for data collection (e.g., mass spectrometry, electron microscopy), sample preparation and separation (e.g., ionisation, chromatography, electrophoresis), and material synthesis (e.g., epitaxy, vapor deposition). The ontology also describes the instruments and equipment used in these experiments, such as mass spectrometers and chromatography columns, as well as the outputs and results generated. ChMO enables semantic annotation of experimental workflows, facilitating data integration, reproducibility, and advanced analysis across chemical research and laboratory information systems. By providing a standardized framework, ChMO supports interoperability between chemical databases, electronic lab notebooks, and computational tools. The ontology is actively maintained and extended to incorporate new methods and technologies as the field evolves. - -**Example Usage**: -Annotate a chemical experiment with ChMO terms to specify the analytical method (e.g., "liquid chromatography-mass spectrometry"), sample preparation steps, instrument configuration, and data outputs, enabling semantic search and integration with other chemical research datasets. +The Chemical Methods Ontology (ChMO) is a structured ontology that +provides a controlled vocabulary for describing chemical methods, +experimental techniques, and analytical procedures used in chemistry and +related sciences [#chmo-repo]_ [#chmo-bioportal]_. ChMO contains terms +covering methods for data collection, sample preparation and separation, +and material synthesis, together with associated instruments and +experimental outputs [#chmo-repo]_ [#chmo-bioportal]_. The ontology is +intended to support semantic annotation of chemical workflows and to +improve interoperability across chemical databases, laboratory +information systems, and computational tools [#chmo-repo]_ +[#chmo-bioportal]_. By providing a standardized framework for chemical +methods and related experimental information, ChMO supports data +integration, reproducibility, and structured querying across chemical +research datasets [#chmo-repo]_ [#chmo-bioportal]_. + +**Example Usage**: Annotate a chemical experiment with ChMO terms to +specify the analytical method, such as liquid +chromatography-mass spectrometry, the sample preparation steps, the +instrument configuration, and the data outputs, enabling semantic +search and integration with other chemical research datasets +[#chmo-repo]_ [#chmo-bioportal]_. Metrics & Statistics -------------------------- @@ -135,3 +152,15 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#chmo-repo] Royal Society of Chemistry. n.d. + "Chemical Methods Ontology (ChMO)." + GitHub repository. + Available at: + `https://github.com/rsc-ontology/rsc-cmo `_ + +.. [#chmo-bioportal] NCBO BioPortal. n.d. "Chemical Methods Ontology." + Available at: `https://bioportal.bioontology.org/ontologies/CHMO `_ diff --git a/docs/source/benchmarking/chemistry/fix.rst b/docs/source/benchmarking/chemistry/fix.rst index 2773ec5..8c7b5e6 100644 --- a/docs/source/benchmarking/chemistry/fix.rst +++ b/docs/source/benchmarking/chemistry/fix.rst @@ -25,9 +25,27 @@ FIX Ontology (FIX) ======================================================================================================== -The FIX (Physico-Chemical Methods and Properties) Ontology provides a systematic vocabulary for describing and classifying physico-chemical methods and their associated properties. It represents analytical and experimental techniques used in chemistry and materials science, including measurement methods, analytical procedures, and the physical and chemical properties they determine. The ontology captures relationships between methods and properties, enabling precise description of experimental workflows and results in laboratory and industrial settings. It supports semantic interoperability in chemistry databases, laboratory information systems (LIMS), and scientific data repositories. - -**Example Usage**: Represent a mass spectrometry measurement as an instance of FIX:AnalyticalMethod linked to FIX:ChemicalProperty instances such as molecular weight or compound identification. +The FIX (Physico-Chemical Methods and Properties) Ontology provides a +systematic vocabulary for describing and classifying physico-chemical +methods and their associated properties [#fix-context]_. It represents +analytical and experimental techniques used in chemistry and materials +science, including measurement methods, analytical procedures, and the +physical and chemical properties they determine [#fix-context]_. In the +broader chemistry ontology landscape, FIX is related to ontologies such +as ChEBI, which provides a comprehensive classification of chemical +entities and their roles [#fix-context]_ [#chebi-paper]_. The ontology +captures relationships between methods and properties, enabling precise +description of experimental workflows and results in laboratory and +industrial settings [#fix-context]_. It supports semantic +interoperability in chemistry databases, laboratory information systems, +and scientific data repositories [#fix-context]_. + +**Example Usage**: Represent a mass spectrometry measurement as an +instance of a FIX analytical method linked to chemical property terms +such as molecular weight or compound identification, and connect the +measured substance to a ChEBI chemical entity to support semantic +integration and structured querying of experimental results +[#fix-context]_ [#chebi-paper]_. Metrics & Statistics -------------------------- @@ -136,3 +154,19 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#fix-context] NFDI4Chem Knowledge Base. n.d. "Ontology." + Available at: + `https://knowledgebase.nfdi4chem.de/knowledge_base/docs/ontology/ `_ + +.. [#chebi-paper] Degtyarenko, K., de Matos, P., Ennis, M., Hastings, J., + Zbinden, M., McNaught, A., Alcántara, R., Darsow, M., Guedj, M., + and Ashburner, M. 2008. "ChEBI: A Database and Ontology for Chemical + Entities of Biological Interest." + *Nucleic Acids Research* 36(Database issue): D344-D350. + doi:10.1093/nar/gkm791 + Available at: + `https://pubmed.ncbi.nlm.nih.gov/17932057/ `_ diff --git a/docs/source/benchmarking/chemistry/massspectrometry.rst b/docs/source/benchmarking/chemistry/massspectrometry.rst index cdbb453..ea26554 100644 --- a/docs/source/benchmarking/chemistry/massspectrometry.rst +++ b/docs/source/benchmarking/chemistry/massspectrometry.rst @@ -25,11 +25,26 @@ Mass Spectrometry Ontology (MassSpectrometry) A structured controlled vocabulary for the annotation of experiments concerned with proteomics mass spectrometry. -The Mass Spectrometry Ontology (MassSpectrometry) is a structured controlled vocabulary designed to annotate experiments in the field of proteomics mass spectrometry. It provides a standardized framework for describing experimental setups, instrumentation, data acquisition methods, and analysis workflows. The ontology captures essential concepts such as ionization techniques, mass analyzers, fragmentation methods, and data processing algorithms. By offering a common language for mass spectrometry, it facilitates data sharing, integration, and reproducibility across proteomics studies. The ontology is widely used in bioinformatics pipelines, proteomics databases, and experimental repositories to ensure semantic consistency and interoperability. Researchers can leverage the Mass Spectrometry Ontology to annotate datasets, describe experimental protocols, and enable advanced queries for comparative analyses. - -**Example Usage**: -Annotate a proteomics experiment with MassSpectrometry terms to specify the ionization method (e.g., "electrospray ionization"), mass analyzer type (e.g., "time-of-flight"), and data processing software used for peak detection and quantification. - +The Mass Spectrometry Ontology (MassSpectrometry) is a structured +controlled vocabulary developed by the HUPO Proteomics Standards +Initiative to support the annotation of proteomics mass spectrometry +experiments [#ms-obo]_ [#ms-paper]_. It provides a standardized +framework for describing experimental setups, instrumentation, data +acquisition methods, and analysis workflows in mass spectrometry +[#ms-paper]_ [#ms-obo]_. The ontology captures key concepts such as +ionization techniques, mass analyzers, fragmentation methods, software, +and data-processing terms used in proteomics experiments [#ms-paper]_ +[#ms-obo]_. By providing a common vocabulary for mass spectrometry, it +supports semantic consistency, data sharing, interoperability, and +reproducibility across proteomics databases, repositories, and analysis +pipelines [#ms-obo]_ [#ms-paper]_. + +**Example Usage**: Annotate a proteomics experiment with +MassSpectrometry terms to specify the ionization method, such as +electrospray ionization, the mass analyzer type, such as time-of-flight, +the fragmentation approach, and the software used for peak detection or +quantification, enabling semantic interoperability and cross-study +comparison [#ms-paper]_ [#ms-obo]_. Metrics & Statistics -------------------------- @@ -137,3 +152,16 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#ms-obo] OBO Foundry. n.d. "Mass Spectrometry Ontology." + Available at: `https://obofoundry.org/ontology/ms.html `_ + +.. [#ms-paper] Mayer, G., Jones, A. R., Binz, P.-A., Deutsch, E. W., + Orchard, S., Montecchi-Palazzi, L., Vizcaíno, J. A., Hermjakob, H., + and others. 2013. "The HUPO Proteomics Standards Initiative-Mass + Spectrometry Controlled Vocabulary." + *Database* 2013: bat009. + doi:10.1093/database/bat009 diff --git a/docs/source/benchmarking/chemistry/mop.rst b/docs/source/benchmarking/chemistry/mop.rst index 90accb6..45e6f8b 100644 --- a/docs/source/benchmarking/chemistry/mop.rst +++ b/docs/source/benchmarking/chemistry/mop.rst @@ -24,10 +24,25 @@ Molecular Process Ontology (MOP) ======================================================================================================== - -The Molecular Process Ontology (MOP) is a systematic vocabulary for describing and classifying molecular-level chemical processes and transformations that occur in organic chemistry. It provides formal definitions of common molecular processes such as cyclization, methylation, demethylation, oxidation, reduction, and other fundamental reaction steps underlying named reactions in chemistry. MOP serves as the foundational semantic layer for the Reaction Ontology (RXNO), enabling precise description of chemical reaction mechanisms and their constituent molecular processes. The ontology facilitates integration of chemical databases, computational chemistry platforms, and reaction informatics systems by providing standardized semantic representations of molecular transformations. MOP enables advanced searching and classification of reactions based on their underlying molecular mechanisms. - -**Example Usage**: Represent a cyclization reaction step in RXNO by linking to MOP terms for the specific cyclization type (e.g., "6-membered ring closure"), enabling automated discovery of similar reactions across chemical databases. +The Molecular Process Ontology (MOP) is a systematic ontology for +describing and classifying molecular-level chemical processes and +transformations [#mop-bioportal]_. It provides a structured vocabulary +for representing processes such as cyclization, methylation, +demethylation, oxidation, reduction, and other fundamental +transformations relevant to chemical reaction description +[#mop-bioportal]_. MOP supports standardized semantic representation of +molecular processes that underlie reaction mechanisms and can be used to +organize and classify reaction information in chemistry databases and +reaction informatics systems [#mop-bioportal]_. By providing explicit +terms for molecular transformations, MOP supports semantic +interoperability, structured querying, and mechanism-oriented analysis +across chemical data resources [#mop-bioportal]_. + +**Example Usage**: Represent a cyclization reaction step by linking it +to an appropriate MOP term for the corresponding molecular process, +such as a ring-closure transformation, enabling structured +classification of reactions and discovery of related transformations +across chemical databases [#mop-bioportal]_. Metrics & Statistics -------------------------- @@ -136,3 +151,9 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#mop-bioportal] NCBO BioPortal. n.d. "Molecular Process Ontology (MOP)." + Available at: `https://bioportal.bioontology.org/ontologies/MOP `_ diff --git a/docs/source/benchmarking/chemistry/nmrcv.rst b/docs/source/benchmarking/chemistry/nmrcv.rst index 870a2ce..5176eac 100644 --- a/docs/source/benchmarking/chemistry/nmrcv.rst +++ b/docs/source/benchmarking/chemistry/nmrcv.rst @@ -25,7 +25,27 @@ Nuclear Magnetic Resonance Controlled Vocabulary (NMRCV) ======================================================================================================== -This artefact is an MSI-approved controlled vocabulary primarily developed under COSMOS EU and PhenoMeNal EU governance. The nmrCV is supporting the nmrML XML format with standardized terms. nmrML is a vendor-agnostic open access NMR raw data standard. Its primary role is analogous to the mzCV for the PSI-approved mzML XML format. It uses BFO2.0 as its top level. This CV was derived from two predecessors (The NMR CV from the David Wishart Group, developed by Joseph Cruz) and the MSI nmr CV developed by Daniel Schober at the EBI. This simple taxonomy of terms (no DL semantics used) serves the nuclear magnetic resonance markup language (nmrML) with meaningful descriptors to amend the nmrML XML file with CV terms. Metabolomics scientists are encouraged to use this CV to annotate their raw and experimental context data, i.e. within nmrML. The approach to have an exchange syntax mixed of an XSD and CV stems from the PSI mzML effort. The reason to branch out from an XSD into a CV is that in areas where the terminology is likely to change faster than the nmrML XSD could be updated and aligned, an externally and decentralized maintained CV can compensate for such dynamics in a more flexible way. A second reason for this setup is that semantic validity of CV terms used in an nmrML XML instance (allowed CV terms, position/relation to each other, cardinality) can be validated by rule-based proprietary validators: by means of cardinality specifications and XPath expressions defined in an XML mapping file (an instance of the CvMappingRules.xsd), one can define what ontology terms are allowed in a specific location of the data model. +The Nuclear Magnetic Resonance Controlled Vocabulary (NMRCV) is an +MSI-approved controlled vocabulary developed to support standardized +annotation of nuclear magnetic resonance data and experiments +[#nmr-bioportal]_ [#nmrml-paper]_. It supports the nmrML XML format, a +vendor-agnostic open standard for the description, storage, and +exchange of raw NMR data, by providing standardized terms for +instrumentation, acquisition parameters, sample context, and other +experimental metadata [#nmrml-paper]_ [#nmr-bioportal]_. The vocabulary +serves as a flexible semantic layer alongside the nmrML schema, +allowing terminology to evolve independently while still supporting +semantic validation and interoperable data exchange [#nmrml-paper]_. +By providing a shared terminology for NMR experiments, NMRCV improves +semantic consistency, reproducibility, validation, and cross-study +integration across metabolomics and analytical chemistry workflows +[#nmr-bioportal]_ [#nmrml-paper]_. + +**Example Usage**: Annotate an nmrML file with NMRCV terms to specify +the instrument type, pulse sequence, sample conditions, acquisition +parameters, and processing metadata, enabling standardized exchange, +validation, and cross-study comparison of NMR datasets +[#nmrml-paper]_ [#nmr-bioportal]_. Metrics & Statistics -------------------------- @@ -134,3 +154,20 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#nmr-bioportal] NCBO BioPortal. n.d. "NMR-Controlled Vocabulary." + Available at: + `https://bioportal.bioontology.org/ontologies/NMR `_ + +.. [#nmrml-paper] Schober, D., Jacob, D., Wilson, M., Cruz, J. A., + Marcu, A., Grant, J. R., Moing, A., Deborde, C., de Figueiredo, L. F., + Haug, K., Rocca-Serra, P., Easton, J., Ebbels, T. M. D., Hao, J., + Ludwig, C., Nasi, N., Narayana, V. K., Sansone, S.-A., Viant, M. R., + and Wishart, D. S. 2018. + "nmrML: A Community Supported Open Data Standard for the Description, + Storage, and Exchange of NMR Data." + *Analytical Chemistry* 90(1): 649-656. + doi:10.1021/acs.analchem.7b02795 diff --git a/docs/source/benchmarking/chemistry/ontokin.rst b/docs/source/benchmarking/chemistry/ontokin.rst index c451c09..a97d0d3 100644 --- a/docs/source/benchmarking/chemistry/ontokin.rst +++ b/docs/source/benchmarking/chemistry/ontokin.rst @@ -25,10 +25,26 @@ Chemical Kinetics Ontology (OntoKin) ======================================================================================================== -OntoKin is a comprehensive ontology developed for formal and standardized representation of chemical kinetics data, reaction mechanisms, and kinetic rate parameters used in chemistry and combustion science. It provides structured definitions of reaction mechanisms, including species involved, reaction pathways, elementary steps, and kinetic rate coefficients essential for modeling chemical processes. OntoKin captures important chemical kinetics concepts such as reaction types (forward, reverse, three-body), activation energies, temperature dependencies, and pressure effects on reaction rates. The ontology facilitates data integration and knowledge sharing in computational chemistry, combustion research, and chemical process modeling by providing unambiguous semantic representations. OntoKin supports automated reasoning and knowledge discovery in chemical databases, enabling researchers to search, compare, and reuse kinetic mechanisms across different applications. - -**Example Usage**: Represent a chemical reaction mechanism with OntoKin terms for species (CH4, O2, H2O), elementary reaction steps with activation energies, and temperature-dependent rate coefficients using Arrhenius or modified Arrhenius equations. - +The Chemical Kinetics Ontology (OntoKin) is an ontology developed for +the formal and standardized representation of chemical kinetic reaction +mechanisms [#ontokin-paper]_ [#ontokin-context]_. It provides structured +definitions for reaction mechanisms, including chemical species, +reaction pathways, elementary reactions, phases, and kinetic rate +coefficients needed to model chemical processes [#ontokin-paper]_ +[#ontokin-context]_. OntoKin captures important kinetics concepts such +as reaction types, activation energies, temperature dependence, and +other parameters used in combustion chemistry and related modeling +domains [#ontokin-paper]_. As a semantic framework for reaction +mechanism knowledge, it supports integration, querying, comparison, and +reuse of kinetic mechanisms across chemistry databases and computational +chemistry systems [#ontokin-paper]_ [#ontokin-context]_. + +**Example Usage**: Represent a combustion reaction mechanism with +OntoKin terms for chemical species such as CH4, O2, and H2O, together +with elementary reaction steps, activation energies, and +temperature-dependent rate coefficients, enabling semantic comparison +and reuse of kinetic models across chemical and combustion datasets +[#ontokin-paper]_ [#ontokin-context]_. Metrics & Statistics -------------------------- @@ -136,3 +152,15 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#ontokin-paper] Farazi, F., Akroyd, J., Mosbach, S., and Kraft, M. 2020. + "An Ontology for Chemical Kinetic Reaction Mechanisms." + *Journal of Chemical Information and Modeling* 60(1): 108-120. + doi:10.1021/acs.jcim.9b00960 + +.. [#ontokin-context] NFDI4Chem Knowledge Base. n.d. "Ontology." + Available at: + `https://nfdi4chem.chemie.uni-mainz.de/knowledge_base/docs/topics/ontology/ `_ diff --git a/docs/source/benchmarking/chemistry/proco.rst b/docs/source/benchmarking/chemistry/proco.rst index dda4abb..4d199d9 100644 --- a/docs/source/benchmarking/chemistry/proco.rst +++ b/docs/source/benchmarking/chemistry/proco.rst @@ -25,9 +25,28 @@ PROcess Chemistry Ontology (PROCO) ======================================================================================================== -PROCO (PROcess Chemistry Ontology) is a formal ontology that standardizes representation of entities, relationships, and processes in process chemistry and chemical manufacturing. It provides comprehensive vocabulary for describing chemical reactions, reaction conditions, reactants, products, catalysts, and process equipment used in industrial and laboratory chemical processes. PROCO captures essential process chemistry concepts including reaction parameters (temperature, pressure, time, stirring), process steps (mixing, heating, separation), material flows, and safety-critical information for chemical synthesis. The ontology enables semantic interoperability across chemical engineering databases, laboratory information management systems (LIMS), and process simulation tools by providing explicit, unambiguous definitions. PROCO supports knowledge integration and reasoning in process chemistry research, enabling automated workflow design, process optimization, and risk assessment. - -**Example Usage**: Represent a multi-step synthesis process with PROCO terms for each reaction step including reactants, reaction conditions (temperature and pressure ranges), catalysts, solvents, workup procedures, and desired products with yield information. +PROCO (PROcess Chemistry Ontology) is a formal ontology developed to +standardize the representation of entities, relationships, and +processes in process chemistry and chemical manufacturing +[#proco-paper]_ [#proco-bioportal]_. It provides a structured +vocabulary for describing chemical reactions, reactants, products, +catalysts, reaction conditions, and process steps used in laboratory +and industrial chemistry workflows [#proco-paper]_ [#proco-bioportal]_. +The ontology captures important process chemistry concepts such as +temperature, pressure, time, stirring, solvents, workup operations, and +other process-related information needed for detailed representation of +chemical synthesis workflows [#proco-paper]_ [#proco-bioportal]_. +By providing explicit and machine-interpretable definitions, PROCO +supports semantic interoperability, data integration, and reasoning +across process chemistry databases, laboratory information systems, and +process development workflows [#proco-paper]_ [#proco-bioportal]_. + +**Example Usage**: Represent a multi-step synthesis process with PROCO +terms for each reaction step, including reactants, catalysts, solvents, +reaction conditions such as temperature and pressure, workup procedures, +and desired products with yield information, enabling semantic +integration, process comparison, and automated reasoning across process +chemistry datasets [#proco-paper]_ [#proco-bioportal]_. Metrics & Statistics -------------------------- @@ -136,3 +155,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#proco-bioportal] NCBO BioPortal. n.d. "Process Chemistry Ontology (PROCO)." + Available at: + `https://bioportal.bioontology.org/ontologies/PROCO `_ + +.. [#proco-paper] Schafer, W. A., Dance, Z. E., Dun, A. L., and He, Y. 2022. + "Collaborative Development of a Process Chemistry Ontology." + In *Proceedings of the International Conference on Biomedical Ontology (ICBO 2022)*. + CEUR Workshop Proceedings. + Available at: + `https://ceur-ws.org/Vol-3805/ICBO-2022_paper_4112.pdf `_ diff --git a/docs/source/benchmarking/chemistry/psimod.rst b/docs/source/benchmarking/chemistry/psimod.rst index 6c9440c..3bebd84 100644 --- a/docs/source/benchmarking/chemistry/psimod.rst +++ b/docs/source/benchmarking/chemistry/psimod.rst @@ -23,10 +23,28 @@ Protein Modifications Ontology (PSIMOD) ======================================================================================================== -The Protein Modifications Ontology (PSIMOD) is a comprehensive ontology developed by the Proteomics Standards Initiative (PSI) to describe chemical modifications of proteins. It organizes protein modifications into a directed acyclic graph (DAG) structure, enabling hierarchical classification based on molecular structure or the modified amino acid residue. PSIMOD captures a wide range of protein modifications, including phosphorylation, acetylation, ubiquitination, and glycosylation, providing detailed descriptions of their chemical nature and biological significance. The ontology supports semantic annotation of proteomics datasets, facilitating data integration, analysis, and sharing across proteomics studies. By providing a standardized vocabulary, PSIMOD enhances the reproducibility and interoperability of proteomics research, enabling advanced queries and comparative analyses. - -**Example Usage**: -Annotate a proteomics dataset with PSIMOD terms to specify protein modifications, such as "PSI-MOD:00046 (phosphorylation)" or "PSI-MOD:00048 (acetylation)," and link these modifications to their respective biological pathways. +The Protein Modifications Ontology (PSIMOD) is a comprehensive ontology +developed by the Proteomics Standards Initiative (PSI) to describe +chemical modifications of proteins [#psimod-paper]_ [#psimod-obo]_. +It organizes protein modifications into a directed acyclic graph (DAG) +structure, enabling hierarchical classification based on molecular +structure or the modified amino acid residue [#psimod-paper]_ +[#psimod-obo]_. PSIMOD captures a wide range of protein modifications, +including phosphorylation, acetylation, ubiquitination, and +glycosylation, providing detailed descriptions of their chemical nature +and biological significance [#psimod-paper]_ [#psimod-obo]_. The +ontology supports semantic annotation of proteomics datasets, +facilitating data integration, analysis, and sharing across proteomics +studies [#psimod-paper]_ [#psimod-obo]_. By providing a standardized +vocabulary, PSIMOD enhances the reproducibility and interoperability of +proteomics research, enabling advanced queries and comparative analyses +[#psimod-paper]_ [#psimod-obo]_. + +**Example Usage**: Annotate a proteomics dataset with PSIMOD terms to +specify protein modifications, such as phosphorylation or acetylation, +and link these modifications to their relevant biological context, +enabling standardized annotation, semantic querying, and cross-study +comparison [#psimod-paper]_ [#psimod-obo]_. Metrics & Statistics -------------------------- @@ -135,3 +153,15 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- +.. [#psimod-obo] OBO Foundry. n.d. "Protein Modification Ontology (MOD)." + Available at: `https://obofoundry.org/ontology/mod.html `_ + +.. [#psimod-paper] Montecchi-Palazzi, L., Beavis, R., Binz, P.-A., + Chalkley, R. J., Cottrell, J., Creasy, D., Shofstahl, J., + Seymour, S. L., Garavelli, J. S., and Orchard, S. 2008. + "The PSI-MOD Community Standard for Representation of Protein + Modification Data." + *Nature Biotechnology* 26(8): 864-866. diff --git a/docs/source/benchmarking/chemistry/rex.rst b/docs/source/benchmarking/chemistry/rex.rst index c99e0fc..c2c442e 100644 --- a/docs/source/benchmarking/chemistry/rex.rst +++ b/docs/source/benchmarking/chemistry/rex.rst @@ -25,9 +25,26 @@ Physico-chemical process ontology (REX) ======================================================================================================== -REX is a comprehensive ontology for formal representation of physico-chemical processes, including both microscopic molecular transformations and macroscopic chemical phenomena occurring over time. It provides structured vocabulary for describing processes at different scales: molecular-level processes (involving chemical bonds, molecular rearrangement, electron transfer) and macroscopic processes (phase changes, dissolution, crystallization). REX distinguishes between different process types and captures temporal and causal relationships between processes, enabling precise semantic representation of chemical transformations. The ontology integrates with biological process vocabularies (e.g., Gene Ontology's biological process namespace) to bridge molecular biochemistry and cellular processes. REX facilitates knowledge integration in chemistry databases, computational chemistry platforms, and systems biology models by providing standardized process definitions. - -**Example Usage**: Represent a multi-step chemical transformation process using REX terms to describe molecular-level processes (e.g., nucleophilic substitution, oxidation) linked together in sequence, with temporal ordering and causal dependencies between elementary steps. +REX is an ontology for the formal representation of physico-chemical +processes, including both microscopic molecular transformations and +macroscopic chemical phenomena [#rex-obo]_ [#rex-bioportal]_. It +provides a structured vocabulary for describing processes at different +scales, such as molecular-level processes involving chemical bond +changes, molecular rearrangements, and electron transfer, as well as +macroscopic processes such as phase changes, dissolution, and +crystallization [#rex-obo]_ [#rex-bioportal]_. REX distinguishes +between different process types and supports formal representation of +chemical transformations in a standardized and machine-readable way +[#rex-obo]_ [#rex-bioportal]_. By providing explicit process +definitions, the ontology supports knowledge integration across +chemistry databases, computational chemistry platforms, and related +scientific data systems [#rex-obo]_ [#rex-bioportal]_. + +**Example Usage**: Represent a multi-step chemical transformation using +REX terms to describe molecular-level processes such as oxidation or +substitution, together with their sequence and relationships, enabling +structured representation and semantic querying of complex +physico-chemical processes [#rex-obo]_ [#rex-bioportal]_. Metrics & Statistics -------------------------- @@ -136,3 +153,12 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#rex-obo] OBO Foundry. n.d. "Physico-chemical process." + Available at: `https://obofoundry.org/ontology/rex.html `_ + +.. [#rex-bioportal] NCBO BioPortal. n.d. "Physico-Chemical Process (REX)." + Available at: `https://bioportal.bioontology.org/ontologies/REX `_ diff --git a/docs/source/benchmarking/chemistry/rxno.rst b/docs/source/benchmarking/chemistry/rxno.rst index 2d61442..fac0993 100644 --- a/docs/source/benchmarking/chemistry/rxno.rst +++ b/docs/source/benchmarking/chemistry/rxno.rst @@ -23,10 +23,26 @@ Reaction Ontology (RXNO) ======================================================================================================== -The Reaction Ontology (RXNO) is a specialized ontology that provides a comprehensive vocabulary for representing organic chemical reactions. It includes over 500 classes that describe named reactions, such as the Diels–Alder cyclization, and their associated mechanisms. RXNO enables the semantic annotation of chemical reaction data, facilitating data sharing, integration, and advanced querying in cheminformatics and organic chemistry research. By standardizing the representation of chemical reactions, RXNO supports the development of reaction databases, computational chemistry tools, and automated synthesis planning systems. The ontology also captures relationships between reactions, reactants, products, and catalysts, enabling detailed modeling of reaction networks. - -**Example Usage**: -Annotate a reaction database with RXNO terms to specify reaction types, such as "RXNO:000001 (Diels–Alder reaction)," and link these reactions to their reactants, products, and catalysts. +The Reaction Ontology (RXNO) is a specialized ontology that provides a +comprehensive vocabulary for representing organic chemical reactions +[#rxno-obo]_ [#rxno-repo]_. It includes classes that describe named +reactions, such as the Diels–Alder cyclization, and their associated +roles in organic synthesis [#rxno-obo]_ [#rxno-repo]_. RXNO enables the +semantic annotation of chemical reaction data, facilitating data +sharing, integration, and advanced querying in cheminformatics and +organic chemistry research [#rxno-obo]_ [#rxno-repo]_. By standardizing +the representation of chemical reactions, RXNO supports the development +of reaction databases, computational chemistry tools, and automated +synthesis planning systems [#rxno-obo]_ [#rxno-repo]_. The ontology +also captures relationships between reactions and related molecular +processes, enabling more detailed modeling of reaction knowledge +[#rxno-obo]_ [#rxno-repo]_. + +**Example Usage**: Annotate a reaction database with RXNO terms to +specify reaction types, such as a Diels–Alder reaction, and link these +reactions to related reaction information to support semantic search, +integration, and comparison across chemical reaction datasets +[#rxno-obo]_ [#rxno-repo]_. Metrics & Statistics -------------------------- @@ -135,3 +151,12 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#rxno-obo] OBO Foundry. n.d. "Name Reaction Ontology." + Available at: `https://obofoundry.org/ontology/rxno.html `_ + +.. [#rxno-repo] Royal Society of Chemistry. n.d. "RXNO: reaction ontologies." + Available at: `https://github.com/rsc-ontology/rxno `_ diff --git a/docs/source/benchmarking/chemistry/vibso.rst b/docs/source/benchmarking/chemistry/vibso.rst index 0ac67f7..9b64f66 100644 --- a/docs/source/benchmarking/chemistry/vibso.rst +++ b/docs/source/benchmarking/chemistry/vibso.rst @@ -25,9 +25,30 @@ Vibrational Spectroscopy Ontology (VIBSO) ======================================================================================================== -The Vibrational Spectroscopy Ontology (VIBSO) is a domain-specific vocabulary for comprehensive and standardized semantic description of vibrational spectroscopy experiments and their resulting research data. It provides formal definitions of technical terms and concepts used in vibrational spectroscopy including spectroscopy types (infrared, Raman, NIR), sample preparation methods, measurement parameters, instrumentation, and data analysis procedures. VIBSO enables researchers to annotate spectroscopy datasets with machine-readable semantic metadata, making data FAIR (Findable, Accessible, Interoperable, Reusable) and supporting automated data discovery and integration. The ontology captures the complete experimental context including measurement conditions, instrument specifications, data processing steps, and quality metrics critical for reproducibility and data reuse. VIBSO facilitates semantic interoperability in spectroscopy databases, analytical chemistry platforms, and materials science research by providing unambiguous definitions of spectroscopic concepts. - -**Example Usage**: Annotate a Raman spectroscopy experiment with VIBSO terms including spectroscopy type (Raman), sample material, laser wavelength, temperature conditions, and data processing methods to enable automated discovery of similar spectroscopic studies. +The Vibrational Spectroscopy Ontology (VIBSO) is a domain-specific +vocabulary for comprehensive and standardized semantic description of +vibrational spectroscopy experiments and their resulting research data +[#vibso-doc]_ [#vibso-repo]_. It provides formal definitions of +technical terms and concepts used in vibrational spectroscopy, +including spectroscopy types such as infrared, Raman, and NIR, sample +preparation methods, measurement parameters, instrumentation, and data +analysis procedures [#vibso-doc]_ [#vibso-repo]_. VIBSO enables +researchers to annotate spectroscopy datasets with machine-readable +semantic metadata, supporting FAIR data practices and automated data +discovery and integration [#vibso-doc]_ [#vibso-repo]_. The ontology +captures the complete experimental context, including measurement +conditions, instrument specifications, data processing steps, and +quality-related metadata important for reproducibility and data reuse +[#vibso-doc]_ [#vibso-repo]_. By providing unambiguous definitions of +spectroscopic concepts, VIBSO facilitates semantic interoperability in +spectroscopy databases, analytical chemistry platforms, and materials +science research [#vibso-doc]_ [#vibso-repo]_. + +**Example Usage**: Annotate a Raman spectroscopy experiment with VIBSO +terms including spectroscopy type, sample material, laser wavelength, +temperature conditions, and data processing methods to enable automated +discovery of similar spectroscopic studies [#vibso-doc]_ +[#vibso-repo]_. Metrics & Statistics -------------------------- @@ -136,3 +157,13 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#vibso-doc] VIBSO Workgroup. n.d. "Vibrational Spectroscopy Ontology Documentation." + Available at: `https://nfdi4chem.github.io/VibrationalSpectroscopyOntology/ `_ + +.. [#vibso-repo] NFDI4Chem. n.d. "Vibrational Spectroscopy Ontology." + GitHub repository. + Available at: `https://github.com/NFDI4Chem/VibrationalSpectroscopyOntology `_ diff --git a/docs/source/benchmarking/ecology_and_environment/envo.rst b/docs/source/benchmarking/ecology_and_environment/envo.rst index 3a8ba7a..04ca612 100644 --- a/docs/source/benchmarking/ecology_and_environment/envo.rst +++ b/docs/source/benchmarking/ecology_and_environment/envo.rst @@ -25,31 +25,29 @@ Environment Ontology (ENVO) ======================================================================================================== - ENVO (Environment Ontology) is a comprehensive, community-driven ontology for the concise, controlled description of environmental -systems, components, and processes [#envo-obo]_ [#envo-2013]_. It -provides standardized vocabulary for describing environmental features -such as biomes, ecosystems, habitats, environmental materials (for -example air, water, and soil), and environmental conditions [#envo-2016]_ -[#envo-2013]_. ENVO captures semantic relationships between environmental -entities and supports precise annotation of environmental, ecological, -biological, and biomedical datasets [#envo-2013]_ [#envo-2016]_. As an -open, FAIR-enabling ontology resource, ENVO promotes semantic -interoperability by providing formal ontological definitions for -environmental concepts that can be used by humans, machines, and -Semantic Web applications [#envo-obo]_ [#envo-2016]_. The ontology -supports diverse applications including environmental data management, -ecology, biodiversity and microbiome studies, and other research that -requires interoperable environmental descriptions [#envo-2013]_ -[#envo-2016]_. +systems, components, and processes [#envo-obo]_ [#envo-2016]_. It +provides a standardized vocabulary for describing environmental +features such as biomes, ecosystems, habitats, environmental materials, +and environmental conditions [#envo-2016]_ [#envo-obo]_. ENVO captures +semantic relationships between environmental entities and supports +precise annotation of environmental, ecological, biological, and +biomedical datasets [#envo-2016]_ [#envo-obo]_. As an open ontology +resource, ENVO promotes semantic interoperability by providing formal +definitions for environmental concepts that can be used by humans, +machines, and Semantic Web applications [#envo-obo]_ [#envo-2016]_. +The ontology supports diverse applications including environmental data +management, ecology, biodiversity and microbiome studies, and other +research that requires interoperable environmental descriptions +[#envo-2016]_ [#envo-obo]_. **Example Usage**: Annotate an environmental dataset with ENVO terms for -a biome, habitat, environmental material, or environmental condition. For -example, terms describing a tropical rainforest environment, a +a biome, habitat, environmental material, or environmental condition. +For example, use terms describing a tropical rainforest environment, a biogeographic setting, or elevated soil moisture to enable semantic search, cross-study integration, and automated discovery of related -environmental and ecological data [#envo-2013]_ [#envo-2016]_. +environmental and ecological data [#envo-2016]_ [#envo-obo]_. Metrics & Statistics -------------------------- @@ -165,13 +163,6 @@ References .. [#envo-obo] OBO Foundry. n.d. "Environment Ontology (ENVO)." Available at: `https://obofoundry.org/ontology/envo.html `_ -.. [#envo-2013] Buttigieg, P. L., Morrison, N., Smith, B., Mungall, C. J., - and Lewis, S. E. 2013. "The Environment Ontology: Contextualising - Biological and Biomedical Entities." - *Journal of Biomedical Semantics* 4:43. - doi:10.1186/2041-1480-4-43 - Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC3904460/ `_ - .. [#envo-2016] Buttigieg, P. L., Pafilis, E., Lewis, S. E., Schildhauer, M. P., Walls, R. L., and Mungall, C. J. 2016. "The Environment Ontology in 2016: Bridging Domains with Increased diff --git a/docs/source/benchmarking/ecology_and_environment/oeo.rst b/docs/source/benchmarking/ecology_and_environment/oeo.rst index f47db89..60ca1c2 100644 --- a/docs/source/benchmarking/ecology_and_environment/oeo.rst +++ b/docs/source/benchmarking/ecology_and_environment/oeo.rst @@ -33,16 +33,16 @@ within the Open Energy Platform ecosystem, OEO provides standardized terminology for representing energy systems, including generation, conversion, transmission, distribution, storage, and consumption concepts across different technologies and sectors [#oeo-paper]_ -[#oeo-github]_. The ontology is represented in Manchester OWL Syntax, -chosen to support user-friendly editing and version control in -collaborative development workflows [#oeo-paper]_ [#oeo-github]_. OEO is -updated regularly through a release cycle and is continuously extended -to incorporate new concepts relevant to energy system modelling and -analysis [#oeo-site]_ [#oeo-github]_. By providing a shared semantic -framework, OEO supports standardized data annotation, knowledge -integration, semantic search, model interfacing, and automated -reasoning for energy system research and related applications -[#oeo-paper]_ [#oeo-site]_. +[#oeo-site]_. The ontology is intended to support collaborative and +interoperable modeling of energy knowledge, enabling clearer data +interpretation and more consistent interfacing between models, datasets, +and analytical workflows [#oeo-paper]_ [#oeo-site]_. OEO is updated +through an ongoing development and release process in order to +incorporate concepts relevant to energy system modelling and analysis +[#oeo-site]_ [#oeo-paper]_. By providing a shared semantic framework, +OEO supports standardized data annotation, knowledge integration, +semantic search, model interfacing, and automated reasoning for energy +system research and related applications [#oeo-paper]_ [#oeo-site]_. **Example Usage**: Annotate an energy system dataset with OEO terms to describe energy carriers and sources, generation technologies, @@ -175,6 +175,3 @@ References .. [#oeo-site] Open Energy Platform. n.d. "OEO Ontology." Available at: `https://openenergyplatform.org/ontology/ `_ - -.. [#oeo-github] OpenEnergyPlatform. n.d. "Repository for the Open Energy Ontology (OEO)." - Available at: `https://github.com/OpenEnergyPlatform/ontology `_ diff --git a/docs/source/benchmarking/ecology_and_environment/sweet.rst b/docs/source/benchmarking/ecology_and_environment/sweet.rst index c07cc81..8f58d42 100644 --- a/docs/source/benchmarking/ecology_and_environment/sweet.rst +++ b/docs/source/benchmarking/ecology_and_environment/sweet.rst @@ -25,26 +25,25 @@ Semantic Web for Earth and Environment Technology Ontology (SWEET) ======================================================================================================== -SWEET is a comprehensive collection of interconnected ontologies designed to enhance discovery and utilization of Earth science data through semantic understanding of web resources and Earth system science concepts. It conceptualizes a knowledge space for Earth system science including orthogonal (cross-cutting) concepts such as space, time, Earth realms (atmosphere, hydrosphere, lithosphere), physical quantities, and units, alongside integrative science knowledge concepts such as phenomena, events, and processes. SWEET is represented in OWL (Web Ontology Language) to enable automated reasoning and semantic interoperability in Earth science research. The ontology supports integration of heterogeneous Earth science datasets and models by providing shared semantic definitions across atmospheric science, oceanography, geology, and climate science domains. SWEET facilitates Earth science data discovery and knowledge management by enabling semantic search and automated linking of related datasets and research findings. SWEET (Semantic Web for Earth and Environmental Terminology) is a comprehensive collection of interconnected ontologies designed to improve discovery and use of Earth science data through semantic -understanding of web resources and Earth system science concepts +representation of Earth system science concepts and web resources [#sweet-paper]_ [#sweet-repo]_. It conceptualizes a knowledge space for Earth system science that includes cross-cutting concepts such as space, time, Earth realms, phenomena, physical quantities, and units, alongside more domain-specific scientific concepts [#sweet-paper]_ -[#sweet-repo]_. SWEET is represented in OWL and organized as a highly -modular ontology suite, enabling semantic interoperability and automated +[#sweet-repo]_. SWEET is represented in OWL and organized as a modular +ontology suite, enabling semantic interoperability and automated reasoning in Earth and environmental science applications [#sweet-paper]_ [#sweet-repo]_. The ontology supports integration of heterogeneous Earth science datasets and models by providing shared semantic definitions across domains such as atmospheric science, oceanography, geology, and climate science [#sweet-paper]_ -[#sweet-search]_. By providing a shared semantic framework, SWEET +[#sweet-repo]_. By providing a shared semantic framework, SWEET supports Earth science data discovery, semantic search, and knowledge management across distributed datasets and services [#sweet-paper]_ -[#sweet-search]_. +[#sweet-repo]_. **Example Usage**: Annotate a climate or Earth observation dataset with SWEET terms to describe observed phenomena, Earth realm or layer, @@ -173,8 +172,3 @@ References "SWEET: Official repository for Semantic Web for Earth and Environmental Terminology Ontologies." Available at: `https://github.com/ESIPFed/sweet `_ - -.. [#sweet-search] Pouchard, L. C., Huhns, M. N., and McGuinness, D. L. - 2013. "Linking Earth and Climate Science to Support Semantic Search." - *Semantic Web*. - Available at: `https://semantic-web-journal.net/content/linking-earth-and-climate-science-semantic-search-supporting-investigation-climate-change `_ diff --git a/docs/source/benchmarking/education/bibframe.rst b/docs/source/benchmarking/education/bibframe.rst index 7b423ad..c716a98 100644 --- a/docs/source/benchmarking/education/bibframe.rst +++ b/docs/source/benchmarking/education/bibframe.rst @@ -23,10 +23,29 @@ Bibliographic Framework Ontology (BIBFRAME) ======================================================================================================== -The Bibliographic Framework Ontology (BIBFRAME) is a comprehensive RDF-based vocabulary developed by the Library of Congress to modernize bibliographic description for libraries, museums, and archives. It provides a structured model for representing bibliographic resources, focusing on three core classes: Work (the abstract creative content), Instance (the physical or digital embodiment), and Item (the specific copy). BIBFRAME supports detailed description of relationships among resources, such as translations, adaptations, and editions, as well as attributes like subject, extent, and publication information. The ontology is designed to facilitate linked data publishing, interoperability, and integration with other metadata standards, enabling richer discovery and reuse of bibliographic information. BIBFRAME is widely adopted by libraries and cultural heritage institutions transitioning from MARC records to semantic web technologies. Its extensible structure allows for domain-specific adaptations and integration with authority files, vocabularies, and digital repositories. - -**Example Usage**: -Describe a library book using BIBFRAME by linking the Work (e.g., "Pride and Prejudice"), its Instance (the 2003 Penguin Classics edition), and the Item (the specific copy held by a library), including relationships to subjects, authors, and related works. +The Bibliographic Framework Ontology (BIBFRAME) is an RDF-based +vocabulary developed by the Library of Congress to modernize +bibliographic description for libraries, museums, and archives +[#bibframe-report]_ [#bibframe-loc]_. It provides a structured model +for representing bibliographic resources, centered on core classes such +as Work, Instance, and Item, together with properties for describing +relationships, subjects, contributions, publication details, and other +bibliographic characteristics [#bibframe-loc]_ [#bibframe-report]_. +BIBFRAME is designed to support linked data publishing, semantic +interoperability, and integration with other metadata standards, +enabling richer discovery and reuse of bibliographic information +[#bibframe-report]_ [#bibframe-loc]_. By providing an extensible +semantic framework for bibliographic description, BIBFRAME supports the +transition from legacy cataloging models to linked data environments in +libraries and cultural heritage institutions [#bibframe-report]_ +[#bibframe-loc]_. + +**Example Usage**: Describe a library book using BIBFRAME by linking the +Work, such as *Pride and Prejudice*, to an Instance representing a +specific edition and to an Item representing a particular copy held by a +library, while also connecting the resource to authors, subjects, and +related works for improved discovery and interoperability +[#bibframe-loc]_ [#bibframe-report]_. Metrics & Statistics -------------------------- @@ -135,3 +154,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#bibframe-report] Library of Congress. 2012. + "Bibliographic Framework as a Web of Data: Linked Data Model and + Supporting Services." + Available at: + `https://www.loc.gov/bibframe/news/pdf/marcld-report-11-21-2012.pdf `_ + + .. [#bibframe-loc] Library of Congress. n.d. "BIBFRAME Model, Vocabulary, + Guidelines, Examples, and Vocabulary Mapping." + Available at: + `https://www.loc.gov/bibframe/docs/index.html `_ diff --git a/docs/source/benchmarking/education/common.rst b/docs/source/benchmarking/education/common.rst index 5ed8a1c..a4dc5fa 100644 --- a/docs/source/benchmarking/education/common.rst +++ b/docs/source/benchmarking/education/common.rst @@ -23,10 +23,24 @@ Common Ontology (Common) ======================================================================================================== -The Common Ontology provides a foundational vocabulary for representing shared and reusable elements used across the Trias (Transport and Mobility) ontology framework. It defines core concepts such as entities, attributes, relationships, and constraints that are applicable to multiple mobility and transportation domains. The ontology captures common modeling patterns for representing agents, organizations, times, locations, and relationships in a standardized way across diverse transportation contexts. Common Ontology enables semantic interoperability across Trias domain modules (mobility, traffic, vehicles) by providing consistent foundational concepts and relationships. It serves as the shared semantic backbone for specialized domain ontologies in transportation, facilitating knowledge integration and reasoning. - -**Example Usage**: -Use Common Ontology entity types to represent agents (persons, organizations), locations (stops, streets, zones), time concepts, and relationships across transportation domain ontologies that extend or reference Common. +The Common Ontology provides a foundational vocabulary for representing +shared and reusable elements used across the Trias ontology framework +[#common-onto]_. It defines core concepts such as entities, attributes, +relationships, and constraints that are applicable to multiple +mobility and transportation domains [#common-onto]_. The ontology +captures common modeling patterns for representing agents, +organizations, times, locations, and relationships in a standardized +way across diverse transportation contexts [#common-onto]_. Common +Ontology enables semantic interoperability across Trias domain modules +by providing consistent foundational concepts and relationships +[#common-onto]_. It serves as the shared semantic backbone for +specialized domain ontologies in transportation, facilitating knowledge +integration and reasoning [#common-onto]_. + +**Example Usage**: Use Common Ontology entity types to represent agents +such as persons and organizations, locations such as stops, streets, +and zones, time concepts, and relationships across transportation +domain ontologies that extend or reference Common [#common-onto]_. Metrics & Statistics -------------------------- @@ -135,3 +149,11 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#common-onto] Toledo, J., García, M. A., and Corcho, O. n.d. + "Common Ontology." + Available at: + `https://w3id.org/mobility/trias/common/0.1.0 `_ diff --git a/docs/source/benchmarking/education/doco.rst b/docs/source/benchmarking/education/doco.rst index ef938f7..b27629d 100644 --- a/docs/source/benchmarking/education/doco.rst +++ b/docs/source/benchmarking/education/doco.rst @@ -23,10 +23,30 @@ Document Components Ontology (DoCO) ======================================================================================================== -DoCO, the Document Components Ontology, is an OWL 2 DL ontology that provides a general-purpose structured vocabulary of document elements. DoCO has been designed as a general unifying ontological framework for describing different aspects related to the content of scientific and other scholarly texts. Its primary goal has been to improve the interoperability and shareability of academic documents (and related services) when multiple formats are actually used for their storage. The ontology defines a wide range of document components, such as sections, paragraphs, figures, tables, references, and metadata elements, supporting detailed semantic annotation of document structure. DoCO enables automated document processing, content extraction, and knowledge management in digital libraries, publishing platforms, and research repositories. By providing a standardized vocabulary, DoCO facilitates the integration of scholarly content across diverse formats and platforms, enhancing discoverability and reuse of academic knowledge. - -**Example Usage**: -Annotate a scientific article with DoCO terms to specify its sections (introduction, methods, results, discussion), figures, tables, and references, enabling automated extraction and semantic search of document components. +DoCO, the Document Components Ontology, is an OWL 2 DL ontology that +provides a general-purpose structured vocabulary of document elements +[#doco-paper]_ [#doco-site]_. DoCO has been designed as a general +unifying ontological framework for describing different aspects related +to the content of scientific and other scholarly texts +[#doco-paper]_ [#doco-site]_. Its primary goal is to improve the +interoperability and shareability of academic documents and related +services when multiple formats are used for their storage +[#doco-paper]_ [#doco-site]_. The ontology defines a wide range of +document components, such as sections, paragraphs, figures, tables, +references, and metadata elements, supporting detailed semantic +annotation of document structure [#doco-paper]_ [#doco-site]_. DoCO +enables automated document processing, content extraction, and +knowledge management in digital libraries, publishing platforms, and +research repositories [#doco-paper]_ [#doco-site]_. By providing a +standardized vocabulary, DoCO facilitates the integration of scholarly +content across diverse formats and platforms, enhancing discoverability +and reuse of academic knowledge [#doco-paper]_ [#doco-site]_. + +**Example Usage**: Annotate a scientific article with DoCO terms to +specify its sections, such as introduction, methods, results, and +discussion, together with figures, tables, and references, enabling +automated extraction and semantic search of document components +[#doco-paper]_ [#doco-site]_. Metrics & Statistics -------------------------- @@ -135,3 +155,18 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#doco-paper] Constantin, A., Peroni, S., Pettifer, S., + Shotton, D., and Vitali, F. 2015. + "The Document Components Ontology (DoCO)." + *Semantic Web*. + doi:10.3233/SW-150177 + Available at: + `https://research.manchester.ac.uk/en/publications/the-document-components-ontology-doco/ `_ + +.. [#doco-site] SPAR Ontologies. n.d. "DoCO, the Document Components Ontology." + Available at: + `https://www.sparontologies.net/ontologies/doco `_ diff --git a/docs/source/benchmarking/events/conference.rst b/docs/source/benchmarking/events/conference.rst index 287894c..eb98f5e 100644 --- a/docs/source/benchmarking/events/conference.rst +++ b/docs/source/benchmarking/events/conference.rst @@ -25,9 +25,30 @@ Conference Ontology (Conference) ======================================================================================================== -The Conference Ontology is a self-contained ontology for modeling conferences, workshops, and related scholarly events. It captures core entities (events, organizers, venues, sessions, papers, posters, and participants) and their relationships, allowing structured representation of program schedules, affiliations, and scholarly communications around conferences. Designed following ontology design patterns and reuse principles, it reuses established vocabularies (e.g., FOAF, BIBO, schema.org) where appropriate and interlinks with the SWC (Semantic Web Conference) ontology to ensure interoperability. The ontology models temporal and spatial aspects (start/end times, room assignments), roles and responsibilities (chairs, speakers, reviewers), and provenance metadata (submission dates, acceptance decisions). Typical applications include conference management systems, semantic search of proceedings, program generation, and linking publications to presentation metadata. - -**Example usage**: represent a conference session as an Event with start/end times, linked to a Room (Venue), containing multiple Talk instances each linked to Speaker agents and associated Paper resources. The ontology supports export to RDF/OWL and integration with digital libraries, repositories, and research discovery services. +The Conference Ontology is a self-contained ontology for modeling +conferences, workshops, and related scholarly events +[#conference-onto]_ [#conference-paper]_. It captures core entities +such as events, organizers, venues, sessions, papers, posters, and +participants, together with their relationships, enabling structured +representation of program schedules, affiliations, and scholarly +communications around conferences [#conference-onto]_ +[#conference-paper]_. Designed following ontology design patterns and +reuse principles, it reuses established vocabularies where appropriate +and interlinks with the Semantic Web Conference ontology to support +interoperability [#conference-paper]_ [#conference-onto]_. The ontology +models temporal and spatial aspects, roles and responsibilities, and +provenance-related information relevant to conference organization and +scholarly communication [#conference-onto]_ [#conference-paper]_. It +supports applications such as conference management systems, semantic +search of proceedings, program generation, and linking publications to +presentation metadata [#conference-onto]_ [#conference-paper]_. + +**Example Usage**: Represent a conference session as an event with +start and end times, linked to a room or venue, and containing multiple +talk instances that are connected to speaker agents and associated +paper resources, enabling RDF/OWL-based integration with digital +libraries, repositories, and research discovery services +[#conference-onto]_ [#conference-paper]_. Metrics & Statistics -------------------------- @@ -136,3 +157,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#conference-onto] scholarlydata.org. n.d. "The Conference Ontology." + Available at: + `https://www.scholarlydata.org/ontology/doc/ `_ + +.. [#conference-paper] Nuzzolese, A. G., Gentile, A. L., + Presutti, V., and Gangemi, A. 2016. + "Semantic Web Conference Ontology - A Refactoring Solution." + In *The Semantic Web: ESWC 2016 Satellite Events*, + Lecture Notes in Computer Science 9989, pp. 84-87. + doi:10.1007/978-3-319-47602-5_18 diff --git a/docs/source/benchmarking/events/icalendar.rst b/docs/source/benchmarking/events/icalendar.rst index f44a576..67ab8c1 100644 --- a/docs/source/benchmarking/events/icalendar.rst +++ b/docs/source/benchmarking/events/icalendar.rst @@ -25,9 +25,31 @@ iCalendar Vocabulary (iCalendar) ======================================================================================================== -iCalendar is an Internet standard and RDF vocabulary for exchanging calendar and scheduling data across applications using a standardized representation (.ics and RDF views). It models core scheduling concepts such as Events, To-Dos, Recurrence Rules, Attendees, Time Zones, and Alarms, enabling interoperable representation of meetings, recurring appointments, and calendar invitations. While the original iCalendar format (.ics) is a text standard widely implemented in client applications (Google Calendar, Outlook), the iCalendar vocabulary expresses these concepts in RDF to support Semantic Web integration, richer linking, and automated reasoning. Key characteristics include explicit recurrence modelling, timezone-aware datatypes, and support for invitations and participation roles. Common applications include calendar synchronization, meeting scheduling services, automated reminders, and semantic linking of event metadata with external datasets. - -**Example usage**: represent a weekly team meeting as an Event with a RRULE for weekly recurrence, linked Attendee agents with roles (organizer, participant), and timezone-aware start/end datetimes. The vocabulary can be combined with other ontologies (e.g., FOAF for people) to enrich event descriptions and support advanced calendar automation. +iCalendar is an Internet standard and RDF vocabulary for exchanging +calendar and scheduling data across applications using standardized +representations such as iCalendar files and RDF views +[#ical-rdf-note]_ [#ical-rfc]_. It models core scheduling concepts such +as events, to-dos, recurrence rules, attendees, time zones, and alarms, +enabling interoperable representation of meetings, recurring +appointments, and calendar invitations [#ical-rdf-note]_ [#ical-rfc]_. +While the original iCalendar format is a text-based standard widely +implemented in calendar clients, the iCalendar vocabulary expresses +these concepts in RDF to support Semantic Web integration, richer +linking, and machine-processable event data [#ical-rdf-note]_ +[#ical-w3c]_. Key characteristics include explicit recurrence modeling, +timezone-aware date and time representation, and support for +invitations and participation roles [#ical-rdf-note]_ [#ical-rfc]_. +By providing a shared semantic framework for calendar and scheduling +data, the vocabulary supports calendar synchronization, meeting +scheduling services, automated reminders, and semantic linking of event +metadata with external datasets [#ical-rdf-note]_ [#ical-w3c]_. + +**Example Usage**: Represent a weekly team meeting as an event with a +recurrence rule for weekly repetition, linked attendee agents with +roles such as organizer and participant, and timezone-aware start and +end datetimes. The vocabulary can also be combined with other +ontologies, such as FOAF for people, to enrich event descriptions and +support advanced calendar automation [#ical-rdf-note]_ [#ical-rfc]_. Metrics & Statistics -------------------------- @@ -136,3 +158,24 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#ical-rdf-note] Connolly, D., and Miller, L. 2005. + "RDF Calendar - An Application of the Resource Description Framework + to iCalendar Data." + W3C Note, 29 September 2005. + Available at: + `https://www.w3.org/2002/12/cal/report1173.html `_ + +.. [#ical-w3c] W3C. n.d. "RDF Calendar Workspace." + Available at: + `https://www.w3.org/2002/12/cal/ `_ + +.. [#ical-rfc] Desruisseaux, B. 2009. + "Internet Calendaring and Scheduling Core Object Specification + (iCalendar)." + RFC 5545. + Available at: + `https://www.rfc-editor.org/rfc/rfc5545 `_ diff --git a/docs/source/benchmarking/events/lode.rst b/docs/source/benchmarking/events/lode.rst index 2682e2c..1ff17c4 100644 --- a/docs/source/benchmarking/events/lode.rst +++ b/docs/source/benchmarking/events/lode.rst @@ -25,9 +25,28 @@ Linking Open Descriptions of Events (LODE) ======================================================================================================== -LODE (Linking Open Descriptions of Events) is an ontology for publishing and interlinking structured event descriptions as Linked Data. It provides lightweight classes and properties for representing events, their time and place, and simple relationships to agents and sources. LODE is intentionally minimalistic to maximize interoperability and ease of adoption: it models events as occurrences with temporal extents and locations, and supports linking to richer event models when needed. Typical use cases include event directories, cultural heritage timelines, news event annotation, and discovery services that aggregate event records from multiple data providers. LODE emphasizes stable URIs and practical tools for populating event descriptions, enabling the creation of a searchable event directory of historical and contemporary events. - -**Example usage**: describe a public lecture as an lode:Event with a start/end time, a dcterms:spatial property linking to a Place URI, and a dc:source pointing to a news article; link the event to authority URIs for the speaker. The ontology's simplicity makes it a useful pivot for integrating event data across heterogeneous datasets. +LODE (Linking Open Descriptions of Events) is an ontology for +publishing and interlinking structured event descriptions as Linked +Data [#lode-paper]_ [#lode-site]_. It provides lightweight classes and +properties for representing events, their time and place, and simple +relationships to agents and sources [#lode-paper]_ [#lode-site]_. +LODE is intentionally minimalistic in order to maximize +interoperability and ease of adoption, modeling events as occurrences +with temporal extents and locations while supporting links to richer +event models when needed [#lode-paper]_ [#lode-site]_. Typical use +cases include event directories, cultural heritage timelines, news +event annotation, and discovery services that aggregate event records +from multiple data providers [#lode-paper]_ [#lode-site]_. By +providing a lightweight and stable semantic framework for event +descriptions, LODE supports semantic integration, search, and reuse of +event data across heterogeneous datasets [#lode-paper]_ [#lode-site]_. + +**Example Usage**: Describe a public lecture as a ``lode:Event`` with +a start and end time, a ``dcterms:spatial`` property linking to a place +URI, and a ``dc:source`` pointing to a news article, while linking the +event to authority URIs for the speaker. The ontology’s simplicity +makes it a useful pivot for integrating event data across heterogeneous +datasets [#lode-paper]_ [#lode-site]_. Metrics & Statistics -------------------------- @@ -136,3 +155,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#lode-paper] Shaw, R., Troncy, R., and Hardman, L. 2009. + "LODE: Linking Open Descriptions of Events." + In *The Semantic Web: Research and Applications*, pp. 153-167. + doi:10.1007/978-3-642-02121-3_11 + Available at: + `https://link.springer.com/chapter/10.1007/978-3-642-02121-3_11 `_ + +.. [#lode-site] Linked Events. n.d. "LODE Ontology Specification." + Available at: + `https://linkedevents.org/ontology/ `_ diff --git a/docs/source/benchmarking/finance/goodrelations.rst b/docs/source/benchmarking/finance/goodrelations.rst index 396a6e4..517fb53 100644 --- a/docs/source/benchmarking/finance/goodrelations.rst +++ b/docs/source/benchmarking/finance/goodrelations.rst @@ -32,20 +32,17 @@ information such as offers, business entities, price specifications, availability, payment options, and delivery methods [#gr-paper]_ [#gr-ref]_. GoodRelations emphasizes machine-processable, fine-grained descriptions of e-commerce information that support product discovery, -comparison, and automated processing on the Web [#gr-paper]_ [#gr-wiki]_. -A key design principle is the distinction between products or services, -the offers made for them, and the legal entities that provide them, -together with detailed modeling of prices and commercial conditions -[#gr-paper]_ [#gr-ref]_. The ontology is designed for interoperability -and can be used in RDF/OWL as well as embedded in Web pages; it also -influenced and was integrated into the schema.org e-commerce model -[#gr-wiki]_ [#schema-releases]_. By providing a shared semantic +comparison, and automated processing on the Web [#gr-paper]_ +[#gr-wiki]_. A key design principle is the distinction between products +or services, the offers made for them, and the legal entities that +provide them, together with detailed modeling of prices and commercial +conditions [#gr-paper]_ [#gr-ref]_. By providing a shared semantic framework for commercial data, GoodRelations supports e-commerce SEO, catalog integration, offer aggregation, and other Semantic Web and Web data applications [#gr-paper]_ [#gr-wiki]_. -**Example Usage**: Describe a product offering as a -``gr:Offering`` that links to a product or service, includes a +**Example Usage**: Describe a product offering as a ``gr:Offering`` +that links to a product or service, includes a ``gr:UnitPriceSpecification`` with currency and price information, and connects to a ``gr:BusinessEntity`` representing the seller together with relevant payment, delivery, and offer-validity information, so @@ -177,7 +174,3 @@ References .. [#gr-wiki] GoodRelations Wiki. n.d. "Documentation/Intro." Available at: `https://wiki.goodrelations-vocabulary.org/Documentation/Intro `_ - -.. [#schema-releases] Schema.org. 2026. - "Schema.org Releases." - Available at: `https://schema.org/docs/releases.html `_ diff --git a/docs/source/benchmarking/food_and_beverage/wine.rst b/docs/source/benchmarking/food_and_beverage/wine.rst index b89d141..90d4ee0 100644 --- a/docs/source/benchmarking/food_and_beverage/wine.rst +++ b/docs/source/benchmarking/food_and_beverage/wine.rst @@ -25,7 +25,24 @@ Wine Ontology (Wine) ======================================================================================================== -The Wine Ontology is a comprehensive RDF-based vocabulary for describing wines, wine-making processes, vineyards, and the wine industry. It provides a detailed classification system for different types of wines (red, white, rosé, sparkling), wine regions, grape varieties, producers, and tasting characteristics. The ontology captures properties such as alcohol content, acidity, vintage year, and flavor profiles, enabling precise semantic representation of wine products and attributes. It integrates with related ontologies for representing food, geography, and commercial information, supporting applications in e-commerce, wine recommendation systems, and food science research. Example: A specific wine instance can be linked to its grape variety (Pinot Noir), wine region (Burgundy), producer, vintage year, and taste descriptors through well-defined ontology properties. +The Wine Ontology is an RDF-based vocabulary for describing wines and +related wine domain concepts in a structured and machine-readable way +[#wine-w3c]_ [#wine-repo]_. It provides a classification framework for +different types of wines, grape varieties, wine regions, wineries, and +wine characteristics, supporting semantic representation of wine +products and their attributes [#wine-w3c]_ [#wine-repo]_. As a well-known +example ontology in the Semantic Web community, it has been used to +illustrate ontology modeling patterns and reasoning over class-based +descriptions [#wine-w3c]_. The ontology can support applications such +as semantic data integration, structured search, recommendation, and +knowledge representation in domains involving wine, food, and related +commercial or cultural information [#wine-repo]_ [#wine-w3c]_. + +**Example Usage**: Describe a specific wine by linking it to its grape +variety, wine region, producer, vintage year, and taste-related +characteristics through ontology properties, enabling structured search, +comparison, and semantic integration of wine-related information +[#wine-repo]_ [#wine-w3c]_. Metrics & Statistics -------------------------- @@ -134,3 +151,16 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#wine-w3c] McGuinness, D. L., and van Harmelen, F. 2004. + "OWL Web Ontology Language Guide." + W3C Recommendation, 10 February 2004. + Available at: + `https://www.w3.org/TR/owl-guide/ `_ + +.. [#wine-repo] UC Davis Library. n.d. "Wine Linked Data / Wine Ontology." + Available at: + `https://github.com/UCDavisLibrary/wine-ontology `_ diff --git a/docs/source/benchmarking/general_knowledge/cco.rst b/docs/source/benchmarking/general_knowledge/cco.rst index 537166b..7cc9503 100644 --- a/docs/source/benchmarking/general_knowledge/cco.rst +++ b/docs/source/benchmarking/general_knowledge/cco.rst @@ -25,10 +25,35 @@ Common Core Ontologies (CCO) ======================================================================================================== -The Common Core Ontologies (CCO) is a comprehensive suite of eleven interconnected ontologies providing logically well-defined generic terms and relations applicable across all domains of interest. CCO is built on formal semantic principles, ensuring that its concepts are unambiguous, semantically consistent, and applicable to diverse knowledge representation tasks. The ontology covers foundational concepts including objects, events, qualities, locations, and abstract entities, with explicit definitions of relationships between them. CCO is designed for maximum reusability across domain ontologies, enabling developers to extend CCO terms for specialized applications while maintaining semantic interoperability. The ontologies are documented with formal definitions, examples, and competency questions supporting both human understanding and computational reasoning. CCO has been adopted in enterprise information systems, knowledge graph construction, and semantic data integration projects requiring rigorous ontological foundations. - -**Example Usage**: -Represent a business domain ontology by extending CCO's generic Object and Event concepts to define company-specific entities (employees, contracts, transactions) and their relationships, ensuring compatibility with other systems using CCO foundations. +The Common Core Ontologies (CCO) are a suite of eleven interconnected +mid-level ontologies that provide logically well-defined generic terms +and relations applicable across many domains of interest [#cco-repo]_ +[#cco-paper]_. CCO extends the Basic Formal Ontology (BFO), an upper-level +ontology, and is designed to support semantic interoperability, data +integration, and reusable domain ontology development [#cco-repo]_. + +CCO is built on formal semantic principles, ensuring that its concepts +are unambiguous, semantically consistent, and suitable for computational +reasoning [#cco-paper]_. The ontology suite covers foundational concepts +including objects, processes, qualities, information entities, locations, +units of measure, agents, artifacts, facilities, and relations between +entities [#cco-repo]_ [#cco-paper]_. Its terms are intended to be reused +and extended by domain-specific ontologies while preserving compatibility +with other CCO- and BFO-based systems [#cco-repo]_. + +The ontologies are documented with formal definitions, examples, and +design patterns that support both human understanding and automated +reasoning [#cco-paper]_. CCO can be used in enterprise information +systems, knowledge graph construction, semantic data integration, and +ontology engineering projects that require rigorous semantic foundations +[#cco-repo]_ [#cco-paper]_. + +**Example Usage**: Represent a business domain ontology by extending +CCO's generic Object, Agent, Organization, and Event/Process concepts to +define company-specific entities such as employees, contracts, +transactions, departments, and business activities. This helps ensure +that the business ontology remains compatible with other systems using +CCO or BFO-based semantic foundations [#cco-repo]_ [#cco-paper]_. Metrics & Statistics -------------------------- @@ -137,3 +162,18 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------------- +.. [#cco-repo] Common Core Ontology Repository. n.d. + "The Common Core Ontologies." + GitHub Repository. + Available at: + `https://github.com/CommonCoreOntology/CommonCoreOntologies `_ + +.. [#cco-paper] Jensen, M., De Colle, G., Kindya, S., More, C., + Cox, A. P., and Beverley, J. 2024. + "The Common Core Ontologies." + arXiv. + Available at: + `https://arxiv.org/abs/2404.17758 `_ diff --git a/docs/source/benchmarking/general_knowledge/dbpedia.rst b/docs/source/benchmarking/general_knowledge/dbpedia.rst index 939a94e..31fce69 100644 --- a/docs/source/benchmarking/general_knowledge/dbpedia.rst +++ b/docs/source/benchmarking/general_knowledge/dbpedia.rst @@ -25,10 +25,35 @@ DBpedia Ontology (DBpedia) ======================================================================================================== -The DBpedia ontology is generated from manually curated specifications in the DBpedia Mappings Wiki, providing a structured semantic model extracted from Wikipedia's rich content across multiple language editions. Each DBpedia release corresponds to a new Wikipedia data extraction, resulting in continuously evolving ontology versions that reflect growing knowledge representation in Wikipedia. The DBpedia ontology has become a shallow but comprehensive cross-domain ontology through crowd-sourced development involving thousands of contributors worldwide. It covers diverse knowledge domains including people, organizations, places, creative works, scientific concepts, and many others with relationships between them. DBpedia serves as a critical bridge between Wikipedia's unstructured information and the semantic web, enabling knowledge graph applications and linked data integration. The ontology is widely used in knowledge graph construction, information retrieval, entity linking, and semantic data integration projects leveraging Wikipedia's comprehensive and multilingual knowledge base. - -**Example Usage**: -Query DBpedia to find relationships between entities (e.g., all people born in Berlin, all films directed by a specific director, companies in a particular industry) by using ontology classes (Person, Film, Company) and properties to enable advanced knowledge discovery and data analytics. +The DBpedia ontology is generated from manually curated specifications +in the DBpedia Mappings Wiki, providing a structured semantic model +extracted from Wikipedia's rich content across multiple language +editions [#dbpedia-ontology]_ [#dbpedia-paper]_. Each DBpedia release +corresponds to a new Wikipedia data extraction, resulting in evolving +ontology versions that reflect changes and growth in Wikipedia-based +knowledge representation [#dbpedia-ontology]_. + +The DBpedia ontology is a shallow but comprehensive cross-domain +ontology developed through community-based mapping and curation +activities [#dbpedia-ontology]_ [#dbpedia-paper]_. It covers diverse +knowledge domains including people, organizations, places, creative +works, scientific concepts, events, and many other entity types, together +with properties that describe relationships between them +[#dbpedia-ontology]_. + +DBpedia serves as a bridge between Wikipedia's semi-structured +information and the Semantic Web, enabling linked data publication, +knowledge graph construction, information retrieval, entity linking, and +semantic data integration [#dbpedia-paper]_. Its ontology and mappings +allow Wikipedia-derived information to be represented in RDF and queried +using semantic technologies such as SPARQL [#dbpedia-paper]_. + +**Example Usage**: Query DBpedia to find relationships between entities, +such as all people born in Berlin, all films directed by a specific +director, or companies in a particular industry, by using ontology +classes such as Person, Film, and Company together with ontology +properties that support structured knowledge discovery and data +analytics [#dbpedia-ontology]_ [#dbpedia-paper]_. Metrics & Statistics -------------------------- @@ -137,3 +162,20 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +--------------- + +.. [#dbpedia-ontology] DBpedia Association. n.d. + "DBpedia Ontology." + Available at: + `https://www.dbpedia.org/resources/ontology/ `_ + +.. [#dbpedia-paper] Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., + Kontokostas, D., Mendes, P. N., Hellmann, S., Morsey, M., + van Kleef, P., Auer, S., and Bizer, C. 2015. + "DBpedia: A Large-scale, Multilingual Knowledge Base Extracted + from Wikipedia." + Semantic Web 6(2): 167-195. + Available at: + `https://jens-lehmann.org/files/2015/swj_dbpedia.pdf `_ diff --git a/docs/source/benchmarking/general_knowledge/dublincore.rst b/docs/source/benchmarking/general_knowledge/dublincore.rst index ce58940..3d5b9f1 100644 --- a/docs/source/benchmarking/general_knowledge/dublincore.rst +++ b/docs/source/benchmarking/general_knowledge/dublincore.rst @@ -25,10 +25,35 @@ Dublin Core Vocabulary (DublinCore) ======================================================================================================== -The Dublin Core Schema is a small yet powerful vocabulary providing essential metadata elements for describing resources across diverse domains. Dublin Core Metadata can be used for multiple purposes including simple resource description, cross-standard metadata interoperability, and Linked Data cloud integration. It comprises fifteen core metadata elements (title, creator, subject, description, publisher, contributor, date, type, format, identifier, source, language, relation, coverage, rights) that are universally applicable across resource types. Dublin Core supports both simple and qualified metadata representation, enabling both basic and complex semantic annotation requirements. The vocabulary is language-independent and has become the de facto standard for resource description in digital libraries, institutional repositories, and data catalogs worldwide. Dublin Core facilitates semantic interoperability across heterogeneous information systems and enables automated resource discovery and management. - -**Example Usage**: -Annotate a research dataset or publication with Dublin Core terms including title, creator (author), date, subject (keywords), description, format (data type), identifier (DOI/URL), and rights (license) to enable standardized discovery and citation across digital repositories. +The Dublin Core Schema is a compact but powerful metadata vocabulary for +describing resources across diverse domains [#dublin-core]_ +[#dublin-core-paper]_. It provides a set of fifteen core metadata +elements, including title, creator, subject, description, publisher, +contributor, date, type, format, identifier, source, language, relation, +coverage, and rights, which are broadly applicable to many types of +resources [#dublin-core]_. + +Dublin Core metadata can be used for simple resource description, +cross-standard metadata interoperability, and resource discovery on the +Internet [#dublin-core-paper]_. The vocabulary supports basic resource +description using the Dublin Core Metadata Element Set and can be used +in digital libraries, repositories, archives, government systems, +scientific institutions, and business information systems +[#dublin-core]_ [#dublin-core-paper]_. + +Dublin Core is language-independent and widely used for resource +description and discovery across heterogeneous information systems +[#dublin-core-paper]_. By providing standardized metadata terms, it +supports semantic interoperability and enables automated resource +discovery, citation, description, and management [#dublin-core]_ +[#dublin-core-paper]_. + +**Example Usage**: Annotate a research dataset or publication with +Dublin Core terms such as title, creator, date, subject, description, +format, identifier, and rights. This allows the resource to be discovered, +cited, exchanged, and integrated across digital repositories and metadata +systems using a common semantic description model [#dublin-core]_ +[#dublin-core-paper]_. Metrics & Statistics -------------------------- @@ -137,3 +162,18 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------------- +.. [#dublin-core] Dublin Core Metadata Initiative. 2020. + "Dublin Core Metadata Element Set, Version 1.1." + Available at: + `https://www.dublincore.org/documents/dces/ `_ + +.. [#dublin-core-paper] Sugimoto, S., Baker, T., and Weibel, S. L. 2002. + "Dublin Core: Process and Principles." + In *Digital Libraries: People, Knowledge, and Technology*, + ICADL 2002, Lecture Notes in Computer Science, vol. 2555, + pp. 25--35. Springer, Berlin, Heidelberg. + DOI: + `10.1007/3-540-36227-4_3 `_ diff --git a/docs/source/benchmarking/general_knowledge/edam.rst b/docs/source/benchmarking/general_knowledge/edam.rst index 44e9838..b33932f 100644 --- a/docs/source/benchmarking/general_knowledge/edam.rst +++ b/docs/source/benchmarking/general_knowledge/edam.rst @@ -25,9 +25,36 @@ The ontology of data analysis and management (EDAM) ======================================================================================================== -EDAM is a domain ontology that formalizes concepts, operations, and data types used in computational data analysis and data management across biological sciences and related domains. It provides structured vocabulary for describing bioinformatics analysis workflows, computational operations (alignment, clustering, prediction), data types, data formats, and the relationships between analysis steps. EDAM comprises four main sections: Topic (research domains and concepts), Operation (analysis/processing operations), Data (data types and identifiers), and Format (computational data formats and standards). The ontology is designed for usability by diverse stakeholders including bioinformaticians, tool developers, and researchers, with a relatively simple hierarchical structure to facilitate adoption. EDAM supports standardization of bioinformatics tool descriptions, workflow definitions, and dataset annotations, enabling automated tool discovery and workflow composition. - -**Example Usage**: Annotate a bioinformatics tool or service with EDAM terms for Input data (e.g., EDAM:data_0006 for sequence alignment), Operation (e.g., EDAM:operation_0496 for pairwise sequence alignment), Output format (e.g., EDAM:format_1929 for FASTA), and research Topic (e.g., EDAM:topic_0199 for sequence analysis). +EDAM is a domain ontology that formalizes concepts, operations, data +types, identifiers, and formats used in computational data analysis and +data management across bioinformatics, biological sciences, and related +scientific domains [#edam-home]_ [#edam-paper]_. It provides a structured +vocabulary for describing bioinformatics analysis workflows, computational +operations, data types, data identifiers, data formats, and relationships +between analysis steps [#edam-paper]_. + +EDAM is organized into four main sections: Topic, Operation, Data, and +Format [#edam-home]_ [#edam-paper]_. Topic represents research domains +and application areas; Operation represents analysis and processing +functions; Data represents data types and identifiers; and Format +represents computational data formats and standards [#edam-home]_. This +structure enables consistent semantic annotation of tools, workflows, +databases, datasets, publications, and software resources in +bioinformatics [#edam-paper]_. + +EDAM is designed for usability by diverse stakeholders, including +bioinformaticians, tool developers, curators, and researchers +[#edam-home]_. Its relatively simple hierarchical organization supports +standardized descriptions of bioinformatics tools and services, automated +tool discovery, workflow composition, dataset annotation, and semantic +integration of computational biology resources [#edam-paper]_. + +**Example Usage**: Annotate a bioinformatics tool or service with EDAM +terms for input data, operation, output format, and research topic, such +as sequence alignment data, pairwise sequence alignment, FASTA format, +and sequence analysis. These annotations make the tool easier to discover, +compare, integrate into workflows, and connect with related datasets or +services [#edam-home]_ [#edam-paper]_. Metrics & Statistics -------------------------- @@ -136,3 +163,20 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#edam-home] EDAM Ontology. n.d. + "EDAM: The Ontology of Data Analysis and Management." + Available at: + `https://edamontology.org/ `_ + +.. [#edam-paper] Ison, J., Kalaš, M., Jonassen, I., Bolser, D., + Uludag, M., McWilliam, H., Malone, J., Lopez, R., Pettifer, S., + and Rice, P. 2013. + "EDAM: An Ontology of Bioinformatics Operations, Types of Data and + Identifiers, Topics and Formats." + *Bioinformatics* 29(10): 1325--1332. + DOI: + `10.1093/bioinformatics/btt113 `_ diff --git a/docs/source/benchmarking/general_knowledge/gist.rst b/docs/source/benchmarking/general_knowledge/gist.rst index d3c40c1..5ba3781 100644 --- a/docs/source/benchmarking/general_knowledge/gist.rst +++ b/docs/source/benchmarking/general_knowledge/gist.rst @@ -25,10 +25,34 @@ GIST Upper Ontology (GIST) ======================================================================================================== -GIST is Semantic Arts' minimalist upper ontology designed specifically for enterprise information systems, providing maximum coverage of typical business concepts with minimal primitives and minimal ambiguity. It emphasizes practical expressiveness and semantic clarity, avoiding unnecessary complexity while maintaining rigorous logical foundations. GIST covers essential business entities including agents (people, organizations), objects, events, measurements, and abstract concepts, with clearly defined relationships between them. The ontology is deliberately lightweight to facilitate adoption and integration into existing enterprise systems while providing sufficient semantic richness for sophisticated business logic representation. GIST supports both simple and complex semantic queries, reasoning, and knowledge graph construction for enterprise data integration and business intelligence applications. The ontology has been widely adopted in financial services, healthcare, and government sectors requiring reliable semantic foundations for data governance. - -**Example Usage**: -Design a healthcare enterprise ontology by extending GIST's Agent (to represent physicians, patients), Event (to represent treatments, procedures), and Object (to represent medications, medical devices) concepts to build a comprehensive healthcare knowledge graph for clinical decision support. +GIST is Semantic Arts' minimalist upper ontology designed for enterprise +information systems, providing broad coverage of common business concepts +with a small set of primitives and reduced ambiguity [#gist-home]_ +[#gist-repo]_. It is intended to serve as a practical starting point for +organizations building enterprise ontologies or semantic models for +business data integration [#gist-repo]_. + +GIST emphasizes practical expressiveness, semantic clarity, and usability +in enterprise settings [#gist-home]_ [#gist-repo]_. It covers essential +business concepts such as agents, organizations, people, physical and +conceptual objects, events, measurements, units, places, agreements, and +other abstract entities, together with relationships that support +structured representation of enterprise knowledge [#gist-repo]_. + +The ontology is deliberately lightweight so that it can be adopted and +extended more easily than larger or more complex upper ontologies +[#gist-home]_. GIST can support semantic querying, reasoning, knowledge +graph construction, enterprise data integration, data governance, and +business intelligence applications by providing a shared semantic +foundation for business information [#gist-home]_ [#gist-repo]_. + +**Example Usage**: Design a healthcare enterprise ontology by extending +GIST's Agent concepts to represent physicians and patients, Event +concepts to represent treatments and procedures, and Object concepts to +represent medications and medical devices. This can support the creation +of a healthcare knowledge graph for clinical decision support, data +integration, and semantic querying across healthcare information systems +[#gist-home]_ [#gist-repo]_. Metrics & Statistics -------------------------- @@ -137,3 +161,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#gist-home] Semantic Arts. n.d. + "gist Upper Ontology." + Available at: + `https://www.semanticarts.com/gist/ `_ + +.. [#gist-repo] Semantic Arts. n.d. + "gist." + GitHub Repository. + Available at: + `https://github.com/semanticarts/gist `_ diff --git a/docs/source/benchmarking/general_knowledge/iao.rst b/docs/source/benchmarking/general_knowledge/iao.rst index 9cf0896..c32468f 100644 --- a/docs/source/benchmarking/general_knowledge/iao.rst +++ b/docs/source/benchmarking/general_knowledge/iao.rst @@ -25,9 +25,34 @@ Information Artifact Ontology (IAO) ======================================================================================================== -The Information Artifact Ontology (IAO) is a comprehensive ontology for formal representation of information entities, information artifacts, and abstract information objects. It provides structured definitions of concepts such as documents, data items, information content, and the relationships between information artifacts and their physical realizations. IAO distinguishes between abstract information objects (the content) and their concrete realizations (documents, files, databases), enabling precise semantic representation of information resources. The ontology captures properties of information artifacts including authorship, creation date, version history, and relationships to the entities they describe or represent. IAO is widely used in biomedical informatics, scientific data management, and linked data applications for annotating information resources with formal semantic types. - -**Example Usage**: Annotate a scientific publication with IAO terms such as "document" for the artifact type, "author" relationships, and "has content" linking to abstract information objects representing the scientific claims and data presented. +The Information Artifact Ontology (IAO) is an ontology for representing +information entities, information artifacts, and related information +objects in a formal and semantically precise way [#iao-repo]_ +[#iao-paper]_. It provides structured concepts for describing entities +such as documents, data items, information content entities, identifiers, +and other representational artifacts [#iao-repo]_. + +IAO helps distinguish between information content and the concrete +artifacts or realizations through which that content is represented, +stored, transmitted, or used [#iao-paper]_. This makes it useful for +modeling relationships between data collections, documents, databases, +records, and the real-world entities or phenomena that they are about +[#iao-paper]_. + +The ontology is widely used in biomedical informatics, scientific data +management, ontology annotation, and linked data applications where +information resources need formal semantic types [#iao-repo]_ +[#iao-paper]_. It supports the annotation of publications, datasets, +measurement results, protocols, databases, licenses, and other +information-bearing resources in a consistent ontology-based framework +[#iao-repo]_. + +**Example Usage**: Annotate a scientific publication with IAO terms such +as document, data item, information content entity, and is about +relations. This can represent the publication as an information artifact, +connect it to its authorship or metadata, and link its content to the +scientific claims, datasets, or real-world entities described in the +publication [#iao-repo]_ [#iao-paper]_. Metrics & Statistics -------------------------- @@ -136,3 +161,19 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#iao-repo] Information Artifact Ontology. n.d. + "Information Artifact Ontology." + GitHub Repository. + Available at: + `https://github.com/information-artifact-ontology/IAO `_ + +.. [#iao-paper] Ceusters, W. 2012. + "An Information Artifact Ontology Perspective on Data Collections + and Associated Representational Artifacts." + *Studies in Health Technology and Informatics* 180: 68--72. + Available at: + `https://pubmed.ncbi.nlm.nih.gov/22874154/ `_ diff --git a/docs/source/benchmarking/general_knowledge/prov.rst b/docs/source/benchmarking/general_knowledge/prov.rst index 850d745..ab03096 100644 --- a/docs/source/benchmarking/general_knowledge/prov.rst +++ b/docs/source/benchmarking/general_knowledge/prov.rst @@ -25,10 +25,40 @@ PROV Ontology (PROV-O) ======================================================================================================== -The PROV Ontology (PROV-O) expresses the PROV Data Model using the OWL2 Web Ontology Language, providing a set of classes, properties, and restrictions for representing and interchanging provenance information. It captures the complete lifecycle of entities, activities, and agents involved in generating data across different systems and contexts. PROV-O enables formal representation of who created what, when they created it, and under what circumstances, supporting accountability and reproducibility in data-intensive research. The ontology is designed to be generic enough for diverse applications while allowing specialization for specific domains through extension mechanisms. PROV-O facilitates automated provenance tracking and reasoning, enabling systems to verify data quality, authenticity, and compliance with policies. The ontology is widely used in scientific workflows, data management systems, and enterprise information governance. - -**Example Usage**: -Annotate a dataset's provenance with PROV-O terms to document that entity X was generated by activity Y using agent Z as input, with timestamps and qualifications about how the transformation occurred, enabling complete traceability and reproducibility of data. +The PROV Ontology (PROV-O) is an OWL2 ontology for representing +provenance information according to the W3C PROV data model +[#prov-o]_ [#prov-bfo-paper]_. It provides classes, properties, and +restrictions for describing how entities, activities, and agents are +involved in the creation, transformation, usage, and management of data +and other resources [#prov-o]_. + +PROV-O captures the lifecycle of information by representing what was +generated, which activity generated it, which entities were used, and +which agents were responsible or associated with the process [#prov-o]_. +This allows systems to formally describe who created or modified a +resource, when it was created, what inputs were used, and under what +circumstances the resource was produced [#prov-o]_. + +The ontology is generic enough to support provenance modeling across +different domains, while also allowing domain-specific extensions for +specialized provenance requirements [#prov-o]_. Recent ontology-alignment +work has also explored mappings between PROV-O, Basic Formal Ontology +(BFO), and Common Core Ontologies (CCO), supporting improved semantic +interoperability and provenance integration across ontology-based systems +[#prov-bfo-paper]_. + +PROV-O is widely used in scientific workflows, data management systems, +linked data applications, digital repositories, and enterprise +information governance to support traceability, accountability, +reproducibility, data quality assessment, and policy compliance +[#prov-o]_ [#prov-bfo-paper]_. + +**Example Usage**: Annotate a dataset's provenance with PROV-O terms to +show that a data entity was generated by a specific activity, used one or +more input entities, and was associated with a responsible agent. Adding +timestamps and qualified relations makes it possible to trace how the +dataset was produced, transformed, and validated, supporting +reproducibility and data accountability [#prov-o]_. Metrics & Statistics -------------------------- @@ -137,3 +167,22 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#prov-o] Lebo, T., Sahoo, S., McGuinness, D., Belhajjame, K., + Cheney, J., Corsar, D., Garijo, D., Soiland-Reyes, S., + Zednik, S., and Zhao, J. 2013. + "PROV-O: The PROV Ontology." + W3C Recommendation, 30 April 2013. + Available at: + `https://www.w3.org/TR/prov-o/ `_ + +.. [#prov-bfo-paper] Prudhomme, T., De Colle, G., Liebers, A., + Sculley, A., Xie, P. K., Cohen, S., and Beverley, J. 2024. + "A Semantic Approach to Mapping the Provenance Ontology to + Basic Formal Ontology." + arXiv. + Available at: + `https://arxiv.org/pdf/2408.03866 `_ diff --git a/docs/source/benchmarking/general_knowledge/ro.rst b/docs/source/benchmarking/general_knowledge/ro.rst index c83b313..142e656 100644 --- a/docs/source/benchmarking/general_knowledge/ro.rst +++ b/docs/source/benchmarking/general_knowledge/ro.rst @@ -25,10 +25,32 @@ Relation Ontology (RO) ======================================================================================================== -The Relations Ontology (RO) is a comprehensive collection of formally defined OWL object properties designed for standardized representation of relationships across diverse biological ontologies. It provides a curated set of relations (part-of, derives-from, has-participant, immediately-precedes, etc.) with precise logical definitions ensuring semantic consistency across different biological knowledge domains. RO is built on foundational principles of formal ontology, ensuring that relations are unambiguous, logically sound, and applicable across multiple biological contexts. The ontology serves as the standard relation vocabulary for Open Biomedical Ontologies (OBO), enabling interoperable knowledge representation in genomics, proteomics, anatomy, and other life sciences. RO enables automated reasoning about biological entities and their complex relationships, supporting knowledge integration and discovery in biomedical research. The ontology is maintained collaboratively and has become the de facto standard for biological relationship representation in the semantic web community. - -**Example Usage**: -Define relationships in a biological ontology such as "protein A is part of complex B", "gene A has function X", or "disease A derives from mutation in gene B" using RO relations to enable automated reasoning about biological systems. +The Relation Ontology (RO) is an ontology of formally defined relations +used to standardize how relationships are represented across biological +and biomedical ontologies [#ro-home]_. Rather than modeling a complete +biological domain by itself, RO provides reusable relation terms such as +part of, has part, develops from, derives from, has participant, and +precedes [#ro-home]_. + +RO is designed to make relational statements in ontologies more precise, +consistent, and logically interpretable [#ro-home]_. These relations help +ontology developers represent spatial, temporal, developmental, causal, +and participatory connections between entities in a standardized way +[#ro-home]_. + +The ontology is widely used within the OBO ontology ecosystem as a shared +relation vocabulary for biological and biomedical ontologies [#ro-home]_. +By reusing RO relations, ontologies in domains such as anatomy, genomics, +development, disease, and biological processes can remain interoperable +and support automated reasoning across datasets and knowledge graphs +[#ro-home]_. + +**Example Usage**: Define relationships in a biological ontology, such as +a protein being part of a protein complex, an anatomical structure +developing from another structure, or a biological process having a +participant. Using RO relations makes these statements consistent across +different ontologies and supports reasoning over biological knowledge +[#ro-home]_. Metrics & Statistics -------------------------- @@ -137,3 +159,11 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#ro-home] OBO Relation Ontology. n.d. + "OBO Relation Ontology." + Available at: + `https://oborel.github.io/ `_ diff --git a/docs/source/benchmarking/general_knowledge/schemaorg.rst b/docs/source/benchmarking/general_knowledge/schemaorg.rst index dfd9944..ae0a6c2 100644 --- a/docs/source/benchmarking/general_knowledge/schemaorg.rst +++ b/docs/source/benchmarking/general_knowledge/schemaorg.rst @@ -25,10 +25,34 @@ Schema.org Ontology (SchemaOrg) ======================================================================================================== -Schema.org is a collaborative, community-driven initiative providing standardized schemas and ontologies for structured data markup across the entire web. It creates and maintains comprehensive vocabularies that enable webmasters, content creators, and developers to annotate web pages, email messages, and other digital resources with machine-readable semantic information. Schema.org covers diverse domains including products, organizations, events, publications, creative works, health and medical information, and many others. The schemas are designed for broad applicability across different industries while remaining flexible enough for domain-specific extensions. Schema.org markup is widely recognized and processed by major search engines, social media platforms, and aggregation services, directly impacting content discovery and presentation. The ontology facilitates knowledge graph construction, enhanced search results (rich snippets), and improved interoperability across web applications and services. - -**Example Usage**: -Annotate a restaurant website with Schema.org markup including Organization (restaurant details), LocalBusiness (address, phone, hours), and AggregateRating (customer reviews) to enable search engines to display rich snippets with ratings, location, and hours directly in search results. +Schema.org is a collaborative, community-driven initiative that provides +a shared vocabulary for structured data markup on the web, in email +messages, and beyond [#schema-home]_ [#schema-paper]_. It enables +webmasters, content creators, and developers to annotate digital +resources with machine-readable semantic information that can be used by +search engines and other applications [#schema-home]_. + +Schema.org provides extensible schemas for describing entities, +relationships, and actions across many domains, including people, +organizations, places, products, events, creative works, publications, +health and medical information, and other web resources [#schema-home]_. +The vocabulary can be expressed using formats such as JSON-LD, RDFa, and +Microdata, making it flexible for different publishing and data +integration scenarios [#schema-home]_ [#schema-paper]_. + +Schema.org is designed for broad applicability across industries while +also supporting extension mechanisms for more specialized use cases +[#schema-home]_. Its markup is widely used by publishers and consumed by +major web applications to support structured data exchange, enhanced +search experiences, knowledge graph construction, content discovery, and +interoperability across web-based services [#schema-paper]_. + +**Example Usage**: Annotate a restaurant website with Schema.org terms +such as Organization, LocalBusiness, address, openingHours, telephone, +and AggregateRating. This allows search engines and other applications to +understand the restaurant's location, contact details, opening hours, and +review information, supporting richer search results and improved +resource discovery [#schema-home]_ [#schema-paper]_. Metrics & Statistics -------------------------- @@ -137,3 +161,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#schema-home] Schema.org. n.d. + "Schema.org." + Available at: + `https://schema.org/ `_ + +.. [#schema-paper] Guha, R. V., Brickley, D., and Macbeth, S. 2016. + "Schema.org: Evolution of Structured Data on the Web." + *Communications of the ACM* 59(2): 44--51. + DOI: + `10.1145/2857274.2857276 `_ diff --git a/docs/source/benchmarking/general_knowledge/umbel.rst b/docs/source/benchmarking/general_knowledge/umbel.rst index b1106ca..acac843 100644 --- a/docs/source/benchmarking/general_knowledge/umbel.rst +++ b/docs/source/benchmarking/general_knowledge/umbel.rst @@ -25,9 +25,9 @@ Upper Mapping and Binding Exchange Layer Vocabulary (UMBEL) ======================================================================================================== -UMBEL (Upper Mapping and Binding Exchange Layer) is a comprehensive reference ontology and interoperability framework designed to facilitate semantic integration and data exchange across diverse domain vocabularies and datasets on the Web. It provides a broad, general-purpose reference structure of approximately 34,000 concepts organized hierarchically, serving as a semantic scaffolding to link, align, and interoperate disparate datasets, domain ontologies, and knowledge bases. UMBEL acts as a bridge layer enabling concept mappings between specialized domain vocabularies, allowing data from different sources to be semantically related and integrated. The vocabulary is designed as a base framework for constructing concept-based domain ontologies that are explicitly designed for semantic interoperability, reducing fragmentation in knowledge representation. UMBEL supports linked data applications by providing standardized concept definitions and mappings that enable automated reasoning and knowledge discovery across heterogeneous information sources. +UMBEL (Upper Mapping and Binding Exchange Layer) is a comprehensive reference ontology and interoperability framework designed to facilitate semantic integration and data exchange across diverse domain vocabularies and datasets on the Web [#umbel-github]_. It provides a broad, general-purpose reference structure of approximately 34,000 concepts organized hierarchically, serving as a semantic scaffolding to link, align, and interoperate disparate datasets, domain ontologies, and knowledge bases [#umbel-release]_. UMBEL acts as a bridge layer enabling concept mappings between specialized domain vocabularies, allowing data from different sources to be semantically related and integrated [#umbel-github]_. The vocabulary is designed as a base framework for constructing concept-based domain ontologies that are explicitly designed for semantic interoperability, reducing fragmentation in knowledge representation [#umbel-github]_. UMBEL supports linked data applications by providing standardized concept definitions and mappings that enable automated reasoning and knowledge discovery across heterogeneous information sources [#umbel-release]_. -**Example Usage**: Link concepts from domain-specific ontologies (e.g., medical terms from medical ontologies, product types from e-commerce vocabularies) to corresponding UMBEL concepts to enable cross-domain data integration and semantic reasoning. +**Example Usage**: Link concepts from domain-specific ontologies, such as medical terms from medical ontologies or product types from e-commerce vocabularies, to corresponding UMBEL concepts to enable cross-domain data integration and semantic reasoning [#umbel-github]_. Metrics & Statistics -------------------------- @@ -136,3 +136,19 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#umbel-github] Structured Dynamics. n.d. + "UMBEL: Upper Mapping and Binding Exchange Layer." + GitHub Repository. + Available at: + `https://github.com/structureddynamics/UMBEL `_ + +.. [#umbel-release] Bergman, Michael K. 2016. + "New, Major Upgrade of UMBEL Released: Version 1.50." + *AI3: Adaptive Information*. + Published May 11, 2016. + Available at: + `https://www.mkbergman.com/1946/new-major-upgrade-of-umbel-released/ `_ diff --git a/docs/source/benchmarking/geography/geo.rst b/docs/source/benchmarking/geography/geo.rst index 48be560..accc88c 100644 --- a/docs/source/benchmarking/geography/geo.rst +++ b/docs/source/benchmarking/geography/geo.rst @@ -25,9 +25,9 @@ Geographical Entities Ontology (GEO) ======================================================================================================== -The Geographical Entities Ontology (GEO) provides a comprehensive inventory and formal representation of geopolitical and geographical entities, including sovereign states, administrative subdivisions, and various geographical regions. GEO distinguishes between political entities (countries, provinces, cities) and natural geographical features (mountains, rivers, seas), enabling precise semantic representation of territorial and regional concepts. The ontology uses hierarchical relationships to model administrative subdivisions and political jurisdictions at multiple levels (national, state, regional, local), supporting complex governance structures. GEO facilitates location-aware data annotation, enabling applications in geopolitical analysis, administrative reporting, and location-based services to unambiguously identify geographic and political entities. The ontology is designed for integration with other geographic and spatial ontologies, supporting linked data applications in geography, governance, and international relations. +The Geographical Entities Ontology (GEO) provides a comprehensive inventory and formal representation of geopolitical and geographical entities, including sovereign states, administrative subdivisions, and various geographical regions [#geo-obofoundry]_ [#geo-bioportal]_. GEO distinguishes between political entities, such as countries, provinces, and cities, and the territorial or regional entities associated with them, enabling precise semantic representation of geographic and geopolitical concepts [#geo-obofoundry]_. The ontology uses hierarchical relationships to model administrative subdivisions and political jurisdictions at multiple levels, including national, state, regional, and local levels [#geo-obofoundry]_ [#geo-bioportal]_. GEO facilitates location-aware data annotation, enabling applications in geopolitical analysis, administrative reporting, and location-based services to unambiguously identify geographic and political entities [#geo-obofoundry]_. The ontology is designed for integration with other geographic and spatial ontologies, supporting linked data applications in geography, governance, and international relations [#geo-bioportal]_. -**Example Usage**: Annotate a political news article with GEO terms such as "France" (sovereign state) linked to its subdivisions "Île-de-France" and "Paris" (administrative entities) to enable geographic and political context discovery. +**Example Usage**: Annotate a political news article with GEO terms such as ``France`` as a sovereign state, linked to subdivisions such as ``Île-de-France`` and ``Paris`` as administrative entities, to enable geographic and political context discovery [#geo-obofoundry]_ [#geo-bioportal]_. Metrics & Statistics -------------------------- @@ -136,3 +136,16 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#geo-obofoundry] OBO Foundry. n.d. + "Geographical Entity Ontology." + Available at: + `https://obofoundry.org/ontology/geo.html `_ + +.. [#geo-bioportal] NCBO BioPortal. 2019. + "Geographical Entity Ontology." + Available at: + `https://bioportal.bioontology.org/ontologies/GEO `_ diff --git a/docs/source/benchmarking/geography/geonames.rst b/docs/source/benchmarking/geography/geonames.rst index e05085f..df11513 100644 --- a/docs/source/benchmarking/geography/geonames.rst +++ b/docs/source/benchmarking/geography/geonames.rst @@ -23,10 +23,10 @@ GeoNames Ontology (GeoNames) ======================================================================================================== -The GeoNames Ontology provides a structured vocabulary and semantic framework for describing geographical features, places, and administrative regions, particularly those defined in the geonames.org database. It enables the representation of place names, geographic coordinates, feature types (such as cities, rivers, mountains), and hierarchical relationships between locations (e.g., country, state, city). The ontology supports multilingual place names and alternative spellings, facilitating global interoperability and data integration. GeoNames is widely used in geographic information systems (GIS), linked data applications, and knowledge graphs to enable geospatial search, mapping, and data enrichment. By providing standardized identifiers and relationships, GeoNames enhances the discoverability and linking of geographic data across datasets and platforms. The ontology is maintained collaboratively and is continuously updated to reflect changes in geographic information and administrative boundaries. +The GeoNames Ontology provides a structured vocabulary and semantic framework for describing geographical features, places, and administrative regions, particularly those represented in the GeoNames geographical dataset [#geonames-ontology]_ [#geonames-semantic-schema]_. It enables the representation of place names, geographic coordinates, feature types such as cities, rivers, and mountains, and semantic relationships between geographical entities [#geonames-ontology]_. The ontology supports semantic interoperability in geospatial applications by providing a structured way to describe geographical concepts, attributes, and relations [#geonames-semantic-schema]_. GeoNames is widely used in geographic information systems, linked data applications, and knowledge graphs to enable geospatial search, mapping, and data enrichment [#geonames-ontology]_ [#geonames-semantic-schema]_. By providing standardized identifiers, geographic coordinates, feature classifications, and semantic relationships, GeoNames enhances the discoverability and linking of geographic data across datasets and platforms [#geonames-ontology]_. The semantic schema proposed for GeoNames also helps improve data quality by defining constraints on the domain and range of attributes and relations, such as population, altitude, area, and capital relations [#geonames-semantic-schema]_. **Example Usage**: -Annotate a dataset of cultural heritage sites with GeoNames terms to specify their geographic coordinates, administrative regions, and feature types, enabling geospatial search and integration with mapping services. +Annotate a dataset of cultural heritage sites with GeoNames terms to specify their geographic coordinates, administrative regions, and feature types, enabling geospatial search and integration with mapping services [#geonames-ontology]_ [#geonames-semantic-schema]_. Metrics & Statistics -------------------------- @@ -135,3 +135,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#geonames-ontology] GeoNames. n.d. + "GeoNames Ontology." + Available at: + `https://www.geonames.org/ontology `_ + +.. [#geonames-semantic-schema] Maltese, Vincenzo, and Feroz Farazi. 2013. + "A Semantic Schema for GeoNames." + INSPIRE Conference 2013. + Available at: + `https://www.researchgate.net/publication/267325484_A_semantic_schema_for_GeoNames `_ diff --git a/docs/source/benchmarking/geography/gts.rst b/docs/source/benchmarking/geography/gts.rst index f8e8b11..d485897 100644 --- a/docs/source/benchmarking/geography/gts.rst +++ b/docs/source/benchmarking/geography/gts.rst @@ -23,10 +23,10 @@ Geologic Timescale model (GTS) ======================================================================================================== -The Geologic Timescale (GTS) is an RDF/OWL ontology representation of the standard geologic timescale model, adapted from the GeoSciML framework and compatible with geospatial information transfer standards. It provides a formal semantic model for representing geological time periods, epochs, eons, and their boundaries based on the Global Stratotype Section and Point (GSSP) framework established by the International Commission on Stratigraphy. GTS enables precise temporal annotation of geological data, enabling scientists to associate geological observations, samples, and events with specific time periods in Earth's history. The ontology supports hierarchical relationships between time divisions, enabling both broad geological age classification and detailed temporal analysis. GTS facilitates integration of paleontological, stratigraphic, and geological survey data across diverse research institutions and databases. +The Geologic Timescale (GTS) is an RDF/OWL ontology representation of the standard geologic timescale model, adapted from the GeoSciML framework and compatible with geospatial information transfer standards [#gts-ontology]_ [#gts-cox-richard-2015]_. It provides a formal semantic model for representing geological time periods, epochs, eons, stages, and their boundaries [#gts-cox-richard-2015]_. GTS enables precise temporal annotation of geological data, allowing scientists to associate geological observations, samples, fossil records, stratigraphic units, and events with specific intervals in Earth's history [#gts-ontology]_. The ontology supports hierarchical relationships between geological time divisions, enabling both broad geological age classification and detailed temporal analysis [#gts-ontology]_ [#gts-cox-richard-2015]_. GTS facilitates integration of paleontological, stratigraphic, and geological survey data across diverse research institutions, databases, and linked data systems [#gts-cox-richard-2015]_. **Example Usage**: -Annotate a rock sample or fossil record with GTS terms such as "Cretaceous" (era), "Campanian" (stage), or specific GSSP boundary ages to enable temporal querying and stratigraphic correlation. +Annotate a rock sample or fossil record with GTS terms such as ``Cretaceous``, ``Campanian``, or specific geologic time boundary information to enable temporal querying, stratigraphic correlation, and integration with geological survey datasets [#gts-ontology]_ [#gts-cox-richard-2015]_. Metrics & Statistics -------------------------- @@ -135,3 +135,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#gts-ontology] CGI-IUGS. n.d. + "Geologic Timescale Ontology." + GitHub Repository. + Available at: + `https://github.com/CGI-IUGS/timescale-ont `_ + +.. [#gts-cox-richard-2015] Cox, Simon J. D., and Stephen M. Richard. 2015. + "A Geologic Timescale Ontology and Service." + Available at: + `https://publications.csiro.au/publications/publication/PIcsiro:EP14855 `_ diff --git a/docs/source/benchmarking/geography/juso.rst b/docs/source/benchmarking/geography/juso.rst index 3304169..0f4cae6 100644 --- a/docs/source/benchmarking/geography/juso.rst +++ b/docs/source/benchmarking/geography/juso.rst @@ -25,10 +25,9 @@ Juso Ontology (Juso) ======================================================================================================== -The Juso Ontology is a comprehensive Web vocabulary for describing and classifying geographical addresses, locations, and geographical features with machine-readable semantic annotations. It provides a structured framework for representing address components including postal codes, street names, building numbers, and administrative hierarchies, enabling standardized address representation across diverse geographic regions. Juso supports multiple address formats and conventions, accommodating international addressing systems and local geographic naming practices. The ontology facilitates geocoding applications, location-based services, and geographic data integration by providing unambiguous semantic definitions of address components and spatial relationships. Juso integrates with broader geographic ontologies (GEO, GeoNames) to link address information with geographic entities and spatial contexts. - -**Example Usage**: Represent a complete address as a Juso address instance with properties for street name, building number, postal code, city, and country, enabling automated address validation and geographic lookup services. +The Juso Ontology is a Web vocabulary for describing geographical addresses and geographical features using machine-readable semantic annotations [#juso-ontology]_ [#juso-github]_. It provides a structured framework for representing core geographic and address-related concepts, including spatial things, features, geometries, points, addresses, names, official names, alternate names, and containment relationships [#juso-github]_. Juso also defines address components such as full address, country, postal code, and multiple levels of administrative divisions, enabling more standardized representation of addresses across geographic datasets [#juso-github]_. The ontology supports semantic description of political and administrative divisions, including countries, provinces, counties, municipalities, districts, towns, townships, neighborhoods, villages, and related regional units [#juso-github]_. By providing explicit classes and properties for addresses, geographic features, and administrative hierarchies, Juso can support geocoding, location-based services, address validation, geographic lookup, and linked data integration [#juso-ontology]_ [#juso-github]_. Juso can also be connected with broader geographic and web vocabularies through linked-data relationships, helping address information interoperate with geographic entities and spatial contexts [#juso-github]_. +**Example Usage**: Represent a complete address as a Juso address instance with properties for street name, building number, postal code, city, administrative division, and country, enabling automated address validation, geographic lookup, and integration with location-based services [#juso-ontology]_ [#juso-github]_. Metrics & Statistics -------------------------- @@ -136,3 +135,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#juso-ontology] Juso Ontology. n.d. + "Juso Ontology." + Available at: + `https://rdfs.co/juso/latest/html `_ + +.. [#juso-github] Juso Ontology Contributors. n.d. + "Juso Ontology." + GitHub Repository. + Available at: + `https://github.com/listinc/juso-ontology `_ diff --git a/docs/source/benchmarking/industry/auto.rst b/docs/source/benchmarking/industry/auto.rst index 0691752..f39939d 100644 --- a/docs/source/benchmarking/industry/auto.rst +++ b/docs/source/benchmarking/industry/auto.rst @@ -23,10 +23,10 @@ Automotive Ontology (AUTO) ======================================================================================================== -The Automotive Ontology (AUTO) is a comprehensive OWL ontology that defines shared conceptual structures and relationships in the automotive industry. It provides a standardized vocabulary for describing vehicles, components, systems, manufacturing processes, and regulatory requirements. AUTO is built upon the auto schema.org extension created by the W3C Automotive Ontology Community Group and follows best practices established by the EDM Council's Financial Industry Business Ontology (FIBO) Community. The ontology supports interoperability between automotive data sources, enabling integration of information from manufacturers, suppliers, service providers, and regulatory bodies. AUTO facilitates advanced applications such as digital twins, predictive maintenance, supply chain optimization, and regulatory compliance. By providing a common semantic framework, AUTO enables automated reasoning, data analytics, and knowledge sharing across the automotive ecosystem. +The Automotive Ontology (AUTO) is a comprehensive OWL ontology that defines shared conceptual structures and relationships in the automotive industry [#auto-edmc]_ [#auto-github]_. It provides a standardized semantic framework for representing automotive concepts and supporting interoperability across automotive data sources [#auto-edmc]_. AUTO is built upon the ``auto`` Schema.org extension created by the W3C Automotive Ontology Community Group [#auto-github]_. Its development process follows best practices established by the EDMC FIBO Community, supporting consistent ontology modeling and reusable conceptual design [#auto-github]_. The ontology supports integration of information from automotive manufacturers, suppliers, dealers, service providers, and other automotive data publishers [#auto-edmc]_ [#auto-github]_. By providing a common semantic framework, AUTO enables automotive data integration, knowledge sharing, semantic reasoning, and interoperability across the automotive ecosystem [#auto-edmc]_. **Example Usage**: -Annotate a vehicle information system with AUTO terms to describe vehicle models, engine types, safety features, and maintenance schedules, enabling integration with manufacturer databases and regulatory reporting systems. +Annotate a vehicle information system with AUTO terms to describe vehicle models, vehicle types, automotive characteristics, and related industry concepts, enabling integration with manufacturer databases, dealer websites, automotive knowledge graphs, and downstream analytics systems [#auto-edmc]_ [#auto-github]_. Metrics & Statistics -------------------------- @@ -135,3 +135,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#auto-edmc] EDM Council. n.d. + "AUTO - Automotive." + Available at: + `https://edmcouncil.org/frameworks/industry-models/auto/ `_ + +.. [#auto-github] EDM Council. n.d. + "Automotive Ontology (AUTO)." + GitHub Repository. + Available at: + `https://github.com/edmcouncil/auto `_ diff --git a/docs/source/benchmarking/industry/dbo.rst b/docs/source/benchmarking/industry/dbo.rst index 3055cb7..adbe6e1 100644 --- a/docs/source/benchmarking/industry/dbo.rst +++ b/docs/source/benchmarking/industry/dbo.rst @@ -23,10 +23,10 @@ Digital Buildings Ontology (DBO) ======================================================================================================== -The Digital Buildings Ontology (DBO) is a structured vocabulary developed by Google for representing information about buildings and building-installed equipment. DBO provides a semantic model for describing building assets, their locations, types, operational states, and relationships, supporting digital twins and smart building applications. The ontology enables integration of data from building management systems, IoT devices, and facility management platforms, facilitating automated monitoring, control, and analytics. DBO is designed to be extensible and interoperable, allowing organizations to adapt the ontology to their specific building types and operational requirements. By providing standardized terms and relationships, DBO supports data-driven decision-making, energy optimization, and predictive maintenance in digital buildings. The ontology is open source and maintained by a community of contributors, ensuring ongoing development and alignment with industry needs. +The Digital Buildings Ontology (DBO) is a structured vocabulary developed by Google for representing information about buildings and building-installed equipment [#dbo-github]_. DBO provides a semantic model for describing building assets, physical spaces, equipment, entity types, operational states, fields, units, and relationships in smart-building environments [#dbo-github]_. The ontology enables integration of data from building management systems, IoT devices, telemetry streams, and facility management platforms by providing a uniform schema for building configuration and validation [#dbo-github]_. DBO is designed to be extensible, allowing organizations to adapt the ontology to their specific building types, equipment configurations, and operational requirements [#dbo-github]_. By providing standardized terms and relationships, DBO supports data-driven building management, monitoring, analytics, energy optimization, predictive maintenance, and scalable digital building operations [#dbo-github]_. The ontology is open source and maintained through Google’s Digital Buildings repository [#dbo-github]_. **Example Usage**: -Annotate a smart building system with DBO terms to describe HVAC equipment, lighting systems, sensors, and their spatial locations, enabling automated control and integration with building management platforms. +Annotate a smart building system with DBO terms to describe HVAC equipment, lighting systems, sensors, telemetry fields, operational states, and spatial locations, enabling automated validation, monitoring, control, and integration with building management platforms [#dbo-github]_. Metrics & Statistics -------------------------- @@ -135,3 +135,12 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#dbo-github] Google. n.d. + "Digital Buildings." + GitHub Repository. + Available at: + `https://github.com/google/digitalbuildings `_ diff --git a/docs/source/benchmarking/industry/doap.rst b/docs/source/benchmarking/industry/doap.rst index f7eee1a..30883ee 100644 --- a/docs/source/benchmarking/industry/doap.rst +++ b/docs/source/benchmarking/industry/doap.rst @@ -25,9 +25,9 @@ The Description of a Project vocabulary (DOAP) ======================================================================================================== -The Description of a Project (DOAP) vocabulary is an RDF/OWL-based ontology for machine-readable description of software projects, particularly open source initiatives. It models core project entities such as Project, Person, Revision, Repository, and License, capturing essential metadata about software development and distribution. DOAP enables representation of project attributes including name, description, homepage, version control systems (Git, SVN), issue tracking systems, programming languages, release history, and developer/maintainer information. The vocabulary facilitates integration of project data across diverse repositories, forges, and development platforms, supporting automated project discovery, dependency analysis, and ecosystem mapping. DOAP enables research on software development practices, project evolution, and open source community dynamics through structured, interoperable metadata. +The Description of a Project (DOAP) vocabulary is an RDF/OWL-based ontology for machine-readable description of software projects, particularly open source initiatives [#doap-github]_. It models core project entities such as ``Project``, ``Person``, ``Revision``, ``Repository``, and ``License``, capturing essential metadata about software development and distribution [#doap-github]_. DOAP enables representation of project attributes including name, description, homepage, version control systems, issue tracking systems, programming languages, release history, and developer or maintainer information [#doap-github]_. The vocabulary facilitates integration of project data across diverse repositories, forges, and development platforms, supporting automated project discovery, dependency analysis, and ecosystem mapping [#doap-github]_. DOAP enables research on software development practices, project evolution, and open source community dynamics through structured, interoperable metadata [#doap-github]_. -**Example Usage**: Describe an open source project with DOAP properties like foaf:name (project name), doap:repository (code repository URL), doap:programming-language (Python), doap:maintainer (developer agent), and doap:license (CC0 or Apache 2.0). +**Example Usage**: Describe an open source project with DOAP properties such as ``foaf:name`` for the project name, ``doap:repository`` for the code repository URL, ``doap:programming-language`` for the implementation language, ``doap:maintainer`` for the developer or maintainer agent, and ``doap:license`` for the project license [#doap-github]_. Metrics & Statistics -------------------------- @@ -136,3 +136,12 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#doap-github] DOAP Contributors. n.d. + "DOAP: Description Of A Project." + GitHub Repository. + Available at: + `https://github.com/ewilderj/doap `_ diff --git a/docs/source/benchmarking/industry/iof.rst b/docs/source/benchmarking/industry/iof.rst index d39b526..eb7e8ab 100644 --- a/docs/source/benchmarking/industry/iof.rst +++ b/docs/source/benchmarking/industry/iof.rst @@ -23,10 +23,10 @@ Industrial Ontology Foundry (IOF) ======================================================================================================== -The Industrial Ontology Foundry (IOF) Core Ontology is a foundational ontology for the manufacturing industry, capturing concepts and relationships common across multiple manufacturing domains. It is implemented in RDF and leverages the Basic Formal Ontology (BFO) as its upper-level framework, while also incorporating terms from other domain-independent and mid-level ontologies. IOF provides a standardized vocabulary for describing manufacturing processes, equipment, materials, products, and organizational structures. The ontology is designed to ensure consistency and interoperability across various domain-specific reference ontologies published by the IOF, supporting integration of manufacturing data from diverse sources. IOF enables advanced applications such as digital twins, smart manufacturing, supply chain optimization, and industrial automation. By providing a common semantic foundation, IOF facilitates data sharing, analytics, and knowledge management in the manufacturing sector. +The Industrial Ontology Foundry (IOF) Core Ontology is a foundational ontology for the manufacturing industry, capturing concepts and relationships common across multiple manufacturing domains [#iof-github]_ [#iof-core-paper]_. It is implemented in RDF/OWL and leverages the Basic Formal Ontology (BFO) as its upper-level framework, while also incorporating terms from other domain-independent and mid-level ontologies [#iof-github]_. IOF Core provides a standardized vocabulary for describing manufacturing-related concepts that support cross-system integration within factories, across enterprises, between suppliers and manufacturers, and throughout the product life cycle [#iof-github]_ [#iof-core-paper]_. The ontology is designed to ensure consistency and interoperability across domain-specific reference ontologies published by the IOF [#iof-github]_. IOF Core supports advanced applications such as smart manufacturing, industrial knowledge graphs, supply chain modeling, digital twins, industrial automation, data sharing, analytics, and knowledge management in the manufacturing sector [#iof-core-paper]_. **Example Usage**: -Annotate a smart factory system with IOF terms to describe production lines, machines, materials, and process steps, enabling integration with enterprise resource planning (ERP) and manufacturing execution systems (MES). +Annotate a smart factory system with IOF Core terms to describe production lines, machines, materials, process steps, and organizational relationships, enabling integration with enterprise resource planning (ERP), manufacturing execution systems (MES), supply chain systems, and industrial knowledge graphs [#iof-github]_ [#iof-core-paper]_. Metrics & Statistics -------------------------- @@ -135,3 +135,18 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#iof-github] Industrial Ontologies Foundry. n.d. + "Industrial Ontologies Foundry." + GitHub Repository. + Available at: + `https://github.com/iofoundry/ontology `_ + +.. [#iof-core-paper] Kulvatunyou, Boonserm, Milos Drobnjakovic, Farhad Ameri, Chris Will, and Barry Smith. 2022. + "The Industrial Ontologies Foundry (IOF) Core Ontology." + *Formal Ontologies Meet Industry (FOMI) 2022*. + Available at: + `https://www.nist.gov/publications/industrial-ontologies-foundry-iof-core-ontology `_ diff --git a/docs/source/benchmarking/industry/pto.rst b/docs/source/benchmarking/industry/pto.rst index 1c0c772..d7c3bfb 100644 --- a/docs/source/benchmarking/industry/pto.rst +++ b/docs/source/benchmarking/industry/pto.rst @@ -25,9 +25,9 @@ Product Types Ontology (PTO) ======================================================================================================== -The Product Types Ontology (PTO) is a comprehensive vocabulary for standardized classification and semantic description of commercial products and services designed to complement GoodRelations e-commerce vocabulary. PTO provides a hierarchical taxonomy of product categories, types, and subtypes covering diverse industries and market segments including consumer goods, electronics, fashion, books, and services. It enables detailed product classification through a fine-grained type hierarchy, facilitating product discovery, comparison shopping, and automated product recommendation systems. PTO is designed for web markup integration using microdata, RDFa, and JSON-LD formats, enabling product information embedded in HTML to be processed by search engines and aggregation platforms. By combining PTO product types with GoodRelations commercial properties (price, availability, shipping), organizations can create rich, machine-readable product descriptions for e-commerce applications. +The Product Types Ontology (PTO) is a vocabulary for standardized classification and semantic description of commercial products and services, designed to complement GoodRelations in semantic e-commerce applications [#pto-w3c]_ [#goodrelations-paper]_. PTO provides product and service type identifiers that can be used to describe what kind of product or service is being offered, while GoodRelations provides the commercial vocabulary for describing offers, prices, sellers, availability, and related e-commerce information [#pto-w3c]_ [#goodrelations-paper]_. PTO enables detailed product classification across diverse market segments, facilitating product discovery, comparison shopping, semantic search, and automated recommendation systems [#pto-w3c]_. The ontology can be used in web markup and linked data settings to make product information more machine-readable and easier for search engines, aggregation platforms, and e-commerce applications to process [#pto-w3c]_. By combining PTO product types with GoodRelations commercial properties such as price, availability, business entity, and offer information, organizations can create rich, machine-readable product descriptions for e-commerce applications [#goodrelations-paper]_. -**Example Usage**: Annotate a product listing with PTO terms such as "Electronics > Smartphones > Android Phones" linked to GoodRelations Offering instances with price and availability information for automated product discovery and price comparison. +**Example Usage**: Annotate a product listing with a PTO product type, such as ``Smartphone`` or ``Android_phone``, and link it to a GoodRelations ``Offering`` that describes the seller, price, availability, and delivery conditions. This enables automated product discovery, semantic search, and price comparison across e-commerce platforms [#pto-w3c]_ [#goodrelations-paper]_. Metrics & Statistics -------------------------- @@ -136,3 +136,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#pto-w3c] W3C Semantic Web Wiki. n.d. + "Productontology." + Available at: + `https://www.w3.org/2001/sw/wiki/Productontology `_ + +.. [#goodrelations-paper] Hepp, Martin. 2008. + "GoodRelations: An Ontology for Describing Products and Services Offers on the Web." + *Knowledge Engineering: Practice and Patterns*, EKAW 2008. + DOI: + `10.1007/978-3-540-87696-0_29 `_ diff --git a/docs/source/benchmarking/industry/tubes.rst b/docs/source/benchmarking/industry/tubes.rst index d4682b3..85f126c 100644 --- a/docs/source/benchmarking/industry/tubes.rst +++ b/docs/source/benchmarking/industry/tubes.rst @@ -23,10 +23,10 @@ TUBES System Ontology (TUBES) ======================================================================================================== -The TUBES System Ontology (TSO) is a domain ontology for the Architecture, Engineering, Construction, and Operations (AECO) industry, explicitly defining interconnected building service systems, their hierarchical subdivisions, structural and functional aspects, and links to spatial entities. TSO supports the semantic representation of building services such as HVAC, plumbing, and electrical systems, enabling detailed modeling of their components, relationships, and operational characteristics. The ontology is designed to facilitate the integration of building information across design, construction, and facility management processes, supporting digital twins and smart building applications. TSO aligns with other W3C community ontologies to ensure interoperability and data exchange in the semantic web of building data. By providing a standardized vocabulary, TSO enables automated reasoning, compliance checking, and lifecycle management of building services. The ontology is actively developed and adopted by industry stakeholders for advanced building information modeling (BIM) and digital transformation initiatives. +The TUBES System Ontology (TSO) is a domain ontology for the Architecture, Engineering, Construction, and Operations (AECO) industry, explicitly defining interconnected building service systems, their hierarchical subdivisions, structural and functional aspects, and links to spatial entities [#tso-github]_ [#tso-paper]_. TSO supports the semantic representation of building service systems such as HVAC, mechanical, plumbing, electrical, and data systems, enabling detailed modeling of their components, relationships, and operational characteristics [#tso-paper]_. The ontology is designed to facilitate the integration of building information across design, construction, and facility management processes by making building service system information machine-readable and linkable [#tso-github]_ [#tso-paper]_. TSO aligns with other W3C community ontologies to support interoperability and data exchange in the semantic web of building data [#tso-github]_. By providing a standardized vocabulary for technical systems and their relationships to spatial structures, TSO supports automated analysis, reasoning, lifecycle management, and integration of building service information across AECO workflows [#tso-github]_ [#tso-paper]_. **Example Usage**: -Annotate a BIM model with TUBES terms to describe the HVAC system, its components (air handling units, ducts, sensors), their spatial locations, and operational relationships, enabling automated analysis and integration with facility management systems. +Annotate a BIM model with TUBES terms to describe an HVAC system, its components such as air handling units, ducts, sensors, their spatial locations, and operational relationships, enabling automated analysis and integration with facility management systems [#tso-github]_ [#tso-paper]_. Metrics & Statistics -------------------------- @@ -135,3 +135,19 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#tso-github] RWTH-E3D. n.d. + "TUBES System Ontology." + GitHub Repository. + Available at: + `https://github.com/RWTH-E3D/tso `_ + +.. [#tso-paper] Pauen, Nadine, Daniel Schlütter, Jacek Siwiecki, + Julian Frisch, and Christoph van Treeck. 2021. + "TUBES System Ontology: Digitalization of Building Service Systems." + *Linked Data in Architecture and Construction 2021*. + Available at: + `https://linkedbuildingdata.net/ldac2021/files/papers/CIB_W78_2021_paper_115.pdf `_ diff --git a/docs/source/benchmarking/law/copyrightonto.rst b/docs/source/benchmarking/law/copyrightonto.rst index 31d8d3a..1134a38 100644 --- a/docs/source/benchmarking/law/copyrightonto.rst +++ b/docs/source/benchmarking/law/copyrightonto.rst @@ -25,13 +25,34 @@ Copyright Ontology (CopyrightOnto) ======================================================================================================== -The Copyright Ontology formalizes the copyright domain to support automated and computer-assisted management of rights, permissions, and obligations across the entire content value chain rather than focusing solely on end-user permissions. It models core legal and business entities such as Works, Authors/Rightsholders, Rights and Permissions, Licenses, Contracts, Transactions, Agents, and Provenance. The ontology captures temporal and jurisdictional dimensions of rights (grant periods, territorial scopes), the lifecycle of rights transfers and assignments, and links between digital artefacts and their legal metadata. - -Key characteristics include an emphasis on provenance and traceability (who granted which right, when, and under what conditions), expressivity for different permission types (reproduction, distribution, modification), and support for linking to external vocabularies (e.g., dcterms for metadata, FOAF/ORCID for agents, and license URIs such as Creative Commons). Its structure is suitable for encoding both normative legal information and operational licensing metadata used in publishing platforms, rights registries, and DRM-aware systems. - -Typical applications are automated rights clearance, license management, tracking provenance of digital content, and enabling marketplaces and repositories to reason about reuse conditions. - -**Example Usage**: describe a digital artwork as a CopyrightOnto Work with associated Rights statements linking to a License URI, a Rightsholder Agent, and validity dates to support automated permission checks. +The Copyright Ontology (CopyrightOnto) is an OWL/RDF ontology for +representing the copyright domain in a machine-processable way in order +to facilitate automated or computer-supported copyright management +through the whole content value chain, rather than focusing only on +end-user permissions [#copyright-repo]_ [#copyright-paper]_. The project +describes the ontology as a Web Ontology implemented using W3C standards +such as RDF and OWL, and organizes the domain into three main parts: +the **Creation Model**, **Rights Model**, and **Actions Model** +[#copyright-repo]_. These models are intended to capture how creations +appear across their lifecycle, which actions can be performed on them, +and which legal rights or constraints regulate those actions +[#copyright-repo]_ [#copyright-jis]_. + +The ontology has been presented in the context of semantic digital +rights management and copyright-aware copyright management systems, +including support for interoperable licensing and contract-related +representations [#copyright-paper]_ [#copyright-contracts]_. This makes +it suitable for applications such as automated rights clearance, license +management, provenance-aware content workflows, and machine-readable +reuse analysis in publishing, repositories, and digital content +platforms [#copyright-joir]_ [#copyright-jis]_. + +**Example Usage**: Describe a digital artwork as a CopyrightOnto +creation/work entity, associate it with a rightsholder, connect it to +the relevant rights or license statements, and attach temporal or legal +constraints so that downstream systems can perform automated permission +checks and copyright-aware reuse analysis [#copyright-repo]_ +[#copyright-joir]_. Metrics & Statistics -------------------------- @@ -140,3 +161,16 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#copyright-repo] Rhizomik. n.d. "CopyrightOnto - Copyright Ontology." + GitHub repository. + Available at: + `https://github.com/rhizomik/copyrightonto `_ + +.. [#copyright-jis] García, R., Celma, Ò., and Gil, R. 2009. + "Content Value Chains Modelling using a Copyright Ontology." + Available at: + `https://rhizomik.net/html/~roberto/papers/rg-jis09.pdf `_ diff --git a/docs/source/benchmarking/library_and_cultural_heritage/gnd.rst b/docs/source/benchmarking/library_and_cultural_heritage/gnd.rst index 6186fbc..44bafed 100644 --- a/docs/source/benchmarking/library_and_cultural_heritage/gnd.rst +++ b/docs/source/benchmarking/library_and_cultural_heritage/gnd.rst @@ -26,9 +26,9 @@ Gemeinsame Normdatei (GND) ======================================================================================================== -The Gemeinsame Normdatei (GND, Integrated Authority File) is a comprehensive semantic vocabulary and linked data resource developed by the German library and information community for describing and disambiguating authority data. GND provides standardized, machine-readable descriptions of persons, organizations, geographic locations, corporate bodies, and other entities to solve name ambiguity problems in library catalogs and information systems. The ontology offers a rich set of properties and relationships for describing biographical data, organizational hierarchies, place names, and subject matter authorities. GND is widely used in German-speaking libraries, archives, and cultural heritage institutions for authority control and semantic data linking. The vocabulary integrates with broader linked data ecosystems and international authority systems (VIAF, LCSH) to enable cross-institutional data integration and discovery. +The Gemeinsame Normdatei (GND, Integrated Authority File) is a comprehensive semantic vocabulary and linked data resource developed by the German library and information community for describing and disambiguating authority data [#gnd-dnb]_ [#gnd-ontology]_. GND provides standardized, machine-readable descriptions of entities such as persons, corporate bodies, conferences and events, geographic entities, topics, and works relating to cultural and academic collections [#gnd-dnb]_. The GND Ontology defines RDF classes and relations used to describe Integrated Authority File data in RDF format, enabling authority data to be represented in semantic web and linked data environments [#gnd-ontology]_. GND offers rich properties and relationships for describing names, biographical information, organizational relationships, geographic entities, subject authorities, and links to related resources [#gnd-ontology]_. It is widely used by libraries and is increasingly relevant for archives, museums, and cultural heritage institutions for authority control, entity identification, and semantic data linking [#gnd-dnb]_. The vocabulary supports integration with broader linked data ecosystems by enabling GND entities to be linked with other authority files and external knowledge resources [#gnd-ontology]_. -**Example Usage**: Link a library catalog record to GND authority entries for the author (as person), publisher (as corporate body), and subject headings (as topic authorities) to enable semantic discovery and disambiguation across German and international library systems. +**Example Usage**: Link a library catalog record to GND authority entries for the author as a person, the publisher as a corporate body, and subject headings as topic authorities. This enables semantic discovery, entity disambiguation, and cross-institutional linking across German and international library systems [#gnd-dnb]_ [#gnd-ontology]_. Metrics & Statistics -------------------------- @@ -137,3 +137,16 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#gnd-dnb] German National Library. n.d. + "The Integrated Authority File (GND)." + Available at: + `https://www.dnb.de/EN/Professionell/Standardisierung/GND/gnd_node.html `_ + +.. [#gnd-ontology] German National Library. n.d. + "RDF Vocabularies: GND Ontology." + Available at: + `https://www.dnb.de/EN/Professionell/Metadatendienste/Exportformate/RDF-Vokabulare/rdf_node.html `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/amontology.rst b/docs/source/benchmarking/materials_science_and_engineering/amontology.rst index e3663f3..c1d2135 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/amontology.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/amontology.rst @@ -22,11 +22,10 @@ Additive Manufacturing Ontology (AMOntology) ======================================================================================================== - -The Additive Manufacturing Ontology (AMOntology) is a domain ontology developed to represent knowledge about additive manufacturing (AM) processes, computational models, and their characteristics. It is structured around two main components: AMProcessOntology, which captures entities and relationships relevant to AM processes, and ModelOntology, which describes modeling concepts for multi-physics, multi-scale simulations. AMOntology integrates these components to provide a comprehensive framework for describing process parameters, material properties, equipment, and computational models in AM. The ontology supports semantic annotation of digital manufacturing workflows, enabling data integration, process optimization, and knowledge sharing across research and industry. By providing a standardized vocabulary, AMOntology facilitates interoperability between digital manufacturing systems, simulation tools, and research databases. The ontology is actively maintained and extended to support new developments in additive manufacturing and computational modeling. +The **Additive Manufacturing Ontology (AMOntology)** is a domain ontology developed to represent knowledge about additive manufacturing (AM) processes, computational models, and their characteristics [#amontology-github]_. It is structured around two main components: **AMProcessOntology**, which captures entities and relationships relevant to AM processes, and **ModelOntology**, which describes modeling concepts for potentially multi-physics and multi-scale processes [#amontology-github]_. AMOntology integrates these components to provide a framework for describing AM process knowledge, model characteristics, assumptions, approximations, and qualitative indicators of model fidelity [#amontology-github]_. It supports semantic annotation and knowledge integration across additive manufacturing workflows, helping improve interoperability, reuse, and sharing of AM information across research and digital manufacturing environments [#ali2019]_ [#amontology-github]_. **Example Usage**: -Annotate an additive manufacturing workflow with AMOntology terms to specify process parameters (e.g., laser power, layer thickness), computational model characteristics, and material properties, enabling semantic search and integration with digital manufacturing platforms. +Annotate an additive manufacturing workflow with AMOntology terms to specify process parameters, material properties, equipment information, and computational model characteristics, enabling semantic search, data integration, and comparison of AM knowledge across digital manufacturing platforms [#ali2019]_ [#amontology-github]_. Metrics & Statistics -------------------------- @@ -135,3 +134,19 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#ali2019] Mohd Ali, M., Rai, R., Otte, J. N., and Smith, B. 2019. + "A product life cycle ontology for additive manufacturing." + *Computers in Industry*, 105, 191--203. + DOI: 10.1016/j.compind.2018.12.007. + Available at: + `https://www.sciencedirect.com/science/article/pii/S0166361518301647 `_ + +.. [#amontology-github] Assouroko, I., Witherell, P., Lopez, F., and contributors. n.d. + "AMontology: NIST's OWL ontology of additive manufacturing." + GitHub repository. + Available at: + `https://github.com/iassouroko/AMontology `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/asmo.rst b/docs/source/benchmarking/materials_science_and_engineering/asmo.rst index f3d7e90..96c8f8a 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/asmo.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/asmo.rst @@ -25,9 +25,14 @@ Atomistic Simulation Methods Ontology (ASMO) ======================================================================================================== -ASMO is a comprehensive ontology that formalizes concepts and relationships needed to describe and classify atomic-scale computational simulation methods used in materials science and computational chemistry. It provides structured vocabulary for representing commonly used simulation methodologies including density functional theory (DFT), molecular dynamics (MD), Monte Carlo (MC) methods, and other first-principles and statistical mechanics techniques. ASMO captures essential properties of simulation methods such as theoretical foundations, computational complexity, applicable material systems, and resulting properties that can be predicted. The ontology uses the Provenance Ontology (PROV-O) to formally describe simulation processes, enabling tracking of computational workflows, software tools, and input parameters used in materials modeling. ASMO facilitates reproducibility and interoperability in computational materials science by providing standardized semantic representations of simulation methods and their parameters. +The **Atomistic Simulation Methods Ontology (ASMO)** is a domain ontology that formalizes concepts and relationships used to describe and classify atomic-scale computational simulation methods in materials science and computational chemistry [#asmo-doc]_ [#asmo-github]_. It provides a structured vocabulary for representing common simulation methodologies, including density functional theory (DFT), molecular dynamics (MD), Monte Carlo (MC) methods, molecular statics, and ab initio molecular dynamics [#asmo-doc]_. -**Example Usage**: Annotate a materials database entry with ASMO terms for the simulation method (e.g., DFT with specific functional), system size (number of atoms), computational details (basis sets, k-point meshes), and resulting properties (band gap, elastic constants). +ASMO captures important simulation-related concepts such as computational methods, simulation algorithms, input parameters, output parameters, interatomic potentials, statistical ensembles, and calculated physical properties [#asmo-doc]_. The ontology also uses the W3C Provenance Ontology (PROV-O) to describe simulation processes and provenance, enabling the representation of computational workflows, activities, inputs, and generated results in materials modeling [#asmo-doc]_ [#asmo-github]_. + +ASMO facilitates reproducibility and interoperability in computational materials science by providing standardized semantic representations of simulation methods, parameters, workflows, and calculated properties [#asmo-doc]_. + +**Example Usage**: +Annotate a materials database entry with ASMO terms for the simulation method, such as DFT or MD, together with computational details such as input parameters, interatomic potentials, statistical ensembles, and resulting properties such as band gap, elastic constants, formation energy, or bulk modulus [#asmo-doc]_. Metrics & Statistics -------------------------- @@ -136,3 +141,19 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#asmo-doc] Azocar Guzman, A. 2024. + "Atomistic Simulation Methods Ontology (ASMO)." + Ontology documentation. + DOI: 10.5281/zenodo.10805591. + Available at: + `https://ocdo.github.io/asmo/ `_ + +.. [#asmo-github] OCDO. n.d. + "ASMO - Atomistic Simulation Methods Ontology." + GitHub repository. + Available at: + `https://github.com/OCDO/asmo `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/atomistic.rst b/docs/source/benchmarking/materials_science_and_engineering/atomistic.rst index 359abaa..1c1c2da 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/atomistic.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/atomistic.rst @@ -23,10 +23,12 @@ Atomistic Ontology (Atomistic) ======================================================================================================== -The Atomistic Ontology is an EMMO-based domain ontology designed for atomistic and electronic modeling in materials science. It provides a structured vocabulary for representing atomic-scale structures, electronic properties, and simulation methods. The ontology supports semantic annotation of computational models, enabling interoperability and data integration across materials modeling platforms. Atomistic Ontology facilitates detailed description of atomic configurations, electronic states, and interactions, supporting advanced materials design and analysis. The ontology is actively maintained and extended to incorporate new modeling techniques and scientific findings. By providing a standardized framework, Atomistic Ontology enhances reproducibility, data sharing, and collaborative research in atomistic simulations. +The Atomistic Ontology is an EMMO-based domain ontology designed for atomistic and electronic modelling [#atomistic-github]_. It provides a structured vocabulary for representing concepts related to atomistic modelling, electronic modelling, and their connection to the wider EMMO materials ontology ecosystem [#atomistic-github]_. The ontology supports semantic annotation of atomistic and electronic modelling knowledge, enabling interoperability and data integration across materials modelling platforms [#atomistic-github]_. + +Atomistic Ontology facilitates the description of computational modelling concepts used in atomistic and electronic simulations, supporting more consistent representation, sharing, and reuse of materials modelling knowledge [#atomistic-github]_. The ontology is described as work-in-progress and under development, rather than as a fully finalized or actively maintained standard [#atomistic-github]_. By providing a standardized semantic framework, Atomistic Ontology supports reproducibility, data sharing, and collaborative research in atomistic and electronic modelling [#atomistic-github]_. **Example Usage**: -Annotate a computational materials science dataset with Atomistic Ontology terms to specify atomic structures, electronic properties, and simulation parameters, enabling semantic search and integration with modeling tools. +Annotate a computational materials science dataset with Atomistic Ontology terms to specify atomistic modelling concepts, electronic modelling concepts, simulation-related information, and their links to EMMO-based materials modelling vocabularies, enabling semantic search and integration with modelling tools [#atomistic-github]_. Metrics & Statistics -------------------------- @@ -135,3 +137,12 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#atomistic-github] EMMO-repo. n.d. + "Domain ontology for atomistic and electronic modelling." + GitHub repository. + Available at: + `https://github.com/emmo-repo/domain-atomistic `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/battinfo.rst b/docs/source/benchmarking/materials_science_and_engineering/battinfo.rst index 0f7864a..0846dba 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/battinfo.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/battinfo.rst @@ -23,10 +23,10 @@ Battery Interface Ontology (BattINFO) ======================================================================================================== -The Battery Interface Ontology (BattINFO) is a domain ontology developed to standardize and harmonize the representation of battery-related knowledge, data, and interfaces. BattINFO provides a structured vocabulary for describing battery materials, components, interfaces, processes, and performance metrics, supporting the annotation and integration of experimental and computational battery data. The ontology is designed to enable FAIR battery data practices, facilitating data sharing, interoperability, and reuse across research, industry, and digital platforms. BattINFO supports semantic annotation of battery experiments, manufacturing workflows, and simulation results, enabling advanced analytics, lifecycle assessment, and knowledge discovery. The ontology is extensible and can be aligned with other materials science and energy ontologies for broader compatibility. BattINFO is actively maintained and extended to incorporate new battery technologies, standards, and research requirements. +The Battery Interface Ontology (BattINFO) is a domain ontology developed to standardize and harmonize the representation of battery-related knowledge, data, and interfaces [#battinfo-doc]_ [#battinfo-github]_. BattINFO provides a structured vocabulary for describing battery cells, materials, methods, and data, supporting the annotation and integration of battery-related experimental and computational information [#battinfo-doc]_. The ontology is designed to support FAIR battery data practices by improving data sharing, interoperability, machine readability, and reuse across research, industry, and digital platforms [#battinfo-github]_. BattINFO supports semantic annotation of battery knowledge and enables linked data representation, semantic querying, and integration with other tools and databases [#battinfo-doc]_ [#battinfo-github]_. **Example Usage**: -Annotate a battery research dataset with BattINFO terms to specify electrode materials, electrolyte composition, interface properties, and performance metrics (e.g., capacity, cycle life), enabling semantic search and integration with battery databases and digital twins. +Annotate a battery research dataset with BattINFO terms to specify battery cells, electrode materials, electrolyte composition, methods, interfaces, and performance-related data, enabling semantic search, data integration, and reuse across battery databases and digital battery platforms [#battinfo-doc]_ [#battinfo-github]_. Metrics & Statistics -------------------------- @@ -135,3 +135,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#battinfo-doc] BIG-MAP. n.d. + "Battery Interface Ontology — BattINFO documentation." + Available at: + `https://big-map.github.io/BattINFO/ `_ + +.. [#battinfo-github] BIG-MAP. n.d. + "BattINFO: Battery Interface Ontology." + GitHub repository. + Available at: + `https://github.com/BIG-MAP/BattINFO `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/bmo.rst b/docs/source/benchmarking/materials_science_and_engineering/bmo.rst index 2fb21ed..d9b4493 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/bmo.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/bmo.rst @@ -23,10 +23,10 @@ Building Material Ontology (BMO) ======================================================================================================== -The Building Material Ontology (BMO) is a domain ontology designed to represent the main concepts, types, layers, and properties of building materials used in construction and civil engineering. BMO provides a structured vocabulary for describing material composition, physical and chemical properties, functional layers, and relationships between materials in building assemblies. The ontology supports semantic annotation of building material data, enabling interoperability between construction databases, digital twins, and building information modeling (BIM) systems. BMO is designed for extensibility, allowing integration with other ontologies and standards for sustainability, performance, and regulatory compliance. By providing a standardized framework, BMO facilitates advanced search, material selection, lifecycle analysis, and knowledge sharing in the construction industry. The ontology is actively maintained and extended to incorporate new materials, technologies, and industry requirements. +The Building Material Ontology (BMO) is a domain ontology designed to represent the main concepts, types, layers, and properties of building materials used in construction and civil engineering [#bmo-doc]_. BMO provides a structured vocabulary for describing material composition, material properties, functional layers, values, units, and relationships between materials in building assemblies [#bmo-doc]_. The ontology supports semantic annotation of building material data, enabling interoperability between construction databases, Building Information Modeling (BIM) systems, and digital construction platforms [#bmo-doc]_. By providing a standardized framework, BMO facilitates material information management, data integration, semantic search, and knowledge sharing in construction workflows [#bmo-doc]_. **Example Usage**: -Annotate a BIM model with BMO terms to specify the material composition of a wall assembly, including layers (e.g., insulation, concrete, plaster), material properties (e.g., thermal conductivity, fire resistance), and sustainability attributes, enabling semantic search and integration with construction databases. +Annotate a BIM model with BMO terms to specify the material composition of a wall assembly, including layers such as insulation, concrete, or plaster, material properties such as thermal conductivity, density, and fire resistance, and related values or units. This enables semantic search and integration with construction databases, BIM tools, and building-performance analysis systems [#bmo-doc]_. Metrics & Statistics -------------------------- @@ -135,3 +135,12 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#bmo-doc] Digital Construction Ontologies. 2021. + "Digital Construction Materials." + Ontology documentation. + Available at: + `https://digitalconstruction.github.io/Materials/v/0.5/ `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/bvco.rst b/docs/source/benchmarking/materials_science_and_engineering/bvco.rst index e9e5ebf..c250167 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/bvco.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/bvco.rst @@ -23,10 +23,12 @@ Battery Value Chain Ontology (BVCO) ======================================================================================================== -The Battery Value Chain Ontology (BVCO) is a domain ontology developed to model processes, entities, and relationships along the battery value chain, from raw material extraction to recycling and disposal. BVCO provides a structured vocabulary for describing holistic processes that transform inputs (matter, energy, information) into outputs (products, byproducts, waste) using tools such as devices and algorithms. The ontology supports decomposition of processes into sub-processes, capturing predecessor and successor relationships, and enabling detailed modeling of manufacturing, logistics, usage, and end-of-life stages. BVCO facilitates semantic annotation of battery value chain data, supporting interoperability, data integration, and advanced analytics across research, industry, and regulatory platforms. By providing a standardized framework, BVCO enables lifecycle assessment, supply chain optimization, and sustainability analysis in the battery industry. The ontology is actively maintained and extended to incorporate new battery technologies, process innovations, and regulatory requirements. +The Battery Value Chain Ontology (BVCO) is a domain ontology developed to model processes along the battery value chain [#bvco-github]_ [#bvco-zenodo]_. BVCO provides a structured vocabulary for describing holistic processes that transform inputs or educts, such as matter, energy, and information, into outputs or products using tools such as devices and algorithms [#bvco-github]_. The ontology supports decomposition of processes into sub-processes and captures predecessor and successor relationships, enabling detailed representation of battery value chain activities [#bvco-github]_. + +BVCO facilitates semantic annotation of battery value chain data and supports interoperability, data integration, and knowledge sharing across battery research and industrial workflows [#bvco-github]_. It is described as a work-in-progress ontology and is based on the General Process Ontology (GPO) and EMMO [#bvco-github]_. **Example Usage**: -Annotate a battery manufacturing workflow with BVCO terms to specify raw material sourcing, cell assembly, quality control, logistics, and recycling processes, enabling semantic search and integration with supply chain management systems. +Annotate a battery manufacturing workflow with BVCO terms to specify raw material processing, cell production, quality-control steps, logistics, and recycling processes, enabling semantic search and integration with battery value chain data platforms [#bvco-github]_. Metrics & Statistics @@ -136,3 +138,19 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#bvco-github] Battery-Value-Chain-Ontology. n.d. + "Battery Value Chain Ontology." + GitHub repository. + Available at: + `https://github.com/Battery-Value-Chain-Ontology/ontology `_ + +.. [#bvco-zenodo] Stier, S., and Gold, L. 2023. + "Battery Value Chain Ontology (BVCO)." + Zenodo. + DOI: 10.5281/zenodo.8114726. + Available at: + `https://zenodo.org/records/8114726 `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/cdco.rst b/docs/source/benchmarking/materials_science_and_engineering/cdco.rst index 112c62a..7972585 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/cdco.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/cdco.rst @@ -23,10 +23,12 @@ Crystallographic Defect Core Ontology (CDCO) ======================================================================================================== -The Crystallographic Defect Core Ontology (CDCO) is a domain ontology designed to provide a unified framework for representing and integrating data about crystallographic defects in materials science. CDCO defines common terminology for various types of defects, including vacancies, interstitials, dislocations, grain boundaries, and stacking faults, as well as their properties and relationships. The ontology supports semantic annotation of experimental and computational data, enabling interoperability, data integration, and advanced analysis across materials databases and research platforms. CDCO is designed for extensibility, allowing researchers to describe new defect types, characterization methods, and material systems. By providing a standardized vocabulary, CDCO facilitates cross-study comparison, defect modeling, and knowledge sharing in materials science. The ontology is actively maintained and extended to incorporate new concepts and requirements from the materials science community. +The Crystallographic Defect Core Ontology (CDCO) is a domain ontology designed to provide common terminology for crystallographic defects and support data integration in materials science [#cdco-doc]_. CDCO provides a structured vocabulary for representing crystalline materials, crystallographic defects, point defects, line defects, planar defects, and defect complexes [#cdco-doc]_. + +The ontology supports semantic annotation of crystallographic defect data by defining relationships such as **has crystallographic defect**, **has defect complex**, and **is part of defect complex** [#cdco-doc]_ [#cdco-hasdefectcomplex]_. The **has defect complex** relation is used to link a crystalline material to a defect complex, where the defect complex represents two or more defects in close proximity that interact with each other [#cdco-hasdefectcomplex]_. By providing a standardized vocabulary, CDCO enables semantic search, data integration, and reuse of defect-related materials data [#cdco-doc]_. **Example Usage**: -Annotate a materials database with CDCO terms to specify the types of crystallographic defects present in a sample, their properties (e.g., density, energy), and relationships to material processing conditions, enabling semantic search and integration with defect modeling tools. +Annotate a materials database with CDCO terms to specify the crystallographic defects present in a crystalline material, such as point defects, line defects, planar defects, or defect complexes. For example, a crystalline material can be linked to a defect complex using **has defect complex**, enabling semantic search and integration with defect-related materials modelling tools [#cdco-doc]_ [#cdco-hasdefectcomplex]_. Metrics & Statistics -------------------------- @@ -135,3 +137,18 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#cdco-doc] OCDO. n.d. + "Crystallographic Defect Core Ontology (CDCO)." + Ontology documentation. + Available at: + `https://ocdo.github.io/cdco/ `_ + +.. [#cdco-hasdefectcomplex] OCDO. n.d. + "has defect complex." + *Crystallographic Defect Core Ontology (CDCO)*. + Available at: + `https://ocdo.github.io/cdco/#/hasDefectComplex `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/chameo.rst b/docs/source/benchmarking/materials_science_and_engineering/chameo.rst index 792d275..05027c3 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/chameo.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/chameo.rst @@ -23,10 +23,12 @@ Characterisation Methodology Domain Ontology (CHAMEO) ======================================================================================================== -The Characterisation Methodology Domain Ontology (CHAMEO) is a domain ontology for materials characterization, representing the evolution of the CHADA template in an ontological form. CHAMEO enables the generation of FAIR documentation for characterization experiments and serves as a foundation for the development of technique-specific and application-specific ontologies in the materials characterization domain. The ontology provides a structured vocabulary for describing experimental setups, measurement techniques, sample properties, data acquisition, and analysis workflows. CHAMEO supports semantic annotation of characterization data, facilitating interoperability, data integration, and advanced analytics across research projects and digital platforms. By providing a standardized framework, CHAMEO enhances reproducibility, knowledge sharing, and cross-laboratory comparison in materials science. The ontology is actively maintained and extended to incorporate new characterization methods, standards, and community requirements. +The Characterisation Methodology Domain Ontology (CHAMEO) is a domain ontology for materials characterisation that represents the CHADA template in ontological form [#chameo-github]_ [#chameo-paper]_. CHAMEO supports the generation of FAIR documentation for characterisation experiments and provides a foundation for developing technique-specific and application-specific ontologies in the materials characterisation domain [#chameo-github]_. It provides a structured vocabulary for describing characterisation methodologies, experimental workflows, samples, measurement processes, instruments, data acquisition, and data processing activities [#chameo-paper]_. + +The ontology supports semantic annotation of materials characterisation data, enabling interoperability, data integration, knowledge sharing, and more consistent documentation across laboratories and digital materials platforms [#chameo-github]_ [#chameo-paper]_. By providing a standardized framework, CHAMEO helps improve reproducibility and comparison of materials characterisation experiments [#chameo-paper]_. **Example Usage**: -Annotate a materials characterization experiment with CHAMEO terms to specify the measurement technique (e.g., X-ray diffraction), sample preparation, instrument configuration, and data analysis workflow, enabling semantic search and integration with materials databases. +Annotate a materials characterisation experiment with CHAMEO terms to specify the measurement technique, sample preparation, instrument configuration, measurement process, acquired data, and data analysis workflow. For example, an X-ray diffraction experiment can be described using CHAMEO concepts for the sample, characterisation method, instrument setup, measurement activity, and data processing steps, enabling semantic search and integration with materials databases [#chameo-github]_ [#chameo-paper]_. Metrics & Statistics -------------------------- @@ -135,3 +137,19 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#chameo-github] EMMO-repo. n.d. + "Characterisation Methodology Domain Ontology." + GitHub repository. + Available at: + `https://github.com/emmo-repo/domain-characterisation-methodology `_ + +.. [#chameo-paper] Del Nostro, P., Goldbeck, G., and Toti, D. 2022. + "CHAMEO: An ontology for the harmonisation of materials characterisation methodologies." + *Applied Ontology*, 17, 401--421. + DOI: 10.3233/AO-220271. + Available at: + `https://journals.sagepub.com/doi/10.3233/AO-220271 `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/cifcore.rst b/docs/source/benchmarking/materials_science_and_engineering/cifcore.rst index beb0c3f..3c81892 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/cifcore.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/cifcore.rst @@ -23,10 +23,12 @@ Crystallographic Information Framework Core Dictionary (CIFCore) ======================================================================================================== -The Crystallographic Information Framework Core Dictionary (CIFCore) is a domain ontology developed to provide a machine-actionable representation of data files covering various aspects of crystallography and related structural sciences. CIFCore explains the historical development of CIF dictionaries and demonstrates the handling of complex information types in crystallographic data. The ontology supports semantic annotation of crystallographic datasets, enabling interoperability, data integration, and advanced analysis in structural biology, materials science, and chemistry. CIFCore facilitates the standardized description of crystal structures, symmetry operations, atomic coordinates, and experimental conditions. By providing a comprehensive vocabulary, CIFCore supports data sharing, reproducibility, and computational modeling in crystallography research. The ontology is actively maintained and extended to incorporate new crystallographic concepts and data standards. +The Crystallographic Information Framework Core Dictionary (CIFCore) is a machine-readable dictionary developed to represent core crystallographic data within the Crystallographic Information Framework (CIF) [#cifcore-iucr]_. It provides structured data names for archiving and exchanging raw data, processed data, and derived structural results in crystallography and related structural sciences [#cifcore-iucr]_. + +The CIF Ontology provides an ontological representation of the CIF Dictionary Definition Language (DDLm) and the IUCr CIF core dictionary, supporting semantic annotation and machine-actionable use of CIF data [#cif-ontology-github]_. CIFCore supports the standardized description of crystallographic information such as crystal structures, symmetry information, atomic coordinates, diffraction data, and experimental details [#cifcore-iucr]_ [#cif-ontology-github]_. By providing a structured vocabulary, CIFCore supports data exchange, validation, interoperability, reproducibility, and integration with crystallographic and structural databases [#cifcore-iucr]_ [#cif-ontology-github]_. **Example Usage**: -Annotate a crystallographic dataset with CIFCore terms to specify crystal lattice parameters, atomic positions, symmetry groups, and experimental conditions, enabling semantic search and integration with structural databases. +Annotate a crystallographic dataset with CIFCore terms to specify crystal lattice parameters, atomic positions, symmetry information, diffraction experiment details, and derived structural results, enabling semantic search, validation, and integration with structural databases [#cifcore-iucr]_ [#cif-ontology-github]_. Metrics & Statistics -------------------------- @@ -135,3 +137,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#cifcore-iucr] International Union of Crystallography. n.d. + "Core CIF dictionary." + Available at: + `https://www.iucr.org/resources/cif/dictionaries/cif_core `_ + +.. [#cif-ontology-github] EMMO-repo. n.d. + "CIF Ontology." + GitHub repository. + Available at: + `https://github.com/emmo-repo/CIF-ontology `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/cmso.rst b/docs/source/benchmarking/materials_science_and_engineering/cmso.rst index 0340ece..091223d 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/cmso.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/cmso.rst @@ -23,10 +23,12 @@ Computational Material Sample Ontology (CMSO) ======================================================================================================== -The Computational Material Sample Ontology (CMSO) is a domain ontology developed to describe computational materials science samples (or structures), with an initial focus on atomic-scale representations and crystalline defects. CMSO provides a structured vocabulary for representing atomic configurations, simulation cells, boundary conditions, and various types of defects such as vacancies, interstitials, and dislocations. The ontology supports semantic annotation of computational models, simulation workflows, and results, enabling interoperability and data integration across materials modeling platforms. CMSO is designed for extensibility, allowing researchers to describe new sample types, simulation methods, and material systems. By providing a standardized framework, CMSO facilitates cross-study comparison, advanced analytics, and knowledge sharing in computational materials science. The ontology is actively maintained and extended to incorporate new concepts and requirements from the materials science community. +The Computational Material Sample Ontology (CMSO) is a domain ontology developed to describe computational materials science samples or structures, with a focus on machine-actionable representation of atomistic simulation data [#cmso-doc]_ [#azocar2026]_. It provides a structured vocabulary for representing computational samples, atomic-scale samples, crystal structures, chemical composition, simulation cells, geometry, and crystallographic defects [#cmso-doc]_ [#azocar2026]_. + +CMSO supports semantic annotation of computational materials data, enabling interoperability, data integration, and reuse across atomistic simulation workflows and materials databases [#azocar2026]_. The ontology is designed in a modular way and can be connected with related ontologies for crystallographic defects and atomistic simulation methods [#azocar2026]_. By providing a standardized framework, CMSO facilitates semantic search, cross-study comparison, and knowledge sharing in computational materials science [#cmso-doc]_ [#azocar2026]_. **Example Usage**: -Annotate a computational materials science dataset with CMSO terms to specify the atomic structure, simulation cell parameters, defect types, and boundary conditions, enabling semantic search and integration with materials modeling databases. +Annotate a computational materials science dataset with CMSO terms to specify the computational sample, atomic-scale structure, chemical composition, crystal structure, simulation cell, geometry, and crystallographic defects, enabling semantic search and integration with materials modelling databases [#cmso-doc]_ [#azocar2026]_. Metrics & Statistics -------------------------- @@ -135,3 +137,18 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#cmso-doc] OCDO. n.d. + "Computational Material Sample Ontology (CMSO)." + Ontology documentation. + Available at: + `https://ocdo.github.io/cmso/ `_ + +.. [#azocar2026] Azócar Guzmán, A., Menon, S., Hickel, T., and Sandfeld, S. 2026. + "Ontology-based knowledge graph infrastructure for interoperable atomistic simulation data." + arXiv:2604.06230v1. + Available at: + `https://arxiv.org/html/2604.06230v1 `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/diso.rst b/docs/source/benchmarking/materials_science_and_engineering/diso.rst index e1b0324..141cb94 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/diso.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/diso.rst @@ -23,10 +23,12 @@ Dislocation Ontology (DISO) ======================================================================================================== -DISO is a specialized ontology that formalizes the conceptualization and semantic representation of linear defects in crystalline materials, with particular focus on dislocations and their complex relationships. It provides structured vocabulary for describing dislocation types (edge, screw, mixed), dislocation properties (Burgers vector, line direction), and dislocation interactions (annihilation, multiplication, cross-slip). The ontology captures the geometric and topological properties of dislocations essential for understanding plastic deformation, work hardening, and material strength in metals and alloys. DISO enables precise annotation of experimental observations and computational simulations of dislocations in crystalline microstructures, supporting materials science research and industrial applications. DISO facilitates knowledge integration in materials databases and computational materials science by providing standardized semantic representations of linear defects. +The Dislocation Ontology (DISO) is a domain ontology that defines concepts and relationships related to linear defects in crystalline materials, especially dislocations [#diso-doc]_ [#ihsan2023]_. It provides a structured vocabulary for describing dislocation-related concepts, including dislocation structures, dislocation lines, Burgers vectors, crystal structures, and relationships between dislocation-domain entities [#ihsan2023]_. + +DISO was developed using a top-down approach, starting from general concepts in the dislocation domain and then specializing them into more specific concepts [#diso-doc]_ [#ihsan2023]_. Version 1.1 adapts and extends DISO for the discrete dislocation dynamics domain by adding missing concepts, improving class definitions, exploring additional relationships, and aligning it with related ontologies such as EMMO and the Materials Design Ontology (MDO) [#diso-doc]_. By providing a standardized semantic representation, DISO supports annotation, interoperability, data integration, and reuse of dislocation-related experimental and simulation data [#ihsan2023]_. **Example Usage**: -Annotate a transmission electron microscopy (TEM) image or molecular dynamics simulation showing dislocations with DISO terms describing dislocation type (edge or screw), Burgers vector, crystal system context, and interactions with other dislocations or grain boundaries. +Annotate a transmission electron microscopy (TEM) dataset or dislocation dynamics simulation with DISO terms to specify dislocation structures, Burgers vectors, line directions, crystal-structure context, and relationships between dislocation-domain entities, enabling semantic search and integration with materials science databases and simulation workflows [#diso-doc]_ [#ihsan2023]_. Metrics & Statistics -------------------------- @@ -135,3 +137,19 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#diso-doc] Materials Data Science and Informatics. n.d. + "Dislocation Ontology (DISO)." + *Dislocation Ontology Suite*. + Available at: + `https://materials-data-science-and-informatics.github.io/Dislocation-Ontology-Suite/DISO/ `_ + +.. [#ihsan2023] Ihsan, A. Z., Fathalla, S., and Sandfeld, S. 2023. + "DISO: A Domain Ontology for Modeling Dislocations in Crystalline Materials." + In *The 38th ACM/SIGAPP Symposium on Applied Computing (SAC '23)*, Article 4, 8 pages. + DOI: 10.1145/3555776.3578739. + Available at: + `https://arxiv.org/html/2401.02540v1 `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/dsim.rst b/docs/source/benchmarking/materials_science_and_engineering/dsim.rst index c7820b7..34455f9 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/dsim.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/dsim.rst @@ -23,10 +23,12 @@ Dislocation Simulation and Model Ontology (DSIM) ======================================================================================================== -The Dislocation Simulation and Model Ontology (DSIM) is a domain ontology developed to model concepts and relationships in the field of discrete dislocation dynamics and microscopy techniques used in dislocation research. DSIM provides a structured vocabulary for representing numerical representations of dislocations in simulations, as well as pictorial concepts such as pixels in experimental images (e.g., TEM, SEM, FIM). The ontology enables semantic annotation of simulation workflows, experimental setups, and image analysis procedures, supporting data integration and reproducibility in materials science. DSIM is designed for extensibility, allowing researchers to describe new simulation methods, image processing techniques, and dislocation phenomena. By providing a standardized framework, DSIM facilitates cross-study comparison, advanced analytics, and knowledge sharing in dislocation research. The ontology is actively maintained and extended to incorporate new concepts and requirements from the materials science community. +The Dislocation Simulation and Model Ontology (DSIM) is a domain ontology developed to model concepts and relationships in the field of discrete dislocation dynamics and microscopy techniques used in dislocation research [#dsim-github]_. DSIM provides a structured vocabulary for representing numerical descriptions of dislocations in simulations, as well as pictorial concepts such as pixels used in experimental images, including TEM, SEM, and FIM images [#dsim-github]_. + +The ontology supports semantic annotation of dislocation simulation data, microscopy image data, and related modelling information, enabling data integration, semantic search, and reuse in materials science research [#dsim-github]_. By providing a standardized representation of simulation and image-based dislocation concepts, DSIM facilitates knowledge sharing and comparison across dislocation research workflows [#dsim-github]_. **Example Usage**: -Annotate a dislocation dynamics simulation with DSIM terms to specify the simulation method, dislocation types, image analysis workflow, and experimental conditions, enabling semantic search and integration with microscopy data. +Annotate a dislocation dynamics simulation or microscopy dataset with DSIM terms to specify numerical dislocation representations, simulation-related concepts, microscopy image pixels, and image-based dislocation information, enabling semantic search and integration with dislocation research databases [#dsim-github]_. Metrics & Statistics -------------------------- @@ -135,3 +137,12 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#dsim-github] OCDO. n.d. + "Dislocation Simulation and Model Ontology." + GitHub repository. + Available at: + `https://github.com/OCDO/DSIM `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/emmo.rst b/docs/source/benchmarking/materials_science_and_engineering/emmo.rst index 4d0a5a1..97bbf9f 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/emmo.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/emmo.rst @@ -23,10 +23,12 @@ The Elementary Multiperspective Material Ontology (EMMO) ======================================================================================================== -The Elementary Multiperspective Material Ontology (EMMO) is a foundational ontology developed by the European Materials Modelling Council (EMMC) to provide a standard representational framework for materials science and engineering. EMMO is unique in that it starts from the bottom level, using the physical world as described by applied sciences, especially physics and materials science, rather than from abstract upper-level concepts. The ontology covers a wide range of concepts including materials, processes, properties, structures, and measurement techniques, supporting the semantic integration of materials modelling, characterization, and experimental data. EMMO is modular and extensible, enabling domain-specific extensions for specialized applications in materials research, digital twins, and manufacturing. By providing a rigorous semantic foundation, EMMO facilitates data interoperability, knowledge sharing, and advanced analytics across the materials science community. The ontology is actively maintained and extended to incorporate new concepts and requirements from ongoing research and industrial projects. +The Elementary Multiperspective Material Ontology (EMMO) is a foundational ontology developed by the European Materials Modelling Council (EMMC) to provide a standard representational framework for materials science, materials modelling, characterisation, and manufacturing [#emmo-emmc]_ [#emmo-github]_. EMMO is distinctive because it starts from the physical world as described by applied sciences, especially physics and materials science, rather than only from abstract upper-level concepts [#emmo-emmc]_. + +The ontology provides a framework for representing materials, processes, properties, models, measurements, and data, supporting semantic interoperability across materials modelling, characterisation, and experimental workflows [#emmo-emmc]_ [#emmo-github]_. EMMO is modular and extensible, allowing domain-specific ontologies to be developed for specialised applications in materials research, digital platforms, and manufacturing [#emmo-github]_. By providing a rigorous semantic foundation, EMMO supports data interoperability, knowledge sharing, FAIR documentation, and reuse across the materials science community [#emmo-emmc]_ [#emmo-github]_. **Example Usage**: -Annotate a materials database with EMMO terms to describe the composition, structure, and properties of a material sample, the experimental techniques used for characterization, and the modelling workflows applied, enabling semantic search and data integration across research projects. +Annotate a materials database with EMMO terms to describe the composition, structure, and properties of a material sample, the characterisation techniques used, and the modelling workflows applied, enabling semantic search and data integration across materials science research projects [#emmo-emmc]_ [#emmo-github]_. Metrics & Statistics -------------------------- @@ -135,3 +137,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#emmo-emmc] European Materials Modelling Council. n.d. + "EMMO – Ontology for Materials Science." + Available at: + `https://emmc.eu/emmo/ `_ + +.. [#emmo-github] EMMO-repo. n.d. + "Elementary Multiperspective Material Ontology (EMMO)." + GitHub repository. + Available at: + `https://github.com/emmo-repo/EMMO `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/emmocrystallography.rst b/docs/source/benchmarking/materials_science_and_engineering/emmocrystallography.rst index 353473d..8d0ea6a 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/emmocrystallography.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/emmocrystallography.rst @@ -23,10 +23,12 @@ Crystallography Ontology (EMMOCrystallography) ======================================================================================================== -The Crystallography Ontology (EMMOCrystallography) is a domain ontology based on the Elementary Multiperspective Material Ontology (EMMO) and the Crystallographic Information Framework (CIF) core dictionary. It provides a formal language for representing crystallographic concepts, including crystal structures, symmetry operations, atomic positions, and experimental conditions. EMMOCrystallography supports semantic annotation of crystallographic datasets, enabling interoperability, data integration, and advanced analysis in materials science and structural biology. The ontology facilitates the standardized description of crystallographic experiments, data processing workflows, and structural models. By providing a rigorous semantic framework, EMMOCrystallography enhances reproducibility, data sharing, and computational modeling in crystallography research. The ontology is actively maintained and extended to incorporate new crystallographic concepts and community requirements. +The Crystallography Domain Ontology (EMMOCrystallography) is an EMMO-based domain ontology for representing crystallographic knowledge [#emmocrystallography-github]_. It provides a formal vocabulary for describing crystallographic concepts such as crystal structures, crystallographic information, symmetry-related concepts, and structural data used in materials science [#emmocrystallography-github]_. + +The ontology supports semantic annotation of crystallographic datasets, enabling interoperability, data integration, and reuse of crystallographic information across materials science and modelling workflows [#emmocrystallography-github]_. By connecting crystallographic concepts with the wider EMMO ontology ecosystem, EMMOCrystallography provides a standardized semantic framework for describing crystallographic structures and related data [#emmocrystallography-github]_. **Example Usage**: -Annotate a crystallographic dataset with EMMOCrystallography terms to specify crystal lattice parameters, symmetry groups, atomic coordinates, and experimental metadata, enabling semantic search and integration with structural databases. +Annotate a crystallographic dataset with EMMOCrystallography terms to specify crystal structures, symmetry information, lattice-related data, atomic positions, and crystallographic metadata, enabling semantic search and integration with materials databases and modelling tools [#emmocrystallography-github]_. Metrics & Statistics -------------------------- @@ -135,3 +137,12 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#emmocrystallography-github] EMMO-repo. n.d. + "Crystallography Domain Ontology." + GitHub repository. + Available at: + `https://github.com/emmo-repo/domain-crystallography `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/fso.rst b/docs/source/benchmarking/materials_science_and_engineering/fso.rst index 64f30a4..fae9be1 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/fso.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/fso.rst @@ -23,10 +23,12 @@ Flow Systems Ontology (FSO) ======================================================================================================== -The Flow Systems Ontology (FSO) is a domain ontology developed to describe interconnected systems with material or energy flow connections and their components. FSO provides a structured vocabulary for representing flow systems, subsystems, flow paths, nodes, and the properties of materials or energy being transferred. The ontology supports semantic annotation of process engineering data, enabling interoperability, data integration, and advanced analysis in chemical engineering, energy systems, and process industries. FSO is designed for extensibility, allowing researchers and engineers to describe new flow system types, process configurations, and measurement techniques. By providing a standardized framework, FSO facilitates process modeling, simulation, optimization, and knowledge sharing across engineering domains. The ontology is actively maintained and extended to incorporate new concepts and requirements from the process engineering community. +The Flow Systems Ontology (FSO) is a domain ontology developed to describe interconnected systems with material or energy flow connections and their components [#fso-doc]_ [#kukkonen2021]_. FSO provides a structured vocabulary for representing flow systems, components, distribution systems, supply systems, return systems, segments, terminals, storage devices, flow controllers, flow-moving devices, and treatment devices [#fso-doc]_. + +The ontology supports the description of system composition and mass or energy flows between systems and components, enabling semantic annotation, interoperability, data integration, and querying across building and engineering workflows [#kukkonen2021]_. FSO was proposed to support flow-system descriptions from design to operation of buildings and was demonstrated using example models and SPARQL queries [#kukkonen2021]_. By providing a standardized semantic framework, FSO supports process modelling, simulation, and knowledge sharing across flow-system-related engineering domains [#fso-doc]_ [#kukkonen2021]_. **Example Usage**: -Annotate a chemical process simulation with FSO terms to specify the flow system structure, material streams, energy flows, and process units, enabling semantic search and integration with process engineering databases and simulation tools. +Annotate a building or process-engineering model with FSO terms to specify flow-system structure, supply and return systems, components such as pumps, valves, ducts, pipes, terminals, and storage devices, and the material or energy flows between them. This enables semantic search, querying, and integration with building information modelling and engineering databases [#fso-doc]_ [#kukkonen2021]_. Metrics & Statistics -------------------------- @@ -135,3 +137,19 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#fso-doc] Kücükavci, A. n.d. + "Flow Systems Ontology." + Ontology documentation. + Available at: + `https://alikucukavci.github.io/FSO/ `_ + +.. [#kukkonen2021] Kukkonen, V., Kücükavci, A., Seidenschnur, M., Rasmussen, M. H., Smith, K. M., and Hviid, C. A. 2021. + "An ontology to support flow system descriptions from design to operation of buildings." + *Automation in Construction*, 134, Article 104067. + DOI: 10.1016/j.autcon.2021.104067. + Available at: + `https://doi.org/10.1016/j.autcon.2021.104067 `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/gpo.rst b/docs/source/benchmarking/materials_science_and_engineering/gpo.rst index e04b33b..98ab1d1 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/gpo.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/gpo.rst @@ -23,10 +23,12 @@ General Process Ontology (GPO) ======================================================================================================== -The General Process Ontology (GPO) is a domain ontology developed to model processes in materials science and engineering, as well as other scientific and industrial domains. GPO provides a structured vocabulary for representing holistic processes that transform inputs (matter, energy, information) into outputs (products, byproducts, waste) using tools such as devices and algorithms. The ontology supports decomposition of processes into sub-processes, capturing predecessor and successor relationships, and enabling detailed modeling of workflows, manufacturing, and experimental procedures. GPO facilitates semantic annotation of process data, supporting interoperability, data integration, and advanced analytics across research, industry, and regulatory platforms. By providing a standardized framework, GPO enables lifecycle assessment, process optimization, and knowledge sharing in multidisciplinary projects. The ontology is actively maintained and extended to incorporate new process types, technologies, and application domains. +The General Process Ontology (GPO) is a domain ontology developed to model processes across scientific and industrial domains [#gpo-github]_ [#gpo-ontocommons]_. GPO provides a structured vocabulary for representing holistic processes that transform inputs or educts, such as matter, energy, and information, into outputs or products using tools such as devices and algorithms [#gpo-github]_ [#gpo-ontocommons]_. + +The ontology supports decomposition of processes into sub-processes and represents predecessor and successor relationships between processes [#gpo-github]_. GPO is based on EMMO and is described as a cross-project development coordinated by Fraunhofer ISC, with application areas including manufacturing, logistics, mining, and information and data processing [#gpo-github]_ [#gpo-ontocommons]_. By providing a standardized process vocabulary, GPO supports semantic annotation, data integration, interoperability, and knowledge sharing across process-related workflows [#gpo-github]_. **Example Usage**: -Annotate a manufacturing workflow with GPO terms to specify process steps, input and output materials, tools used, and process dependencies, enabling semantic search and integration with process management systems. +Annotate a manufacturing workflow with GPO terms to specify process steps, input materials or information, output products, tools such as devices or algorithms, and predecessor or successor relationships between process steps, enabling semantic search and integration with process management systems [#gpo-github]_ [#gpo-ontocommons]_. Metrics & Statistics -------------------------- @@ -135,3 +137,18 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#gpo-github] General-Process-Ontology. n.d. + "General Process Ontology (GPO)." + GitHub repository. + Available at: + `https://github.com/General-Process-Ontology/ontology `_ + +.. [#gpo-ontocommons] OntoCommons. n.d. + "General Process Ontology (GPO)." + Ontology catalogue entry. + Available at: + `https://data.ontocommons.linkeddata.es/vocabulary/GeneralProcessOntology%28gpo%29 `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/hpont.rst b/docs/source/benchmarking/materials_science_and_engineering/hpont.rst index 13dafed..6f0bbc5 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/hpont.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/hpont.rst @@ -23,10 +23,12 @@ The Heat Pump Ontology (HPOnt) ======================================================================================================== -The Heat Pump Ontology (HPOnt) is a domain ontology developed to formalize and represent all relevant information about heat pumps, including their components, operational parameters, performance metrics, and integration with building energy systems. HPOnt provides a structured vocabulary for describing heat pump types, working fluids, control strategies, installation contexts, and maintenance procedures. The ontology supports semantic annotation of heat pump data, enabling interoperability between smart building systems, energy management platforms, and research databases. HPOnt is designed for extensibility and can be adapted to represent new heat pump technologies, regulatory requirements, and sustainability metrics. By providing a standardized framework, HPOnt facilitates advanced analytics, predictive maintenance, and optimization of heat pump operation in residential, commercial, and industrial settings. The ontology is actively maintained and extended as part of the REACT project and is aligned with European Union research and innovation initiatives. +The Heat Pump Ontology (HPOnt) is a domain ontology developed to formalize and represent information about heat pump systems [#hpont-doc]_. HPOnt provides a structured vocabulary for describing heat pump-related concepts such as operating mode, cooling capacity, power supply, storage volume, power consumption, and other technical information relevant to heat pump operation [#hpont-doc]_. + +The ontology supports semantic annotation of heat pump data, enabling interoperability, data integration, querying, and reuse across smart-building and energy-management systems [#hpont-doc]_ [#hpont-bioregistry]_. HPOnt was developed in the context of the REACT project and is registered as the Heat Pump Ontology in Bioregistry [#hpont-bioregistry]_. **Example Usage**: -Annotate a smart building energy management system with HPOnt terms to specify the types of heat pumps installed, their operational parameters (e.g., COP, setpoints), maintenance schedules, and integration with renewable energy sources, enabling semantic search and optimization of building energy performance. +Annotate a smart building energy management system with HPOnt terms to specify heat pump operating mode, cooling capacity, power supply, storage volume, power consumption, and related system information, enabling semantic search and integration with building energy management platforms [#hpont-doc]_ [#hpont-bioregistry]_. Metrics & Statistics -------------------------- @@ -135,3 +137,18 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#hpont-doc] REACT Project. 2021. + "The Heat Pump Ontology (HPOnt)." + Ontology documentation. + Available at: + `https://react2020.github.io/REACT-ONTOLOGY/HPOnt/index-en.html `_ + +.. [#hpont-bioregistry] Bioregistry. n.d. + "Heat Pump Ontology." + Registry entry. + Available at: + `https://bioregistry.io/tib.hpont `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/ldo.rst b/docs/source/benchmarking/materials_science_and_engineering/ldo.rst index bc90e83..4d59560 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/ldo.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/ldo.rst @@ -23,10 +23,12 @@ Line Defect Ontology (LDO) ======================================================================================================== -The Line Defect Ontology (LDO) is a domain ontology developed to provide a comprehensive and standardized vocabulary for describing line defects in crystalline materials, such as dislocations and disclinations. LDO enables the semantic annotation of experimental and computational data related to line defects, supporting interoperability and data integration across materials science databases and research platforms. The ontology covers key concepts including defect types, geometric and topological properties, formation mechanisms, and interactions with other defects or microstructural features. LDO is designed for extensibility, allowing researchers to describe new line defect types, characterization methods, and material systems as the field evolves. By providing a rigorous semantic framework, LDO facilitates advanced analytics, defect modeling, and knowledge sharing in materials science and engineering. The ontology is actively maintained and extended to incorporate new concepts and requirements from the materials science community. +The Line Defect Ontology (LDO) is a domain ontology designed to describe line defects in crystalline materials, such as dislocations and disclinations [#ldo-github]_. LDO provides a structured vocabulary for representing line-defect concepts and their relationships, supporting the semantic description of linear defects in crystalline materials [#ldo-github]_. + +The ontology enables semantic annotation of experimental and computational data related to line defects, supporting interoperability, data integration, semantic search, and reuse across materials science databases and research workflows [#ldo-github]_. By providing a standardized vocabulary, LDO facilitates knowledge sharing and comparison of line-defect information in materials science and engineering [#ldo-github]_. **Example Usage**: -Annotate a transmission electron microscopy (TEM) dataset with LDO terms to specify the types of line defects observed, their Burgers vectors, line directions, and interactions with grain boundaries, enabling semantic search and integration with defect modeling tools. +Annotate a transmission electron microscopy (TEM) dataset or computational materials dataset with LDO terms to specify line defects such as dislocations or disclinations, enabling semantic search and integration with crystallographic defect and materials modelling databases [#ldo-github]_. Metrics & Statistics -------------------------- @@ -135,3 +137,12 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#ldo-github] OCDO. n.d. + "Line Defect Ontology (LDO)." + GitHub repository. + Available at: + `https://github.com/OCDO/ldo `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/lpbfo.rst b/docs/source/benchmarking/materials_science_and_engineering/lpbfo.rst index 6d3eff7..1d66de3 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/lpbfo.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/lpbfo.rst @@ -23,10 +23,12 @@ Laser Powder Bed Fusion Ontology (LPBFO) ======================================================================================================== -The Laser Powder Bed Fusion Ontology (LPBFO) is a domain ontology for describing additive manufacturing processes, specifically Laser Powder Bed Fusion (LPBF) and Selective Laser Melting (SLM). LPBFO provides a structured vocabulary for representing process parameters, materials, equipment, component geometries, and quality attributes relevant to LPBF manufacturing. The ontology builds on BFO2.0 and BWMD_mid, and incorporates terminology from ISO/ASTM 52900:2015 to ensure alignment with industry standards. LPBFO supports semantic annotation of digital manufacturing workflows, enabling data integration, process optimization, and sustainability assessment through Life Cycle Analysis (LCA) classes. By providing a standardized framework, LPBFO facilitates interoperability between digital manufacturing systems, quality management, and research databases. The ontology is actively maintained and extended to support new developments in additive manufacturing and sustainability assessment. +The Laser Powder Bed Fusion Ontology (LPBFO) is a domain ontology developed to describe knowledge related to Laser Powder Bed Fusion (LPBF) additive manufacturing [#lpbfo-gitlab]_. LPBFO provides a structured vocabulary for representing LPBF manufacturing knowledge, including process information, materials, equipment, component-related data, and quality-relevant concepts [#lpbfo-gitlab]_. + +The ontology supports semantic annotation of LPBF manufacturing workflows, enabling data integration, knowledge sharing, semantic search, and interoperability across additive manufacturing research and industrial platforms [#lpbfo-gitlab]_. By providing a standardized semantic framework, LPBFO supports the reuse and comparison of LPBF manufacturing knowledge [#lpbfo-gitlab]_. **Example Usage**: -Annotate an LPBF manufacturing workflow with LPBFO terms to specify process parameters (e.g., laser power, scan speed), material types, component geometry, and LCA attributes, enabling semantic search and integration with digital manufacturing platforms. +Annotate an LPBF manufacturing workflow with LPBFO terms to specify process parameters such as laser power, scan speed, layer thickness, material type, equipment information, component geometry, and quality-related attributes, enabling semantic search and integration with digital manufacturing platforms [#lpbfo-gitlab]_. Metrics & Statistics -------------------------- @@ -135,3 +137,12 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#lpbfo-gitlab] Fraunhofer EMI_datamanagement. n.d. + "LPBFO: Laser Powder Bed Fusion Ontology." + GitLab repository. + Available at: + `https://gitlab.cc-asp.fraunhofer.de/EMI_datamanagement/LPBFO `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/mambo.rst b/docs/source/benchmarking/materials_science_and_engineering/mambo.rst index 949a1a7..b7873c1 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/mambo.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/mambo.rst @@ -23,10 +23,12 @@ Molecules And Materials Basic Ontology (MAMBO) ======================================================================================================== -MAMBO (Molecules And Materials Basic Ontology) is a domain ontology for molecular materials, designed to facilitate the retrieval and integration of structured information regarding molecular materials and their applications. The ontology provides a comprehensive framework for representing molecular structures, properties, and interactions, supporting both computational modeling and empirical research. MAMBO encompasses key entities such as molecules, materials, devices, and processes, and models relationships between these entities to capture the complexity of molecular systems. The ontology employs a class-based modeling approach, defining classes for different types of molecules, materials, and devices, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. MAMBO supports the integration of data from computational simulations and experimental studies, promoting interoperability and data-driven research in materials science. Typical applications of MAMBO include the development of new materials for electronic devices, the optimization of molecular structures for specific applications, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, MAMBO enhances collaboration and innovation in the field of molecular materials. +The Materials And Molecules Basic Ontology (MAMBO) is a domain ontology for molecular materials and related applications [#mambo-paper]_ [#mambo-github]_. It is designed to organize knowledge about computational and experimental workflows involving molecular materials, nanomaterials, supramolecular systems, molecular aggregates, organic materials, and polymeric materials [#mambo-paper]_. + +MAMBO provides a structured vocabulary for representing molecular materials, structures, properties, devices, methods, workflows, and application-related concepts [#mambo-paper]_ [#mambo-github]_. The ontology supports the retrieval and integration of structured information about molecular materials and helps connect computational simulations with experimental studies [#mambo-github]_. By providing a lightweight and modular semantic framework, MAMBO supports interoperability, data integration, predictive modelling, materials design, and knowledge sharing in molecular materials research [#mambo-paper]_. **Example Usage**: -Annotate a dataset of molecular materials with MAMBO terms to specify molecular structures, properties, and interactions, enabling semantic search and integration with computational modeling tools and experimental databases. +Annotate a molecular materials dataset with MAMBO terms to specify molecular structures, material properties, devices, methods, workflows, and application contexts, enabling semantic search and integration with computational modelling tools and experimental databases [#mambo-paper]_ [#mambo-github]_. Metrics & Statistics -------------------------- @@ -135,3 +137,18 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#mambo-paper] Le Piane, F., Baldoni, M., Gaspari, M., and Mercuri, F. 2024. + "MAMBO: a lightweight ontology for multiscale materials and applications." + arXiv:2412.17877. + Available at: + `https://arxiv.org/abs/2412.17877 `_ + +.. [#mambo-github] DAIMONERS. n.d. + "MAMBO: Ontology for molecular materials." + GitHub repository. + Available at: + `https://github.com/daimoners/MAMBO `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/mat.rst b/docs/source/benchmarking/materials_science_and_engineering/mat.rst index 6dc3737..3f85ab6 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/mat.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/mat.rst @@ -23,11 +23,12 @@ Material Properties Ontology (MAT) ======================================================================================================== -The Material Properties Ontology (MAT) is designed to provide a comprehensive vocabulary for describing building components, materials, and their properties within the construction industry. It focuses on supporting applications related to building renovation projects by offering a structured framework for representing material characteristics, performance metrics, and relationships between different building elements. MAT encompasses key entities such as materials, components, and properties, and models relationships to capture the complexity of construction materials. +The Material Properties Ontology (MAT) is designed to provide a structured vocabulary for describing building components, materials, and their properties within the construction industry [#mat-doc]_ [#mat-bioregistry]_. It supports building renovation projects by representing material characteristics, property values, and relationships between materials, components, and building elements [#mat-doc]_. -The ontology employs a class-based modeling approach, defining classes for various material types and properties, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. MAT supports the integration of data from design, construction, and renovation projects, promoting interoperability and data-driven research in the construction industry. +MAT enables semantic annotation and integration of data from design, construction, and renovation workflows, supporting interoperability, data retrieval, and reuse in construction informatics platforms [#mat-doc]_. By providing a standardized vocabulary for material-property representation, MAT supports semantic search, material assessment, and knowledge sharing in building renovation and construction projects [#mat-doc]_ [#mat-bioregistry]_. -Typical applications of MAT include the design and optimization of building materials for energy efficiency, the assessment of material properties for renovation projects, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, MAT enhances collaboration and innovation in the field of construction materials. +**Example Usage**: +Annotate a building renovation project with MAT terms to specify material types, building components, material properties, and performance-related values, enabling semantic search and integration with construction informatics and BIM-based renovation platforms [#mat-doc]_ [#mat-bioregistry]_. Metrics & Statistics -------------------------- @@ -137,5 +138,18 @@ Use the following code to import this ontology programmatically: taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations -**Example Usage**: -Annotate a building renovation project with MAT terms to specify material types, properties, and performance metrics, enabling semantic search and integration with construction informatics platforms. + +References +---------- + +.. [#mat-doc] Poveda-Villalón, M., and Chávez-Feria, S. n.d. + "Material Properties Ontology." + Ontology documentation. + Available at: + `https://bimerr.iot.linkeddata.es/def/material-properties/ `_ + +.. [#mat-bioregistry] Bioregistry. n.d. + "Material properties ontology." + Registry entry. + Available at: + `https://bioregistry.io/tib.mat `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/materialinformation.rst b/docs/source/benchmarking/materials_science_and_engineering/materialinformation.rst index 96fb9e6..8a3230f 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/materialinformation.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/materialinformation.rst @@ -23,14 +23,12 @@ Material Information Ontology (MaterialInformation) ======================================================================================================== -The Material Information Ontology is a comprehensive framework designed to represent various aspects of materials science, including environment, geometry, material information, manufacturing processes, properties, substances, unit dimensions, structures, equations, and physical constants. This ontology is divided into smaller partitions, each focusing on a specific domain, to facilitate detailed modeling and integration of materials data. +The Material Information Ontology is based on Ashino's materials ontology framework for exchanging materials information and knowledge across heterogeneous materials databases [#ashino2010]_. It provides a computer-readable concept map for materials science and engineering, using Semantic Web ontology language to describe materials-related concepts and relationships [#ashino2010]_. -The ontology employs a modular approach, defining classes and properties for each partition to capture the complexity of materials science. It supports semantic annotation of materials data, enabling interoperability, data integration, and advanced analysis across research databases and digital platforms. By providing a standardized framework, the Material Information Ontology facilitates cross-study comparison, materials selection, and knowledge sharing in materials research and industry. - -Typical applications include the integration of materials data from various sources, the development of new materials with specific properties, and the optimization of manufacturing processes. The ontology is actively maintained and extended to incorporate new materials, technologies, and research requirements. +The ontology is organized into sub-ontologies for key materials-information areas such as substance, process, environment, and property [#ashino2010]_. It supports metadata description, data exchange, information integration, and retrieval across distributed materials databases [#ashino2010]_. By providing a structured vocabulary, the Material Information Ontology helps improve interoperability, materials information sharing, and knowledge reuse in materials science and engineering [#ashino2010]_. **Example Usage**: -Annotate a materials database with Material Information Ontology terms to specify material properties, manufacturing processes, and environmental conditions, enabling semantic search and integration with materials informatics platforms. +Annotate a materials database with Material Information Ontology terms to specify substances, material properties, processing conditions, and environmental information, enabling semantic search, data exchange, and integration across heterogeneous materials informatics platforms [#ashino2010]_. Metrics & Statistics -------------------------- @@ -139,3 +137,12 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#ashino2010] Ashino, T. 2010. + "Materials Ontology: An Infrastructure for Exchanging Materials Information and Knowledge." + *Data Science Journal*, 9, 54--61. + Available at: + `https://datascience.codata.org/de-DE/articles/131/files/submission/proof/131-1-245-1-10-20150415.pdf `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/matonto.rst b/docs/source/benchmarking/materials_science_and_engineering/matonto.rst index 2363e9d..5d06265 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/matonto.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/matonto.rst @@ -23,10 +23,12 @@ Material Ontology (MatOnto) ======================================================================================================== -The Material Ontology (MatOnto) is a domain ontology based on the Basic Formal Ontology (BFO) designed to provide a structured vocabulary for representing materials, their properties, and relationships in materials science and engineering. MatOnto supports semantic annotation of materials data, enabling interoperability, data integration, and advanced analysis across research databases, digital twins, and manufacturing systems. The ontology covers key concepts such as material types, compositions, processing methods, properties, and applications. MatOnto is designed for extensibility, allowing researchers and engineers to describe new materials, characterization techniques, and performance metrics. By providing a standardized framework, MatOnto facilitates cross-study comparison, materials selection, and knowledge sharing in materials research and industry. The ontology is actively maintained and extended to incorporate new materials, technologies, and research requirements. +The Material Ontology (MatOnto) is a domain ontology for representing materials science knowledge, including materials, material categories, properties, units, symbols, crystal-related concepts, and thermodynamic or thermomechanical properties [#matonto-matportal]_ [#matonto-github]_. It provides a structured vocabulary for describing material types such as metals, ceramics, composites, polymers, plastics, glasses, nanomaterials, and related material qualities [#matonto-github]_. + +MatOnto supports semantic annotation of materials data, enabling interoperability, data integration, retrieval, and reuse across materials science databases and informatics workflows [#matonto-matportal]_. The ontology includes concepts for measured properties such as band gap, heat capacity, specific heat, bulk modulus, shear modulus, Gibbs free energy, and other material-related quantities [#matonto-github]_. By providing a standardized vocabulary, MatOnto facilitates semantic search, materials information sharing, and knowledge integration in materials science and engineering [#matonto-matportal]_ [#matonto-github]_. **Example Usage**: -Annotate a materials database with MatOnto terms to specify material types (e.g., polymer, alloy), composition, processing methods, and properties (e.g., tensile strength, thermal conductivity), enabling semantic search and integration with materials informatics platforms. +Annotate a materials database with MatOnto terms to specify material types such as metal, ceramic, polymer, or nanomaterial, together with properties such as band gap, heat capacity, bulk modulus, shear modulus, crystal system, and space group information, enabling semantic search and integration with materials informatics platforms [#matonto-matportal]_ [#matonto-github]_. Metrics & Statistics -------------------------- @@ -135,3 +137,18 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#matonto-matportal] Materials Open Laboratory. 2021. + "MatOnto Ontology." + MatPortal ontology entry. + Available at: + `https://matportal.org/ontologies/MATONTO `_ + +.. [#matonto-github] iNovexIrad. n.d. + "MatOnto-Ontologies." + GitHub repository. + Available at: + `https://github.com/inovexcorp/MatOnto-Ontologies `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/matvoc.rst b/docs/source/benchmarking/materials_science_and_engineering/matvoc.rst index 7cf4f0c..b241874 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/matvoc.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/matvoc.rst @@ -23,14 +23,12 @@ Materials Vocabulary (MatVoc) ======================================================================================================== -The Materials Vocabulary (MatVoc) is an ontology developed within the STREAM project to provide a structured vocabulary for representing materials and their properties. It aims to facilitate the integration and retrieval of materials data across various domains, supporting applications in materials science, engineering, and related fields. MatVoc encompasses key entities such as materials, properties, and processes, and models relationships to capture the complexity of materials data. +The Materials Vocabulary (MatVoc) is a vocabulary developed in the context of the STREAM project to provide structured terminology for representing materials-related knowledge [#matvoc-github]_. It supports the description of materials concepts in RDF/Turtle form and provides documentation and RDF files through the STREAM project resources [#matvoc-github]_. -The ontology employs a class-based modeling approach, defining classes for different types of materials and properties, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. MatVoc supports the integration of data from experimental studies, computational simulations, and industrial applications, promoting interoperability and data-driven research in materials science. - -Typical applications of MatVoc include the development of new materials with specific properties, the optimization of materials for industrial applications, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, MatVoc enhances collaboration and innovation in the field of materials science. +MatVoc supports semantic annotation, retrieval, and integration of materials data, enabling interoperability and reuse across materials science workflows and related ontology-based applications [#matvoc-github]_. It is also used together with other vocabularies in materials-science ontology work, such as the Materials Science Laboratory Equipment ontology, where MatVoc contributes materials-related terminology [#msle2023]_. **Example Usage**: -Annotate a materials database with MatVoc terms to specify material types, properties, and processes, enabling semantic search and integration with materials informatics platforms. +Annotate a materials database with MatVoc terms to specify materials-related concepts and properties, enabling semantic search, RDF-based integration, and reuse of materials information across materials informatics platforms [#matvoc-github]_. Metrics & Statistics -------------------------- @@ -139,3 +137,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- +.. [#matvoc-github] STREAM Project. n.d. + "Ontology." + GitHub repository. + Available at: + `https://github.com/stream-project/ontology `_ + +.. [#msle2023] Jalali, M., et al. 2023. + "An ontology for Materials Science Laboratory Equipment." + arXiv:2308.07325. + Available at: + `https://arxiv.org/pdf/2308.07325 `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/matwerk.rst b/docs/source/benchmarking/materials_science_and_engineering/matwerk.rst index 7ff853e..43c0fb2 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/matwerk.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/matwerk.rst @@ -23,14 +23,12 @@ NFDI MatWerk Ontology (MatWerk) ======================================================================================================== -NFDI MatWerk Ontology (MWO) aims to establish a digital infrastructure for Materials Science and Engineering (MSE), fostering improved data sharing and collaboration. This ontology provides a comprehensive framework for structuring research data and enhancing interoperability within the MSE community. MWO is aligned with the Basic Formal Ontology (BFO) and incorporates the modular approach of the NFDIcore mid-level ontology, enriching metadata through standardized classes and properties. +The NFDI MatWerk Ontology (MWO) is a BFO-compliant ontology developed for research data management in Materials Science and Engineering (MSE) [#mwo-doc]_ [#mwo-paper]_. It provides a semantic framework for structuring MSE research data and improving interoperability, data sharing, and knowledge representation within the NFDI-MatWerk community [#mwo-doc]_ [#mwo-paper]_. -The ontology addresses key aspects of MSE research data, including the NFDI-MatWerk community structure, covering task areas, infrastructure use cases, projects, researchers, and organizations. It also describes essential NFDI resources, such as software, workflows, ontologies, publications, datasets, metadata schemas, instruments, facilities, and educational materials. Additionally, MWO represents NFDI-MatWerk services, academic events, courses, and international collaborations. - -As the foundation for the MSE Knowledge Graph, MWO facilitates efficient data integration and retrieval, promoting collaboration and knowledge representation across MSE domains. This digital transformation enhances data discoverability, reusability, and accelerates scientific exchange, innovation, and discoveries by optimizing research data management and accessibility. +MWO incorporates the modular approach of the NFDIcore mid-level ontology and represents key MSE research-data entities such as task areas, infrastructure use cases, projects, researchers, organizations, datasets, software, workflows, ontologies, publications, metadata schemas, instruments, facilities, services, and educational resources [#mwo-doc]_. As a foundation for the MSE Knowledge Graph, MWO supports semantic annotation, data integration, retrieval, discoverability, and reuse of materials science research data [#mwo-doc]_ [#mwo-paper]_. **Example Usage**: -Annotate a research project with MWO terms to specify task areas, infrastructure use cases, and resources, enabling semantic search and integration with the MSE Knowledge Graph. +Annotate a materials science research project with MWO terms to specify task areas, infrastructure use cases, participating researchers, organizations, datasets, software, workflows, instruments, and related resources, enabling semantic search and integration with the MSE Knowledge Graph [#mwo-doc]_ [#mwo-paper]_. Metrics & Statistics -------------------------- @@ -139,3 +137,19 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#mwo-doc] ISE-FIZKarlsruhe. n.d. + "NFDI MatWerk Ontology (MWO)." + Ontology documentation. + Available at: + `https://ise-fizkarlsruhe.github.io/mwo/docs/ `_ + +.. [#mwo-paper] Beygi Nasrabadi, H., et al. 2025. + "NFDI MatWerk Ontology (MWO): A BFO-Compliant Ontology for Research Data Management in Materials Science and Engineering." + *Advanced Engineering Materials*. + DOI: 10.1002/adem.202502331. + Available at: + `https://advanced.onlinelibrary.wiley.com/doi/10.1002/adem.202502331 `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/mdo.rst b/docs/source/benchmarking/materials_science_and_engineering/mdo.rst index cbb4599..fda6fc1 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/mdo.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/mdo.rst @@ -23,11 +23,12 @@ Materials Design Ontology (MDO) ======================================================================================================== -The Materials Design Ontology (MDO) is a comprehensive framework developed to represent domain knowledge in the field of materials design, particularly focusing on solid-state physics and computational materials science. MDO provides a structured vocabulary for describing materials, their properties, and design processes, supporting both theoretical and experimental research in materials science. +The Materials Design Ontology (MDO) is a domain ontology developed to represent knowledge in the materials design field, especially concepts from solid-state physics and computational materials science [#li2020]_ [#mdo-github]_. It defines concepts and relations for describing materials, structures, properties, calculations, and data used in materials design databases [#li2020]_. -The ontology employs a class-based modeling approach, defining classes for different types of materials, properties, and design processes, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. MDO supports the integration of data from computational simulations and experimental studies, promoting interoperability and data-driven research in materials design. +MDO is guided by data models from well-known materials databases and the OPTIMADE effort, supporting improved interoperability and data integration across heterogeneous computational materials databases [#li2020]_ [#mdo-github]_. By providing a standardized vocabulary, MDO supports semantic annotation, data access, search, and reuse of materials design data [#li2020]_. -Typical applications of MDO include the development of new materials with specific properties, the optimization of materials for industrial applications, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, MDO enhances collaboration and innovation in the field of materials design. +**Example Usage**: +Annotate a computational materials database with MDO terms to specify a material, its crystal structure, calculated properties, calculation information, and related database identifiers, enabling semantic search and integration across materials informatics platforms [#li2020]_ [#mdo-github]_. Metrics & Statistics -------------------------- @@ -137,5 +138,18 @@ Use the following code to import this ontology programmatically: taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations -**Example Usage**: -Annotate a materials design project with MDO terms to specify material types, design processes, and properties, enabling semantic search and integration with materials informatics platforms. +References +---------- + +.. [#li2020] Li, H., Armiento, R., and Lambrix, P. 2020. + "An Ontology for the Materials Design Domain." + In *The Semantic Web -- ISWC 2020*, 212--227. + DOI: 10.1007/978-3-030-62466-8_14. + Available at: + `https://arxiv.org/abs/2006.07712 `_ + +.. [#mdo-github] LiUSemWeb. n.d. + "Materials Design Ontology." + GitHub repository. + Available at: + `https://github.com/LiUSemWeb/Materials-Design-Ontology `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/mds.rst b/docs/source/benchmarking/materials_science_and_engineering/mds.rst index d149179..2595f9a 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/mds.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/mds.rst @@ -23,14 +23,12 @@ Materials Data Science Ontology (MDS) ======================================================================================================== -The Materials Data Science Ontology (MDS) is a comprehensive framework designed to encompass multiple domains relevant to materials science, chemical synthesis, characterizations, photovoltaics, and geospatial datasets. MDS provides a structured vocabulary for representing data and processes in these domains, supporting both theoretical and experimental research in materials science. +The Materials Data Science Ontology (MDS) is a low-level ontology designed to unify domain knowledge in materials science and applied data science [#mds-paper]_ [#mds-matportal]_. It encompasses multiple domains relevant to materials science, chemical synthesis and characterisation, photovoltaics, and geospatial datasets [#mds-matportal]_. -The ontology employs a class-based modeling approach, defining classes for different types of materials, processes, and data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. MDS supports the integration of data from various sources, promoting interoperability and data-driven research in materials science. - -Typical applications of MDS include the integration of materials data from various sources, the development of new materials with specific properties, and the optimization of manufacturing processes. By providing a standardized vocabulary and framework, MDS enhances collaboration and innovation in the field of materials science. +MDS provides a structured vocabulary for representing materials-related data, processes, and domain-specific concepts, with terms mapped to PMDCo and BFO ontologies [#mds-matportal]_ [#mds-paper]_. It follows a modular approach to support interoperability, data integration, semantic reasoning, and reuse across heterogeneous materials and data science datasets [#mds-paper]_. By providing a standardized framework, MDS supports semantic annotation, knowledge graph construction, and data-driven research in materials science [#mds-paper]_. **Example Usage**: -Annotate a materials database with MDS terms to specify material properties, processes, and data, enabling semantic search and integration with materials informatics platforms. +Annotate a materials database with MDS terms to specify materials science concepts, chemical synthesis information, characterisation data, photovoltaic datasets, and geospatial information, enabling semantic search, data integration, and knowledge graph construction across materials informatics platforms [#mds-paper]_ [#mds-matportal]_. Metrics & Statistics -------------------------- @@ -139,3 +137,19 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#mds-paper] Rajamohan, B. P., Bradley, A. C. H., Tran, V. D., et al. 2025. + "Materials Data Science Ontology(MDS-Onto): Unifying Domain Knowledge in Materials and Applied Data Science." + *Scientific Data*, 12, 628. + DOI: 10.1038/s41597-025-04938-5. + Available at: + `https://www.nature.com/articles/s41597-025-04938-5 `_ + +.. [#mds-matportal] MatPortal. n.d. + "Materials Data Science Ontology." + Ontology registry entry. + Available at: + `https://matportal.org/ontologies/MDS `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/mdsonto.rst b/docs/source/benchmarking/materials_science_and_engineering/mdsonto.rst index db0090b..3cddcc5 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/mdsonto.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/mdsonto.rst @@ -24,14 +24,12 @@ The Modular Ontology for Materials and Data Science (MDS-Onto) ============================================================================== -MDS-Onto is a domain-level ontology that describes terms in Materials Data Science. It is divided into six large modules: BuiltEnv, Exposure, Chemistry, Manufacture, Characterization, and Geospatial. Each module contains multiple sub-modules such as FTIR, AFM, Chem-Rxn, PV-Module, and Accelerated Exposure, providing a comprehensive framework for representing data and processes in materials science. +MDS-Onto is a domain-level ontology for Materials Data Science that provides structured terminology for representing materials-related data and applied data science concepts [#mds-paper]_. It is organized into six major modules: BuiltEnv, Exposure, Chemistry, Manufacture, Characterization, and Geospatial, with sub-modules such as FTIR, AFM, Chem-Rxn, PV-Module, and Accelerated Exposure [#mds-paper]_. -MDS-Onto employs a modular approach, defining classes and properties for each module to capture the complexity of materials data science. It supports semantic annotation of materials data, enabling interoperability, data integration, and advanced analysis across research databases and digital platforms. By providing a standardized framework, MDS-Onto facilitates cross-study comparison, materials selection, and knowledge sharing in materials research and industry. - -Typical applications include the integration of materials data from various sources, the development of new materials with specific properties, and the optimization of manufacturing processes. The ontology is actively maintained and extended to incorporate new materials, technologies, and research requirements. +MDS-Onto follows a modular approach, defining classes and properties for each module to represent the complexity of materials data science [#mds-paper]_. It supports semantic annotation, interoperability, data integration, and knowledge graph construction across heterogeneous materials science datasets and digital platforms [#mds-paper]_. By providing a standardized framework, MDS-Onto facilitates data sharing, cross-domain integration, and reuse of materials and data science knowledge [#mds-paper]_. **Example Usage**: -Annotate a materials database with MDS-Onto terms to specify material properties, processes, and data, enabling semantic search and integration with materials informatics platforms. +Annotate a materials database with MDS-Onto terms to specify material properties, manufacturing or synthesis processes, characterization data, exposure conditions, photovoltaic module information, or geospatial metadata, enabling semantic search and integration with materials informatics platforms [#mds-paper]_. Metrics & Statistics @@ -141,3 +139,12 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- +.. [#mds-paper] Rajamohan, B. P., Bradley, A. C. H., Tran, V. D., et al. 2025. + "Materials Data Science Ontology(MDS-Onto): Unifying Domain Knowledge in Materials and Applied Data Science." + *Scientific Data*, 12, 628. + DOI: 10.1038/s41597-025-04938-5. + Available at: + `https://www.nature.com/articles/s41597-025-04938-5 `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/mechanicaltesting.rst b/docs/source/benchmarking/materials_science_and_engineering/mechanicaltesting.rst index 34e34b3..d26b816 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/mechanicaltesting.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/mechanicaltesting.rst @@ -23,14 +23,12 @@ Mechanical Testing Ontology (MechanicalTesting) ======================================================================================================== -The Mechanical Testing Ontology (MechanicalTesting) is a domain ontology developed to represent knowledge in the field of mechanical testing, based on the Elementary Multiperspective Material Ontology (EMMO). It provides a structured vocabulary for describing mechanical testing methods, equipment, and results, supporting both experimental and computational research in materials science. +The Mechanical Testing Ontology (MechanicalTesting) is a domain ontology developed to represent knowledge in the field of mechanical testing and is built on top of the Elementary Multiperspective Material Ontology (EMMO) [#mechanicaltesting-github]_ [#morgado2020]_. It provides a structured vocabulary for describing mechanical testing concepts, supporting semantic representation of experiments, models, software, and data in materials science [#morgado2020]_. -The ontology employs a class-based modeling approach, defining classes for different types of mechanical tests, equipment, and results, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. MechanicalTesting supports the integration of data from experimental studies and simulations, promoting interoperability and data-driven research in mechanical testing. - -Typical applications of MechanicalTesting include the development of new testing methods, the optimization of testing procedures, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, MechanicalTesting enhances collaboration and innovation in the field of mechanical testing. +The ontology supports semantic annotation, data integration, interoperability, and sharing of mechanical-testing information across materials science workflows [#mechanicaltesting-github]_ [#morgado2020]_. It is described as an EMMO-based domain ontology and was developed as part of efforts to create EMMO-compliant domain ontologies for materials science [#mechanicaltesting-github]_ [#morgado2020]_. By providing a standardized semantic framework, MechanicalTesting supports knowledge representation, data retrieval, and reuse in mechanical testing and digital-twin-related applications [#morgado2020]_. **Example Usage**: -Annotate a mechanical testing dataset with MechanicalTesting terms to specify test types, equipment, and results, enabling semantic search and integration with materials informatics platforms. +Annotate a mechanical testing dataset with MechanicalTesting terms to specify mechanical testing methods, experiment-related information, test data, models, software, and results, enabling semantic search and integration with materials informatics platforms [#mechanicaltesting-github]_ [#morgado2020]_. Metrics & Statistics -------------------------- @@ -139,3 +137,18 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#mechanicaltesting-github] EMMO-repo. n.d. + "Domain: mechanical-testing." + GitHub repository. + Available at: + `https://github.com/emmo-repo/domain-mechanical-testing `_ + +.. [#morgado2020] Morgado, J. F., Ghedini, E., Goldbeck, G., Hashibon, A., Schmitz, G. J., Friis, J., and de Baas, A. F. 2020. + "Mechanical testing ontology for digital-twins: A roadmap based on EMMO." + *International Workshop on Semantic Digital Twins (SeDiT 2020)*. + Available at: + `https://publica.fraunhofer.de/entities/publication/cda015f5-a194-4e82-8b5f-cba29b43cf5b `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/microstructures.rst b/docs/source/benchmarking/materials_science_and_engineering/microstructures.rst index 95c21d5..3c422d7 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/microstructures.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/microstructures.rst @@ -23,10 +23,12 @@ EMMO-based ontology for microstructures (MicroStructures) ======================================================================================================== -The EMMO-based ontology for microstructures (MicroStructures) is a domain ontology designed to represent metallic microstructures, including their composition, particles (both stable and metastable), grains, subgrains, grain boundaries, particle free zones (PFZs), texture, and dislocations. The ontology provides a structured vocabulary for describing microstructure features, supporting both microstructure modeling and experimental characterization. MicroStructures enables semantic annotation of microstructural data, facilitating interoperability, data integration, and advanced analysis in materials science and engineering. The ontology is designed for extensibility, allowing researchers to describe new microstructure types, characterization techniques, and material systems. By providing a standardized framework, MicroStructures supports cross-study comparison, microstructure-property correlation, and knowledge sharing in materials research. The ontology is actively maintained and extended to incorporate new concepts and requirements from the materials science community. +The EMMO-based ontology for microstructures (MicroStructures) is a domain ontology designed to represent metallic microstructures and related materials science concepts [#microstructures-emmc]_. It provides a structured framework for describing microstructure information, including microstructure features, materials, properties, processes, characterisation data, and modelling workflows [#microstructures-emmc]_. + +MicroStructures supports semantic annotation and integration of microstructural data, helping connect experimental characterisation, data processing, and through-scale or through-process modelling in materials science [#microstructures-emmc]_. By providing an EMMO-based framework, it supports interoperability, knowledge sharing, and more consistent representation of microstructure-related information across research and engineering workflows [#microstructures-emmc]_. **Example Usage**: -Annotate a microstructure characterization dataset with MicroStructures terms to specify grain size distribution, particle types, texture, and dislocation density, enabling semantic search and integration with materials databases and modeling tools. +Annotate a microstructure characterisation dataset with MicroStructures terms to describe microstructure features, material properties, characterisation results, processing history, and modelling information, enabling semantic search and integration with materials databases and modelling tools [#microstructures-emmc]_. Metrics & Statistics -------------------------- @@ -135,3 +137,11 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +--------- + +.. [#microstructures-emmc] European Materials Modelling Council. 2024. + "TG Microstructure domain ontology." + Available at: + `https://emmc.eu/tg-microstructure-domain-ontology/ `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/mmo.rst b/docs/source/benchmarking/materials_science_and_engineering/mmo.rst index a2fdcb3..f0bb5fe 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/mmo.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/mmo.rst @@ -23,14 +23,12 @@ Materials Mechanics Ontology (MMO) ======================================================================================================== -The Materials Mechanics Ontology (MMO) is an application-level ontology developed to support named entity recognition tasks in the materials fatigue domain. It provides a structured vocabulary for describing concepts related to materials mechanics, including crystallographic defects, microstructural entities, and materials fatigue. MMO is designed to facilitate the integration of materials mechanics data, supporting both theoretical and experimental research in materials science. +The Materials Mechanics Ontology (MMO) is an application-level ontology developed to support ontology-based named entity recognition in the materials mechanics and materials fatigue domain [#mmo-paper]_ [#mmo-gitlab]_. It provides a structured vocabulary for representing mechanics-of-materials concepts, including crystallographic defects, microstructural entities, material properties, damage mechanisms, fatigue concepts, specimens, tests, and processing-related entities [#mmo-paper]_. -The ontology employs a class-based modeling approach, defining classes for different types of materials, defects, and microstructures, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. MMO supports the integration of data from various sources, promoting interoperability and data-driven research in materials mechanics. - -Typical applications of MMO include the development of new materials with specific mechanical properties, the optimization of materials for fatigue resistance, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, MMO enhances collaboration and innovation in the field of materials mechanics. +MMO links ontology concepts with textual entities from scientific literature, enabling fine-grained and coarse-grained NER datasets for materials mechanics text mining [#mmo-paper]_. The ontology is mapped to PMDco and includes concepts relevant to composition-process-microstructure-property relationships, supporting semantic annotation, data standardization, information extraction, and knowledge graph generation from materials science literature [#mmo-paper]_. **Example Usage**: -Annotate a materials mechanics dataset with MMO terms to specify defect types, microstructural features, and fatigue properties, enabling semantic search and integration with materials informatics platforms. +Annotate a materials fatigue paper or dataset with MMO terms to specify defects, microstructural features, fatigue properties, mechanical tests, damage mechanisms, and processing conditions, enabling ontology-based named entity recognition, semantic search, and integration with materials informatics workflows [#mmo-paper]_ [#mmo-gitlab]_. Metrics & Statistics -------------------------- @@ -139,3 +137,19 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#mmo-paper] Durmaz, A. R., Thomas, A., Mishra, L., Murthy, R. N., and Straub, T. 2024. + "An ontology-based text mining dataset for extraction of process-structure-property entities." + *Scientific Data*, 11, Article 1112. + DOI: 10.1038/s41597-024-03926-5. + Available at: + `https://www.nature.com/articles/s41597-024-03926-5 `_ + +.. [#mmo-gitlab] Fraunhofer IWM Micro Mechanics Public. n.d. + "Materials Mechanics Ontology." + GitLab repository. + Available at: + `https://gitlab.cc-asp.fraunhofer.de/iwm-micro-mechanics-public/ontologies/materials-mechanics-ontology `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/molbrinell.rst b/docs/source/benchmarking/materials_science_and_engineering/molbrinell.rst index b58d731..cf72208 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/molbrinell.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/molbrinell.rst @@ -23,14 +23,12 @@ MatoLab Brinell Test Ontology (MOL_BRINELL) ======================================================================================================== -The MatoLab Brinell Test Ontology (MOL_BRINELL) is a domain ontology developed to describe the Brinell hardness testing process. It provides a structured vocabulary for representing the testing methods, equipment, and results, supporting both experimental and computational research in materials testing. +The MatoLab Brinell Test Ontology (MOL_BRINELL) is an application-level ontology developed to represent the Brinell hardness testing process [#molbrinell-matportal]_ [#bto-github]_. It provides a structured vocabulary for describing Brinell testing processes, testing equipment requirements, test-piece characteristics, related testing parameters, and measurement procedures according to the DIN EN ISO 6506-1 standard [#bto-github]_. -The ontology employs a class-based modeling approach, defining classes for different types of tests, equipment, and results, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. MOL_BRINELL supports the integration of data from experimental studies and simulations, promoting interoperability and data-driven research in materials testing. - -Typical applications of MOL_BRINELL include the development of new testing methods, the optimization of testing procedures, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, MOL_BRINELL enhances collaboration and innovation in the field of materials testing. +MOL_BRINELL supports semantic annotation of Brinell hardness testing data, enabling interoperability, data integration, semantic search, and reuse in materials testing and materials informatics workflows [#molbrinell-matportal]_ [#bto-github]_. The Brinell Test Ontology repository also documents multiple ontology versions developed with different top-level ontology alignments, including BFO+CCO, EMMO+CHAMEO, PROV-O+PMDco, and BFO+IOF [#bto-github]_. **Example Usage**: -Annotate a Brinell hardness testing dataset with MOL_BRINELL terms to specify test types, equipment, and results, enabling semantic search and integration with materials informatics platforms. +Annotate a Brinell hardness testing dataset with MOL_BRINELL terms to specify the Brinell testing process, testing equipment, test-piece characteristics, testing parameters, measurement procedure, and hardness-test results, enabling semantic search and integration with materials informatics platforms [#molbrinell-matportal]_ [#bto-github]_. Metrics & Statistics -------------------------- @@ -139,3 +137,18 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#bto-github] Nasrabadi, H. B. n.d. + "Brinell-Test-Ontology-BTO." + GitHub repository. + Available at: + `https://github.com/HosseinBeygiNasrabadi/Brinell-Test-Ontology-BTO- `_ + +.. [#molbrinell-matportal] MatPortal. 2022. + "MatoLab Brinell Test Ontology (MOL_BRINELL)." + Ontology registry entry. + Available at: + `https://matportal.org/ontologies/MOL_BRINELL `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/moltensile.rst b/docs/source/benchmarking/materials_science_and_engineering/moltensile.rst index bf9d364..ca796ea 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/moltensile.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/moltensile.rst @@ -23,12 +23,12 @@ Matolab Tensile Test Ontology (MOL_TENSILE) ======================================================================================================== +The Matolab Tensile Test Ontology (MOL_TENSILE) is a domain ontology developed to describe the tensile test process [#moltensile-matportal]_. It provides a structured vocabulary for representing tensile testing methods, equipment, test-related entities, and results in materials testing workflows [#moltensile-matportal]_. -The Matolab Tensile Test Ontology (MOL_TENSILE) is a domain ontology developed to describe the tensile test process. It provides a structured vocabulary for representing the testing methods, equipment, and results, supporting both experimental and computational research in materials testing. +MOL_TENSILE supports semantic annotation and integration of tensile testing data, enabling interoperability, semantic search, and reuse in materials informatics platforms [#moltensile-matportal]_. By providing a standardized vocabulary for tensile testing, the ontology helps organize experimental testing information and supports knowledge sharing in materials testing applications [#moltensile-matportal]_. -The ontology employs a class-based modeling approach, defining classes for different types of tests, equipment, and results, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. MOL_TENSILE supports the integration of data from experimental studies and simulations, promoting interoperability and data-driven research in materials testing. - -Typical applications of MOL_TENSILE include the development of new testing methods, the optimization of testing procedures, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, MOL_TENSILE enhances collaboration and innovation in the field of materials testing. +**Example Usage**: +Annotate a tensile testing dataset with MOL_TENSILE terms to specify test types, equipment, testing conditions, and results, enabling semantic search and integration with materials informatics platforms [#moltensile-matportal]_. Metrics & Statistics -------------------------- @@ -138,4 +138,11 @@ Use the following code to import this ontology programmatically: taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations -Annotate a tensile testing dataset with MOL_TENSILE terms to specify test types, equipment, and results, enabling semantic search and integration with materials informatics platforms. +References +---------- + +.. [#moltensile-matportal] MatPortal. 2021. + "Matolab Tensile Test Ontology (MOL_TENSILE)." + Ontology registry entry. + Available at: + `https://matportal.org/ontologies/MOL_TENSILE `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/mseo.rst b/docs/source/benchmarking/materials_science_and_engineering/mseo.rst index 71936f9..576c573 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/mseo.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/mseo.rst @@ -23,14 +23,12 @@ Materials Science and Engineering Ontology (MSEO) ======================================================================================================== -The Materials Science and Engineering Ontology (MSEO) is a domain ontology developed to represent experiments and resulting data in materials science and engineering. It provides a structured vocabulary for describing materials, processes, and data, supporting both theoretical and experimental research in materials science. +The Materials Science and Engineering Ontology (MSEO) is a domain ontology developed to represent experiments and resulting data in materials science and engineering [#mseo-matportal]_ [#mseo-github]_. It provides a structured vocabulary for describing materials, processes, properties, software, identifiers, data transformations, and experiment-related information [#mseo-github]_. -The ontology employs a class-based modeling approach, defining classes for different types of materials, processes, and data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. MSEO supports the integration of data from various sources, promoting interoperability and data-driven research in materials science. - -Typical applications of MSEO include the integration of materials data from various sources, the development of new materials with specific properties, and the optimization of manufacturing processes. By providing a standardized vocabulary and framework, MSEO enhances collaboration and innovation in the field of materials science. +MSEO supports the creation of machine-readable and human-readable semantic data, enabling interoperability, semantic annotation, data integration, retrieval, and reuse across materials science workflows [#mseo-matportal]_ [#mseo-github]_. By providing a standardized vocabulary, MSEO helps organize materials science experiments and supports knowledge sharing in materials informatics platforms [#mseo-github]_. **Example Usage**: -Annotate a materials science experiment with MSEO terms to specify material types, processes, and data, enabling semantic search and integration with materials informatics platforms. +Annotate a materials science experiment with MSEO terms to specify material types, experimental processes, measured properties, software, identifiers, data transformations, and resulting data, enabling semantic search and integration with materials informatics platforms [#mseo-matportal]_ [#mseo-github]_. Metrics & Statistics -------------------------- @@ -139,3 +137,18 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#mseo-matportal] MatPortal. 2023. + "Material Science and Engineering Ontology." + Ontology registry entry. + Available at: + `https://matportal.org/ontologies/MSEO `_ + +.. [#mseo-github] Mat-O-Lab. n.d. + "MSEO: Repository of Materials Science and Engineering Ontology." + GitHub repository. + Available at: + `https://github.com/Mat-O-Lab/MSEO `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/msle.rst b/docs/source/benchmarking/materials_science_and_engineering/msle.rst index d4b8ce2..40e4687 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/msle.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/msle.rst @@ -23,14 +23,12 @@ Material Science Lab Equipment Ontology (MSLE) ======================================================================================================== -The Material Science Lab Equipment Ontology (MSLE) is a domain ontology developed to describe laboratory equipment used in materials science. It provides a structured vocabulary for representing equipment, processes, and data, supporting both experimental and computational research in materials science. +The Material Science Lab Equipment Ontology (MSLE) is a domain ontology developed to describe laboratory equipment used in materials science, with a focus on large-scale devices for materials characterization [#msle-paper]_. It provides a structured vocabulary for representing laboratory equipment, equipment types, device specifications, acronyms, alternative labels, and characterization-related information [#msle-paper]_. -The ontology employs a class-based modeling approach, defining classes for different types of equipment, processes, and data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. MSLE supports the integration of data from various sources, promoting interoperability and data-driven research in materials science. - -Typical applications of MSLE include the integration of laboratory data from various sources, the development of new materials with specific properties, and the optimization of laboratory processes. By providing a standardized vocabulary and framework, MSLE enhances collaboration and innovation in the field of materials science. +MSLE supports semantic annotation, interoperability, data integration, querying, and reuse of laboratory-equipment information in materials science workflows [#msle-paper]_. The ontology integrates relevant semantic web resources such as the Semantic Sensor Network ontology, Material Vocabulary, SKOS, and SHACL to represent and validate equipment-related knowledge [#msle-paper]_. By providing a standardized vocabulary, MSLE supports better organization, discovery, and integration of materials science laboratory equipment data [#msle-paper]_. **Example Usage**: -Annotate a laboratory equipment dataset with MSLE terms to specify equipment types, processes, and data, enabling semantic search and integration with materials informatics platforms. +Annotate a laboratory equipment dataset with MSLE terms to specify equipment types, device specifications, acronyms, alternative names, and characterization-device information, enabling semantic search and integration with materials informatics platforms [#msle-paper]_. Metrics & Statistics -------------------------- @@ -139,3 +137,13 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#msle-paper] Jalali, M., Mail, M., Aversa, R., and Kübel, C. 2023. + "MSLE: An ontology for Materials Science Laboratory Equipment. Large-Scale Devices for Materials Characterization." + *Materials Today Communications*, 35, 105532. + DOI: 10.1016/j.mtcomm.2023.105532. + Available at: + `https://arxiv.org/abs/2308.07325 `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/nanomine.rst b/docs/source/benchmarking/materials_science_and_engineering/nanomine.rst index d6893b9..dc27040 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/nanomine.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/nanomine.rst @@ -23,14 +23,12 @@ NanoMine Ontology (NanoMine) ======================================================================================================== -The NanoMine Ontology is a domain ontology developed to support research in polymer nanocomposites. It provides a structured vocabulary for representing the inter-relationships between different materials processing methods, compositions, and resulting material properties. NanoMine enables researchers to develop and test hypotheses about how these inter-relationships affect material performance, supporting both experimental and computational research in materials science. +The NanoMine Ontology is a domain ontology developed to support research in polymer nanocomposites [#nanomine-github]_ [#nanomine-paper]_. It provides a structured vocabulary for representing relationships between polymer nanocomposite composition, processing methods, microstructure, characterization data, and resulting material properties [#nanomine-paper]_. -The ontology employs a class-based modeling approach, defining classes for different types of materials, processes, and properties, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. NanoMine supports the integration of data from various sources, promoting interoperability and data-driven research in polymer nanocomposites. - -Typical applications of NanoMine include the development of new polymer nanocomposites with specific properties, the optimization of processing methods, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, NanoMine enhances collaboration and innovation in the field of polymer nanocomposites. +NanoMine supports semantic annotation, data integration, search, reuse, and analysis of polymer nanocomposite data [#nanomine-github]_ [#nanomine-paper]_. The ontology helps researchers explore processing-structure-property relationships and supports hypothesis development about how material composition and processing conditions affect nanocomposite performance [#nanomine-paper]_. By providing a standardized semantic framework, NanoMine facilitates interoperability, knowledge sharing, and data-driven research in polymer nanocomposites [#nanomine-github]_ [#nanomine-paper]_. **Example Usage**: -Annotate a polymer nanocomposite dataset with NanoMine terms to specify material types, processing methods, and properties, enabling semantic search and integration with materials informatics platforms. +Annotate a polymer nanocomposite dataset with NanoMine terms to specify polymer matrix, filler material, filler loading, processing method, characterization technique, microstructure information, and measured properties, enabling semantic search and integration with materials informatics platforms [#nanomine-github]_ [#nanomine-paper]_. Metrics & Statistics -------------------------- @@ -139,3 +137,19 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#nanomine-github] Tetherless World. n.d. + "nanomine-ontology: Ontology and related data support for the Nanomine project." + GitHub repository. + Available at: + `https://github.com/tetherless-world/nanomine-ontology `_ + +.. [#nanomine-paper] Zhao, H., Wang, Y., Lin, A., Hu, B., Yan, R., McCusker, J., Chen, W., McGuinness, D. L., Schadler, L., and Brinson, L. C. 2018. + "NanoMine schema: An extensible data representation for polymer nanocomposites." + *APL Materials*, 6, 111108. + DOI: 10.1063/1.5046839. + Available at: + `https://pubs.aip.org/aip/apm/article/6/11/111108/121743/NanoMine-schema-An-extensible-data-representation `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/oiecharacterisation.rst b/docs/source/benchmarking/materials_science_and_engineering/oiecharacterisation.rst index eac0198..8ba7eee 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/oiecharacterisation.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/oiecharacterisation.rst @@ -23,14 +23,12 @@ Open Innovation Environment Characterisation (OIECharacterisation) ======================================================================================================== -The Open Innovation Environment Characterisation (OIECharacterisation) ontology is an EMMO-compliant, domain-level ontology developed to represent characterization methods in materials science. It provides a structured vocabulary for describing characterization techniques, equipment, and data, supporting both experimental and computational research in materials science. +The Open Innovation Environment Characterisation (OIECharacterisation) ontology is an EMMO-compliant, domain-level ontology developed to represent characterisation methods in materials science [#oiecharacterisation-github]_. It provides a structured vocabulary for describing characterisation-method concepts and supports their alignment with the wider EMMO ontology ecosystem [#oiecharacterisation-github]_. -The ontology employs a class-based modeling approach, defining classes for different types of characterization methods, equipment, and data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. OIECharacterisation supports the integration of data from various sources, promoting interoperability and data-driven research in materials characterization. - -Typical applications of OIECharacterisation include the development of new characterization methods, the optimization of characterization procedures, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, OIECharacterisation enhances collaboration and innovation in the field of materials characterization. +OIECharacterisation is part of the Open Innovation Environment (OIE) ontology set, which covers characterisation methods, manufacturing processes, materials, models, and software products [#oiecharacterisation-github]_. The OIE ontologies are aligned with EMMO and were developed in the context of the OYSTER project [#oiecharacterisation-github]_. By providing a standardized semantic framework, OIECharacterisation supports semantic annotation, interoperability, data integration, and reuse of characterisation-related materials science information [#oiecharacterisation-github]_. **Example Usage**: -Annotate a characterization dataset with OIECharacterisation terms to specify characterization methods, equipment, and data, enabling semantic search and integration with materials informatics platforms. +Annotate a materials characterisation dataset with OIECharacterisation terms to specify characterisation methods, related measurement information, and links to EMMO-aligned materials science concepts, enabling semantic search and integration with materials informatics platforms [#oiecharacterisation-github]_. Metrics & Statistics -------------------------- @@ -139,3 +137,12 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#oiecharacterisation-github] EMMO-repo. n.d. + "Open Innovation Environment (OIE) domain ontologies." + GitHub repository. + Available at: + `https://github.com/emmo-repo/OIE-Ontologies `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/oiemanufacturing.rst b/docs/source/benchmarking/materials_science_and_engineering/oiemanufacturing.rst index 3343033..e3f7ba5 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/oiemanufacturing.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/oiemanufacturing.rst @@ -23,14 +23,12 @@ Open Innovation Environment Manufacturing (OIEManufacturing) ======================================================================================================== -The Open Innovation Environment Manufacturing (OIEManufacturing) ontology is a domain-level ontology developed to represent manufacturing processes in materials science. It provides a structured vocabulary for describing manufacturing methods, equipment, and data, supporting both experimental and computational research in materials science. +The Open Innovation Environment Manufacturing (OIEManufacturing) ontology is a domain-level ontology developed to represent manufacturing processes in materials science [#oiemanufacturing-github]_. It provides a structured vocabulary for describing manufacturing-related concepts and supports their alignment with the wider Open Innovation Environment ontology set [#oiemanufacturing-github]_. -The ontology employs a class-based modeling approach, defining classes for different types of manufacturing methods, equipment, and data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. OIEManufacturing supports the integration of data from various sources, promoting interoperability and data-driven research in materials manufacturing. - -Typical applications of OIEManufacturing include the development of new manufacturing methods, the optimization of manufacturing processes, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, OIEManufacturing enhances collaboration and innovation in the field of materials manufacturing. +OIEManufacturing is part of the Open Innovation Environment ontology collection, which includes domain-level ontologies for characterisation methods, manufacturing processes, materials, models, and software products [#oiemanufacturing-github]_. The ontology supports semantic annotation, interoperability, data integration, and reuse of manufacturing-related materials science information [#oiemanufacturing-github]_. By providing a standardized semantic framework, OIEManufacturing helps organize manufacturing knowledge for semantic search and integration with materials informatics platforms [#oiemanufacturing-github]_. **Example Usage**: -Annotate a manufacturing dataset with OIEManufacturing terms to specify manufacturing methods, equipment, and data, enabling semantic search and integration with materials informatics platforms. +Annotate a manufacturing dataset with OIEManufacturing terms to specify manufacturing processes, manufacturing methods, and related materials science information, enabling semantic search and integration with materials informatics platforms [#oiemanufacturing-github]_. Metrics & Statistics -------------------------- @@ -139,3 +137,12 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#oiemanufacturing-github] EMMO-repo. n.d. + "Open Innovation Environment (OIE) domain ontologies." + GitHub repository. + Available at: + `https://github.com/emmo-repo/OIE-Ontologies `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/oiematerials.rst b/docs/source/benchmarking/materials_science_and_engineering/oiematerials.rst index 6225516..79a7c8d 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/oiematerials.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/oiematerials.rst @@ -23,15 +23,12 @@ Open Innovation Environment Materials (OIEMaterials) ======================================================================================================== +The Open Innovation Environment Materials (OIEMaterials) ontology is a domain-level ontology developed to represent materials-related concepts in materials science [#oiematerials-github]_. It provides a structured vocabulary for describing materials and supports their alignment with the wider Open Innovation Environment ontology set [#oiematerials-github]_. -The Open Innovation Environment Materials (OIEMaterials) ontology is a domain-level ontology developed to represent materials in materials science. It provides a structured vocabulary for describing materials, their properties, and data, supporting both experimental and computational research in materials science. - -The ontology employs a class-based modeling approach, defining classes for different types of materials, properties, and data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. OIEMaterials supports the integration of data from various sources, promoting interoperability and data-driven research in materials science. - -Typical applications of OIEMaterials include the development of new materials with specific properties, the optimization of material properties, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, OIEMaterials enhances collaboration and innovation in the field of materials science. +OIEMaterials is part of the Open Innovation Environment ontology collection, which includes EMMO-compliant, domain-level ontologies for characterisation methods, manufacturing processes, materials, models, and software products [#oiematerials-github]_. The ontology supports semantic annotation, interoperability, data integration, and reuse of materials-related information across materials science and informatics workflows [#oiematerials-github]_. **Example Usage**: -Annotate a materials dataset with OIEMaterials terms to specify material types, properties, and data, enabling semantic search and integration with materials informatics platforms. +Annotate a materials dataset with OIEMaterials terms to specify materials and related materials science information, enabling semantic search and integration with materials informatics platforms [#oiematerials-github]_. Metrics & Statistics -------------------------- @@ -140,3 +137,12 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#oiematerials-github] EMMO-repo. n.d. + "Open Innovation Environment (OIE) domain ontologies." + GitHub repository. + Available at: + `https://github.com/emmo-repo/OIE-Ontologies `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/oiemodels.rst b/docs/source/benchmarking/materials_science_and_engineering/oiemodels.rst index fe931d1..b2288a5 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/oiemodels.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/oiemodels.rst @@ -23,14 +23,12 @@ Open Innovation Environment Models (OIEModels) ======================================================================================================== -The Open Innovation Environment Models (OIEModels) ontology is a domain-level ontology developed to represent models in materials science. It provides a structured vocabulary for describing models, their properties, and data, supporting both experimental and computational research in materials science. +The Open Innovation Environment Models (OIEModels) ontology is a domain-level ontology developed to represent model-related concepts in materials science [#oiemodels-github]_. It provides a structured vocabulary for describing models and supports their alignment with the wider Open Innovation Environment ontology set [#oiemodels-github]_. -The ontology employs a class-based modeling approach, defining classes for different types of models, properties, and data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. OIEModels supports the integration of data from various sources, promoting interoperability and data-driven research in materials modeling. - -Typical applications of OIEModels include the development of new models with specific properties, the optimization of model properties, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, OIEModels enhances collaboration and innovation in the field of materials modeling. +OIEModels is part of the Open Innovation Environment ontology collection, which includes EMMO-compliant, domain-level ontologies for characterisation methods, manufacturing processes, materials, models, and software products [#oiemodels-github]_. The ontology supports semantic annotation, interoperability, data integration, and reuse of model-related information across materials modelling and materials informatics workflows [#oiemodels-github]_. **Example Usage**: -Annotate a modeling dataset with OIEModels terms to specify model types, properties, and data, enabling semantic search and integration with materials informatics platforms. +Annotate a materials modelling dataset with OIEModels terms to specify model types, model-related information, and links to materials science concepts, enabling semantic search and integration with materials informatics platforms [#oiemodels-github]_. Metrics & Statistics -------------------------- @@ -139,3 +137,12 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#oiemodels-github] EMMO-repo. n.d. + "Open Innovation Environment (OIE) domain ontologies." + GitHub repository. + Available at: + `https://github.com/emmo-repo/OIE-Ontologies `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/oiesoftware.rst b/docs/source/benchmarking/materials_science_and_engineering/oiesoftware.rst index 4271682..97edf12 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/oiesoftware.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/oiesoftware.rst @@ -23,10 +23,12 @@ Open Innovation Environment Software (OIESoftware) ======================================================================================================== -The Open Innovation Environment Software Ontology (OIESoftware) is an EMMO-compliant, domain-level ontology designed to represent software products and their roles in materials science and engineering. OIESoftware provides a structured vocabulary for describing software tools, simulation codes, data analysis packages, and their relationships to computational workflows and experimental processes. The ontology supports semantic annotation of software metadata, enabling interoperability, data integration, and advanced analytics across research platforms and digital infrastructures. OIESoftware is designed for extensibility, allowing researchers and developers to describe new software products, functionalities, and application domains. By providing a standardized framework, OIESoftware facilitates software discovery, workflow automation, and knowledge sharing in computational materials science. The ontology is actively maintained and extended to incorporate new software technologies and research requirements. +The Open Innovation Environment Software Ontology (OIESoftware) is an EMMO-compliant, domain-level ontology developed to represent software products in materials science and engineering [#oiesoftware-github]_. It provides a structured vocabulary for describing software-related concepts and supports their alignment with the wider Open Innovation Environment ontology set [#oiesoftware-github]_. + +OIESoftware is part of the Open Innovation Environment ontology collection, which includes domain-level ontologies for characterisation methods, manufacturing processes, materials, models, and software products [#oiesoftware-github]_. The ontology supports semantic annotation, interoperability, data integration, and reuse of software-related information across materials science workflows and digital research infrastructures [#oiesoftware-github]_. **Example Usage**: -Annotate a computational workflow with OIESoftware terms to specify the simulation codes used (e.g., "LAMMPS", "VASP"), their input/output formats, and their roles in the workflow, enabling semantic search and integration with research data management systems. +Annotate a computational materials science workflow with OIESoftware terms to specify the software products used, their roles in the workflow, and their links to related models, materials, or characterisation/manufacturing processes, enabling semantic search and integration with research data management systems [#oiesoftware-github]_. Metrics & Statistics -------------------------- @@ -135,3 +137,12 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#oiesoftware-github] EMMO-repo. n.d. + "Open Innovation Environment (OIE) domain ontologies." + GitHub repository. + Available at: + `https://github.com/emmo-repo/OIE-Ontologies `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/ontocape.rst b/docs/source/benchmarking/materials_science_and_engineering/ontocape.rst index 66ce42d..9b6e462 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/ontocape.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/ontocape.rst @@ -1,69 +1,134 @@ +.. sidebar:: + + .. list-table:: **Ontology Card** + :header-rows: 0 + + * - **Domain** + - Materials Science and Engineering + * - **Category** + - Manufacturing + * - **Current Version** + - 2.0 + * - **Last Updated** + - None + * - **Creator** + - RWTH Aachen University + * - **License** + - GNU General Public License + * - **Format** + - OWL + * - **Download** + - `Download Ontology of Computer-Aided Process Engineering (OntoCAPE) `_ + Ontology of Computer-Aided Process Engineering (OntoCAPE) -======================================================================================================================== - -Overview --------- -OntoCAPE is a large-scale ontology for the domain of Computer Aided Process Engineering (CAPE). Represented in a formal, -machine-interpretable ontology language, OntoCAPE captures consensual knowledge of the process engineering domain -in a generic way such that it can be reused and shared by groups of people and across software systems. -On the basis of OntoCAPE, novel software support for various engineering activities can be developed; -possible applications include the systematic management and retrieval of simulation models and design documents, -electronic procurement of plant equipment, mathematical modeling, -as well as the integration of design data from distributed sources. - -:Domain: Materials Science & Engineering -:Category: Manufacturing -:Current Version: 2.0 -:Last Updated: None -:Creator: RWTH Aachen University -:License: GNU General Public License. -:Format: OWL -:Download: `Ontology of Computer-Aided Process Engineering (OntoCAPE) Homepage `_ - -Graph Metrics -------------- - - **Total Nodes**: 11 - - **Total Edges**: 10 - - **Root Nodes**: 1 - - **Leaf Nodes**: 10 - -Knowledge coverage ------------------- - - Classes: 0 - - Individuals: 0 - - Properties: 0 - -Hierarchical metrics --------------------- - - **Maximum Depth**: 1 - - **Minimum Depth**: 0 - - **Average Depth**: 0.91 - - **Depth Variance**: 0.08 - -Breadth metrics ------------------- - - **Maximum Breadth**: 10 - - **Minimum Breadth**: 1 - - **Average Breadth**: 5.50 - - **Breadth Variance**: 20.25 - -Dataset Statistics ------------------- -Generated Benchmarks: - - **Term Types**: 0 - - **Taxonomic Relations**: 0 - - **Non-taxonomic Relations**: 0 - - **Average Terms per Type**: 0.00 +======================================================================================================== + +The Ontology of Computer-Aided Process Engineering (OntoCAPE) is a large-scale ontology for the domain of Computer-Aided Process Engineering (CAPE) [#ontocape-homepage]_. It is represented in a formal, machine-interpretable ontology language and captures consensual knowledge of the process engineering domain in a generic way so that it can be reused and shared across groups of people and software systems [#ontocape-homepage]_. + +OntoCAPE supports the development of software tools for engineering activities such as the systematic management and retrieval of simulation models and design documents, electronic procurement of plant equipment, mathematical modelling, and integration of design data from distributed sources [#ontocape-homepage]_. By providing a standardized semantic framework, OntoCAPE facilitates interoperability, knowledge sharing, and data integration in process engineering and manufacturing-related workflows [#ontocape-homepage]_. + +**Example Usage**: +Annotate a process engineering project with OntoCAPE terms to specify process types, plant equipment, simulation models, mathematical models, and design data, enabling semantic search and integration with process informatics platforms [#ontocape-homepage]_. + +Metrics & Statistics +-------------------------- + +.. tab:: Graph + + + .. list-table:: Graph Statistics + :widths: 50 50 + :header-rows: 0 + + * - **Total Nodes** + - 11 + * - **Total Edges** + - 10 + * - **Root Nodes** + - 1 + * - **Leaf Nodes** + - 10 + :: + + +.. tab:: Coverage + + + .. list-table:: Knowledge Coverage Statistics + :widths: 50 50 + :header-rows: 0 + + * - **Classes** + - 0 + * - **Individuals** + - 0 + * - **Properties** + - 0 + + :: + +.. tab:: Hierarchy + + + .. list-table:: Hierarchical Metrics + :widths: 50 50 + :header-rows: 0 + + * - **Maximum Depth** + - 1 + * - **Minimum Depth** + - 0 + * - **Average Depth** + - 0.91 + * - **Depth Variance** + - 0.08 + :: + + +.. tab:: Breadth + + + .. list-table:: Breadth Metrics + :widths: 50 50 + :header-rows: 0 + + * - **Maximum Breadth** + - 10 + * - **Minimum Breadth** + - 1 + * - **Average Breadth** + - 5.50 + * - **Breadth Variance** + - 20.25 + :: + +.. tab:: LLMs4OL + + + .. list-table:: LLMs4OL Dataset Statistics + :widths: 50 50 + :header-rows: 0 + + * - **Term Types** + - 0 + * - **Taxonomic Relations** + - 179 + * - **Non-taxonomic Relations** + - 0 + * - **Average Terms per Type** + - 0.00 + :: Usage Example -------------- +---------------- +Use the following code to import this ontology programmatically: + .. code-block:: python from ontolearner.ontology import OntoCAPE - # Initialize and load ontology ontology = OntoCAPE() - ontology.load("path/to/ontology.owl") + ontology.load("path/to/OntoCAPE-ontology.owl") # Extract datasets data = ontology.extract() @@ -72,4 +137,11 @@ Usage Example term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations -Annotate a process engineering project with OntoCAPE terms to specify process types, equipment, and data, enabling semantic search and integration with process informatics platforms. + +References +---------- + +.. [#ontocape-homepage] RWTH Aachen University. n.d. + "Ontology of Computer-Aided Process Engineering (OntoCAPE)." + Available at: + `https://www.avt.rwth-aachen.de/cms/avt/forschung/sonstiges/software/~ipts/ontocape/?lidx=1 `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/ontorule.rst b/docs/source/benchmarking/materials_science_and_engineering/ontorule.rst index ebbc523..d89f50a 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/ontorule.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/ontorule.rst @@ -23,10 +23,12 @@ Ontology for the Steel Domain (ONTORULE) ======================================================================================================== -The Ontology for the Steel Domain (ONTORULE) is a domain ontology developed for the steel industry use case, providing a structured vocabulary for representing concepts, attributes, and relationships relevant to steel production and processing. ONTORULE supports the semantic annotation of steel industry data, including materials, processes, equipment, and quality attributes, enabling data integration and knowledge sharing across manufacturing systems. The ontology is designed for extensibility and can be adapted to represent new steel grades, production methods, and regulatory requirements. ONTORULE facilitates advanced analytics, process optimization, and compliance management in the steel industry by providing a standardized framework for knowledge representation. The ontology is documented with an HTML specification generated from the OWL file and is aligned with best practices in industrial ontology development. +The Ontology for the Steel Domain (ONTORULE) is a domain ontology developed for the steel industry case study of the ONTORULE project [#ontorule-steel-doc]_. It provides a structured vocabulary for representing steel-domain concepts, attributes, and relationships relevant to steel production and processing [#ontorule-steel-doc]_. + +ONTORULE supports semantic annotation and representation of steel industry data, including materials, process-related information, defects, sampling information, and quality-relevant production data [#ontorule-steel-doc]_. By providing a standardized ontology for the steel domain, ONTORULE supports data integration, semantic search, and knowledge sharing across steel manufacturing and quality-management workflows [#ontorule-steel-doc]_. **Example Usage**: -Annotate a steel manufacturing process with ONTORULE terms to specify the materials used, process steps, equipment, and quality control attributes, enabling semantic search and integration with production and quality management systems. +Annotate a steel manufacturing process with ONTORULE terms to specify steel materials, process steps, defects, sampling information, and quality-control attributes, enabling semantic search and integration with production and quality management systems [#ontorule-steel-doc]_. Metrics & Statistics -------------------------- @@ -135,3 +137,11 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#ontorule-steel-doc] ONTORULE Project. 2010. + "Steel Industry case study Ontology." + Available at: + `https://ontorule-project.eu/resources/steel.html `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/photovoltaics.rst b/docs/source/benchmarking/materials_science_and_engineering/photovoltaics.rst index a1cad04..f9dec19 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/photovoltaics.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/photovoltaics.rst @@ -24,10 +24,12 @@ EMMO Domain Ontology for Photovoltaics (Photovoltaics) ======================================================================================================== +The EMMO Domain Ontology for Photovoltaics is an EMMO-based domain ontology for representing knowledge in the photovoltaics domain [#photovoltaics-github]_. The repository describes the ontology as focused on perovskite solar cells, while the ontology metadata identifies it as a top-level photovoltaics domain ontology based on EMMO [#photovoltaics-github]_. -The EMMO Domain Ontology for Photovoltaics is a specialized vocabulary built upon the Elementary Multiperspective Material Ontology (EMMO) framework for comprehensive semantic description of perovskite solar cells and photovoltaic systems. It provides structured vocabulary for describing photovoltaic device components including perovskite absorber layers, transport layers, electrodes, and contact materials, along with their properties and functions. The ontology captures essential photovoltaic device concepts such as optical properties (bandgap, absorption coefficients), electrical properties (carrier mobility, recombination), and performance metrics (efficiency, fill factor, open-circuit voltage). Photovoltaics ontology enables standardized annotation of experimental fabrication procedures, characterization techniques, and computational modeling of perovskite solar cells. The ontology facilitates knowledge integration in photovoltaics research by providing EMMO-compliant semantic representations compatible with broader materials science knowledge bases. +The ontology provides structured vocabulary for describing photovoltaic concepts such as photovoltaic devices, photovoltaic cells, photovoltaic modules, perovskite molecular entities, transport layers, substrate preparation, cell area, substrate area, storing before measurement, and photovoltaic quantities [#photovoltaics-github]_. By providing an EMMO-aligned semantic framework, the ontology supports semantic annotation, interoperability, data integration, and reuse of photovoltaic research information [#photovoltaics-github]_. -**Example Usage**: Annotate a perovskite solar cell research study with Photovoltaics terms describing device configuration (n-i-p or p-i-n), perovskite composition (CsPbI3, MAPbI3), transport materials (TiO2, spiro-OMeTAD), performance parameters (PCE, Voc, Jsc), and fabrication methods (spin-coating, vapor deposition). +**Example Usage**: +Annotate a perovskite solar cell research dataset with Photovoltaics ontology terms to specify photovoltaic devices, photovoltaic cells, modules, perovskite molecular entities, hole transport layers, substrate cleaning, substrate area, cell area, and measurement-related information, enabling semantic search and integration with materials informatics platforms [#photovoltaics-github]_. Metrics & Statistics -------------------------- @@ -136,3 +138,12 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#photovoltaics-github] EMMO-repo. n.d. + "Photovoltaics - EMMO domain ontology." + GitHub repository. + Available at: + `https://github.com/emmo-repo/domain-photovoltaics `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/pldo.rst b/docs/source/benchmarking/materials_science_and_engineering/pldo.rst index e125057..a37b795 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/pldo.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/pldo.rst @@ -23,14 +23,12 @@ Planar Defects Ontology (PLDO) ======================================================================================================== -The Planar Defects Ontology (PLDO) is an ontology designed to describe planar defects in crystalline materials, such as grain boundaries and stacking faults, with a focus on their atomic-scale structure and properties. PLDO provides a structured vocabulary for representing the types, properties, and interactions of planar defects, supporting both experimental and computational research in materials science. +The Planar Defects Ontology (PLDO) is an ontology designed to describe planar defects in crystalline materials, such as grain boundaries and stacking faults [#pldo-github]_. It provides a structured vocabulary for representing planar-defect concepts, their properties, and relationships in materials science [#pldo-github]_. -The ontology employs a class-based modeling approach, defining classes for different types of planar defects, properties, and interactions, along with properties to describe their characteristics and effects on material properties. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. PLDO supports the integration of data from various sources, promoting interoperability and data-driven research in materials science. - -Typical applications of PLDO include the development of new materials with specific defect properties, the optimization of material properties through defect engineering, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, PLDO enhances collaboration and innovation in the field of materials science. +PLDO supports semantic annotation of experimental and computational data related to planar defects, enabling interoperability, semantic search, data integration, and reuse across materials science databases and research workflows [#pldo-github]_. By providing a standardized vocabulary, PLDO helps organize planar-defect knowledge and supports comparison of defect-related information in crystalline materials [#pldo-github]_. **Example Usage**: -Annotate a materials science study with PLDO terms to specify planar defect types, properties, and interactions, enabling semantic search and integration with materials informatics platforms. +Annotate a materials science study with PLDO terms to specify planar defect types such as grain boundaries or stacking faults, together with their related properties and relationships, enabling semantic search and integration with materials informatics platforms [#pldo-github]_. Metrics & Statistics -------------------------- @@ -139,3 +137,12 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#pldo-github] OCDO. n.d. + "Planar Defects Ontology (PLDO)." + GitHub repository. + Available at: + `https://github.com/OCDO/pldo `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/pmdco.rst b/docs/source/benchmarking/materials_science_and_engineering/pmdco.rst index 5b67dbe..e45c408 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/pmdco.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/pmdco.rst @@ -23,13 +23,12 @@ The Platform MaterialDigital core ontology (PMDco) ======================================================================================================== +The Platform MaterialDigital Core Ontology (PMDco) is a mid-level ontology developed for Materials Science and Engineering (MSE) [#pmdco-doc]_ [#pmdco-paper]_. It provides a structured semantic framework for representing materials, processes, experiments, workflows, simulations, data, metadata, and material properties across MSE domains [#pmdco-doc]_. -The ontology employs a class-based modeling approach, defining classes for different types of materials, processes, and data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. PMDco supports the integration of data from various sources, promoting interoperability and data-driven research in materials science. - -Typical applications of PMDco include the development of new materials with specific properties, the optimization of materials for industrial applications, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, PMDco enhances collaboration and innovation in the field of materials science. +PMDco is based on the Basic Formal Ontology (BFO) and reuses BFO-aligned ontologies such as RO, IAO, and OBI to support interoperability with established ontology ecosystems [#pmdco-doc]_. The ontology helps bridge semantic gaps between top-level ontologies, domain-specific MSE application ontologies, and real-world research or industrial data sources [#pmdco-paper]_. By providing a standardized vocabulary, PMDco supports semantic annotation, data integration, traceability, reproducibility, FAIR data practices, and knowledge sharing in materials science [#pmdco-doc]_ [#pmdco-paper]_. **Example Usage**: -Annotate a materials science project with PMDco terms to specify material types, processes, and data, enabling semantic search and integration with materials informatics platforms. +Annotate a materials science project with PMDco terms to specify material types, processes, experiments, workflows, simulation data, measurement results, and metadata, enabling semantic search and integration with materials informatics platforms [#pmdco-doc]_ [#pmdco-paper]_. Metrics & Statistics -------------------------- @@ -138,3 +137,19 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#pmdco-doc] Platform MaterialDigital. n.d. + "PMD Core Ontology (PMDco)." + Ontology documentation. + Available at: + `https://materialdigital.github.io/core-ontology/docs/ `_ + +.. [#pmdco-paper] Bayerlein, B., Schilling, M., Birkholz, H., Jung, M., Waitelonis, J., Mädler, L., and Sack, H. 2024. + "PMD Core Ontology: Achieving semantic interoperability in materials science." + *Materials & Design*, 237, 112603. + DOI: 10.1016/j.matdes.2023.112603. + Available at: + `https://www.sciencedirect.com/science/article/pii/S0264127523010195 `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/podo.rst b/docs/source/benchmarking/materials_science_and_engineering/podo.rst index 572c5c7..46d7046 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/podo.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/podo.rst @@ -24,10 +24,12 @@ Point Defects Ontology (PODO) ======================================================================================================== +The Point Defect Ontology (PODO) is a specialized ontology designed to describe point defects in crystalline materials [#podo-github]_. It provides a structured vocabulary for representing point-defect concepts such as vacancies, interstitials, impurities, antisite defects, Frenkel defects, and Schottky defects [#podo-github]_. -PODO is a specialized ontology that formalizes the conceptualization and semantic representation of point defects in crystalline materials, enabling precise description and classification of atomic-scale defects. It provides structured vocabulary for describing different point defect types including vacancies, interstitials, substitutional defects, and antisite defects, along with their properties and formation mechanisms. PODO captures essential characteristics of point defects such as charge state, migration energy, binding interactions with other defects, and effects on material properties. The ontology enables systematic annotation of experimental observations (X-ray diffraction, electron microscopy) and computational predictions (first-principles calculations) of point defects in various crystal structures. PODO facilitates knowledge integration in materials informatics and computational materials databases by providing standardized semantic representations of point defect phenomena. +PODO supports semantic annotation of experimental and computational data related to point defects, enabling interoperability, semantic search, data integration, and reuse across materials science databases and research workflows [#podo-github]_. By providing a standardized representation of point-defect knowledge, PODO helps organize and compare defect-related information in crystalline materials [#podo-github]_. -**Example Usage**: Annotate a first-principles DFT study of point defects in semiconductors with PODO terms describing defect type (e.g., oxygen vacancy in TiO2), charge state (+2, neutral, -2), formation energy, charge transition levels, and effects on band structure and electrical properties. +**Example Usage**: +Annotate a first-principles DFT study of point defects in semiconductors with PODO terms to specify point-defect types such as vacancies, interstitials, impurities, antisite defects, Frenkel defects, or Schottky defects, enabling semantic search and integration with materials informatics platforms [#podo-github]_. Metrics & Statistics -------------------------- @@ -136,3 +138,12 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#podo-github] OCDO. n.d. + "Point Defects Ontology (PODO)." + GitHub repository. + Available at: + `https://github.com/OCDO/podo `_s diff --git a/docs/source/benchmarking/materials_science_and_engineering/prima.rst b/docs/source/benchmarking/materials_science_and_engineering/prima.rst index 82059b0..cb0d4f3 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/prima.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/prima.rst @@ -25,9 +25,12 @@ PRovenance Information in MAterials science (PRIMA) ======================================================================================================== -PRIMA is a comprehensive ontology that formalizes and captures provenance information essential for understanding the complete lifecycle and traceability of materials science data, experiments, and computational workflows. It provides structured vocabulary for describing the origins, history, transformations, and chain of custody of materials science information from experimental synthesis through characterization to computational modeling. PRIMA enables documentation of experimental procedures, computational methods, instruments used, software tools employed, and human researchers involved in materials science investigations. The ontology captures temporal information (when experiments were conducted), spatial context (where work was performed), and relationships between different research activities and resulting data. PRIMA facilitates reproducibility, data authentication, and knowledge integration in materials science by providing standardized provenance metadata compatible with linked data standards. +The PRovenance Information in MAterials science (PRIMA) ontology is designed to capture provenance information in the materials science domain [#prima-github]_. It provides a structured vocabulary for describing the origins, history, activities, agents, equipment, software, settings, techniques, and data-related entities involved in materials science research workflows [#prima-github]_. -**Example Usage**: Annotate a materials science dataset with PRIMA terms describing experimental origin (synthesis method, instrument, date), processing steps applied, computational analyses performed, and final data product generation, enabling complete traceability and reproducibility of materials discovery workflows. +PRIMA supports the semantic annotation of experimental and computational workflows, enabling traceability, interoperability, data integration, and reuse of materials science information [#prima-github]_. Starting from PRIMA v3, the ontology modules are grounded in BFO and aligned with PMDco v3, improving compatibility with related materials science ontology resources [#prima-github]_. By providing standardized provenance metadata, PRIMA supports reproducibility, data authentication, and knowledge integration in materials science [#prima-github]_. + +**Example Usage**: +Annotate a materials science dataset with PRIMA terms to describe the study, project, research users, equipment, software, settings, techniques, data acquisition, data analysis, and generated data products, enabling traceability and reproducibility of materials research workflows [#prima-github]_. Metrics & Statistics -------------------------- @@ -136,3 +139,12 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#prima-github] Materials Data Science and Informatics. n.d. + "PRovenance Information in MAterials science (PRIMA)." + GitHub repository. + Available at: + `https://github.com/Materials-Data-Science-and-Informatics/MDMC-NEP-top-level-ontology `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/ssn.rst b/docs/source/benchmarking/materials_science_and_engineering/ssn.rst index 307554b..6dc8046 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/ssn.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/ssn.rst @@ -23,14 +23,12 @@ Semantic Sensor Network Ontology (SSN) ======================================================================================================== -The Semantic Sensor Network (SSN) ontology is an ontology for describing sensors and their observations, the involved procedures, the studied features of interest, the samples used to do so, and the observed properties, as well as actuators. SSN follows a horizontal and vertical modularization architecture by including a lightweight but self-contained core ontology called SOSA (Sensor, Observation, Sample, and Actuator) for its elementary classes and properties. With their different scope and different degrees of axiomatization, SSN and SOSA are able to support a wide range of applications and use cases, including satellite imagery, large-scale scientific monitoring, industrial and household infrastructures, social sensing, citizen science, observation-driven ontology engineering, and the Web of Things. Both ontologies are described below, and examples of their usage are given. +The Semantic Sensor Network (SSN) ontology is an ontology for describing sensors, observations, procedures, features of interest, samples, observed properties, and actuators [#ssn-w3c]_. SSN includes a lightweight, self-contained core ontology called SOSA - Sensor, Observation, Sample, and Actuator which provides elementary classes and properties for modelling observation, sampling, and actuation activities [#ssn-w3c]_. -The ontology employs a class-based modeling approach, defining classes for different types of sensors, observations, and related data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. SSN supports the integration of data from various sources, promoting interoperability and data-driven research in sensor networks. - -Typical applications of SSN include the development of new sensor network methods, the optimization of sensor data management practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, SSN enhances collaboration and innovation in the field of sensor networks. +SSN and SOSA support a modular architecture with different scopes and degrees of axiomatization, enabling use across applications such as satellite imagery, scientific monitoring, industrial and household infrastructures, social sensing, citizen science, observation-driven ontology engineering, and the Web of Things [#ssn-w3c]_. By providing a standardized vocabulary, SSN supports semantic annotation, interoperability, data integration, querying, and reuse of sensor and observation data across sensor-network and data-management platforms [#ssn-w3c]_. **Example Usage**: -Annotate a sensor network dataset with SSN terms to specify sensor types, observations, and related data, enabling semantic search and integration with sensor network management platforms. +Annotate a sensor network dataset with SSN/SOSA terms to specify sensors, observations, procedures, features of interest, samples, observed properties, actuators, and results, enabling semantic search and integration with sensor-network management and Web of Things platforms [#ssn-w3c]_. Metrics & Statistics -------------------------- @@ -139,3 +137,12 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#ssn-w3c] W3C and OGC. 2017. + "Semantic Sensor Network Ontology." + W3C Recommendation. + Available at: + `https://www.w3.org/TR/vocab-ssn/ `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/systemcapabilities.rst b/docs/source/benchmarking/materials_science_and_engineering/systemcapabilities.rst index fc4827d..1382503 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/systemcapabilities.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/systemcapabilities.rst @@ -23,14 +23,12 @@ System Capabilities Ontology (SystemCapabilities) ======================================================================================================== -The System Capabilities Ontology (SystemCapabilities) is designed to describe system capabilities, operating ranges, and survival ranges in materials science and engineering. It provides a structured vocabulary for representing the capabilities and limitations of systems, supporting both theoretical and experimental research in materials science. +The System Capabilities Ontology (SystemCapabilities) is an ontology module designed to describe system capabilities, operating ranges, and survival ranges [#systemcapabilities-w3c]_ [#systemcapabilities-bioregistry]_. It provides a structured vocabulary for representing what a system is capable of doing under specified conditions, including capability properties, operating properties, survival properties, operating ranges, and survival ranges [#systemcapabilities-w3c]_. -The ontology employs a class-based modeling approach, defining classes for different types of systems, capabilities, and ranges, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. SystemCapabilities supports the integration of data from various sources, promoting interoperability and data-driven research in materials science. - -Typical applications of SystemCapabilities include the development of new systems with specific capabilities, the optimization of system performance, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, SystemCapabilities enhances collaboration and innovation in the field of materials science. +SystemCapabilities is part of the SSN/SOSA ontology framework and is used to describe capabilities and limitations of systems such as sensors and actuators [#systemcapabilities-w3c]_. The ontology supports semantic annotation, interoperability, data integration, and reuse of system-related information across sensor, engineering, and data-management applications [#systemcapabilities-w3c]_. **Example Usage**: -Annotate a materials science project with SystemCapabilities terms to specify system types, capabilities, and ranges, enabling semantic search and integration with materials informatics platforms. +Annotate a sensor or engineering system with SystemCapabilities terms to specify its measurement capability, operating range, survival range, accuracy, frequency, latency, or environmental limits, enabling semantic search and integration with sensor-network and engineering data platforms [#systemcapabilities-w3c]_. Metrics & Statistics -------------------------- @@ -139,3 +137,18 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#systemcapabilities-w3c] W3C and OGC. 2017. + "Semantic Sensor Network Ontology: System capabilities, operating ranges, and survival ranges." + W3C Recommendation. + Available at: + `https://www.w3.org/TR/vocab-ssn/#System-capabilities `_ + +.. [#systemcapabilities-bioregistry] Bioregistry. n.d. + "System capabilities, operating ranges, and survival ranges ontology." + Registry entry. + Available at: + `https://bioregistry.io/ssn.system `_ diff --git a/docs/source/benchmarking/materials_science_and_engineering/vimmp.rst b/docs/source/benchmarking/materials_science_and_engineering/vimmp.rst index f95157e..81d1d0d 100644 --- a/docs/source/benchmarking/materials_science_and_engineering/vimmp.rst +++ b/docs/source/benchmarking/materials_science_and_engineering/vimmp.rst @@ -23,14 +23,12 @@ Virtual Materials Marketplace Ontologies (VIMMP) ======================================================================================================== -The Virtual Materials Marketplace Ontologies (VIMMP) are developed as part of the Virtual Materials Marketplace project, which aims to provide an open platform for accessing services related to materials modeling. VIMMP employs a system of marketplace-level ontologies to characterize services, models, and interactions between users, using the European Materials and Modelling Ontology (EMMO) as a top-level ontology. +The Virtual Materials Marketplace Ontologies (VIMMP) are a system of marketplace-level ontologies developed as part of the Virtual Materials Marketplace project [#vimmp-paper]_. The VIMMP project aims to provide an open platform for providing and accessing services related to materials modelling [#vimmp-paper]_. Within VIMMP, the ontologies are used to characterise services, models, and interactions between users, with the European Materials and Modelling Ontology (EMMO) employed as a top-level ontology [#vimmp-paper]_. -The ontologies provide a structured vocabulary for describing services, models, and interactions, supporting both theoretical and experimental research in materials modeling. VIMMP ontologies enable the annotation of data stored in the ZONTAL Space component and support the ingest and retrieval of data and metadata at the VIMMP marketplace front-end. - -Typical applications of VIMMP include the development of new materials modeling services, the optimization of modeling workflows, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, VIMMP enhances collaboration and innovation in the field of materials modeling. +The VIMMP ontologies support semantic annotation of marketplace data stored in the ZONTAL Space component and help with the ingest and retrieval of data and metadata at the VIMMP marketplace frontend [#vimmp-paper]_. By providing a structured semantic framework, the ontologies support interoperability, data management, knowledge sharing, and integration of materials modelling services and workflows [#vimmp-paper]_. **Example Usage**: -Annotate a materials modeling project with VIMMP terms to specify service types, models, and interactions, enabling semantic search and integration with materials informatics platforms. +Annotate a materials modelling project with VIMMP ontology terms to specify service types, models, simulation workflows, user interactions, and marketplace metadata, enabling semantic search and integration with materials modelling and marketplace platforms [#vimmp-paper]_. Metrics & Statistics -------------------------- @@ -139,3 +137,13 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#vimmp-paper] Horsch, M. T., Chiacchiera, S., Seaton, M. A., Todorov, I. T., Šindelka, K., Lísal, M., Andreon, B., Bayro Kaiser, E., Mogni, G., Goldbeck, G., Kunze, R., Summer, G., Fiseni, A., Brüning, H., Schiffels, P., and Leite Cavalcanti, W. 2020. + "Ontologies for the Virtual Materials Marketplace." + arXiv:1912.01519. + DOI: 10.48550/arXiv.1912.01519. + Available at: + `https://arxiv.org/abs/1912.01519 `_ diff --git a/docs/source/benchmarking/medicine/bto.rst b/docs/source/benchmarking/medicine/bto.rst index bdd470e..61cb4aa 100644 --- a/docs/source/benchmarking/medicine/bto.rst +++ b/docs/source/benchmarking/medicine/bto.rst @@ -25,9 +25,12 @@ BRENDA Tissue Ontology (BTO) ======================================================================================================== -The BRENDA Tissue Ontology (BTO) is a structured controlled vocabulary for systematically describing and classifying the tissues, cell lines, cell types, and cell cultures that serve as sources for enzymatic and biochemical studies. Developed as part of the BRENDA (BRaunschweig ENzyme DAtabase) project, BTO provides standardized terminology for identifying experimental sample origins and biological sources in biochemistry and molecular biology research. The ontology includes comprehensive hierarchical classifications of human tissues, animal tissues, plant tissues, and microbial cell types, enabling precise semantic annotation of biological materials used in enzyme research and biotechnology. BTO supports data integration across biochemical databases and enables accurate searches for enzymes studied in specific tissue contexts. +The BRENDA Tissue Ontology (BTO) is a structured controlled vocabulary for describing and classifying enzyme sources, including tissues, cell lines, cell types, and cell cultures [#bto-github]_ [#bto-paper]_. Developed as part of the BRENDA enzyme information system, BTO provides standardized terminology for identifying biological sample origins in enzymatic, biochemical, and molecular biology studies [#bto-paper]_. -**Example Usage**: Annotate an enzyme assay result with a BTO term like "BTO:0000079" (liver) or "BTO:0000142" (kidney) to indicate the tissue source of the enzyme sample. +BTO supports semantic annotation and integration of enzyme-related data by linking tissue and cell-source information to biochemical research records [#bto-github]_ [#bto-paper]_. It includes terms for tissues, anatomical structures, organs, cell cultures, cell types, and cell lines from different organisms, enabling accurate search and comparison of enzymes studied in specific biological contexts [#bto-paper]_. + +**Example Usage**: +Annotate an enzyme assay result with a BTO term such as **BTO:0000079** for liver or **BTO:0000142** for kidney to indicate the tissue source of the enzyme sample, enabling semantic search and integration with biochemical databases [#bto-github]_ [#bto-paper]_. Metrics & Statistics -------------------------- @@ -136,3 +139,19 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#bto-github] BRENDA-Enzymes. n.d. + "BTO: BRENDA Tissue Ontology." + GitHub repository. + Available at: + `https://github.com/BRENDA-Enzymes/BTO `_ + +.. [#bto-paper] Gremse, M., Chang, A., Schomburg, I., Grote, A., Scheer, M., Ebeling, C., and Schomburg, D. 2011. + "The BRENDA Tissue Ontology (BTO): The first all-integrating ontology of all organisms for enzyme sources." + *Nucleic Acids Research*, 39, D507--D513. + DOI: 10.1093/nar/gkq968. + Available at: + `https://pubmed.ncbi.nlm.nih.gov/21030441/ `_ diff --git a/docs/source/benchmarking/medicine/deb.rst b/docs/source/benchmarking/medicine/deb.rst index 17b558e..4dd6b45 100644 --- a/docs/source/benchmarking/medicine/deb.rst +++ b/docs/source/benchmarking/medicine/deb.rst @@ -23,10 +23,12 @@ Devices, Experimental scaffolds and Biomaterials Ontology (DEB) ======================================================================================================== -The Devices, Experimental scaffolds and Biomaterials Ontology (DEB) is an open, community-driven ontology for organizing information about biomaterials, their design, manufacture, and biological testing. DEB provides a structured vocabulary for describing biomaterial types, experimental scaffolds, fabrication methods, and the biological assays used to evaluate them. The ontology was developed using text analysis of a biomaterials gold standard corpus and systematically curated to represent the domain's lexicon, with validation by biomaterials research experts. DEB supports semantic annotation of biomaterials research data, enabling interoperability, data integration, and advanced queries across experimental studies and databases. By providing a standardized framework, DEB facilitates reproducibility, knowledge sharing, and meta-analysis in biomaterials science and tissue engineering. The ontology is actively maintained and extended to incorporate new materials, experimental techniques, and biological endpoints as the field evolves. +The Devices, Experimental Scaffolds and Biomaterials Ontology (DEB) is an ontology for organizing information about biomaterials, devices, experimental scaffolds, and related biological testing [#deb-github]_ [#deb-bioportal]_. It provides a structured vocabulary for describing biomaterial types, scaffold-related concepts, device information, fabrication or processing details, and biological evaluation data [#deb-github]_ [#deb-bioportal]_. + +DEB supports semantic annotation, data integration, search, and reuse of biomaterials research information across experimental studies and databases [#deb-github]_ [#deb-bioportal]_. By providing standardized terminology, DEB helps organize biomaterials knowledge and supports cross-study comparison in biomaterials science and tissue engineering [#deb-bioportal]_. **Example Usage**: -Annotate a biomaterials experiment with DEB terms to specify the scaffold material (e.g., "collagen hydrogel"), fabrication method (e.g., "electrospinning"), and biological assay (e.g., "cell viability test"), enabling cross-study comparison and data integration. +Annotate a biomaterials experiment with DEB terms to specify the scaffold material, device type, fabrication or processing method, and biological assay information, enabling semantic search, cross-study comparison, and data integration [#deb-github]_ [#deb-bioportal]_. Metrics & Statistics -------------------------- @@ -135,3 +137,18 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#deb-github] ProjectDebbie. n.d. + "Ontology_DEB: The Device, Experimental Scaffolds and Biomaterials Ontology." + GitHub repository. + Available at: + `https://github.com/ProjectDebbie/Ontology_DEB `_ + +.. [#deb-bioportal] BioPortal. 2021. + "Devices, Experimental scaffolds and Biomaterials Ontology." + Ontology registry entry. + Available at: + `https://bioportal.bioontology.org/ontologies/DEB `_ diff --git a/docs/source/benchmarking/medicine/doid.rst b/docs/source/benchmarking/medicine/doid.rst index 262b4fa..160d041 100644 --- a/docs/source/benchmarking/medicine/doid.rst +++ b/docs/source/benchmarking/medicine/doid.rst @@ -25,9 +25,12 @@ Human Disease Ontology (DOID) ======================================================================================================== -The Disease Ontology (DOID) is a standardized, machine-readable ontology that provides consistent, reusable and sustainable descriptions of human diseases, medical conditions, and disease-related phenotypic characteristics. Developed collaboratively by the biomedical research community, DOID comprehensively covers disease classifications across diverse medical domains including infectious diseases, genetic disorders, cancers, cardiovascular diseases, and mental health conditions. The ontology employs hierarchical relationships to organize diseases from general categories to specific disease subtypes, enabling both broad and fine-grained disease annotation. DOID integrates with other biomedical ontologies (e.g., phenotype ontologies, gene ontologies) to link disease concepts with associated genes, symptoms, and environmental factors. The ontology is widely used in biomedical databases, genomics research, and clinical informatics for disease annotation and knowledge integration. +The Disease Ontology (DOID) is a standardized, machine-readable ontology for describing and classifying human diseases [#doid-github]_ [#doid-obofoundry]_. It provides reusable disease identifiers and a structured hierarchy for organizing human diseases across different biomedical domains [#doid-github]_ [#doid-obofoundry]_. -**Example Usage**: Annotate a disease research paper or dataset with DOID terms such as "DOID:2841 (lymphoma)" or "DOID:1816 (diabetes mellitus)" to enable automated discovery of disease-related research and clinical data. +DOID supports semantic annotation, data integration, search, and reuse of disease-related information across biomedical databases, genomics research, and clinical informatics workflows [#doid-github]_ [#doid-obofoundry]_. By providing a standardized disease vocabulary, DOID enables consistent disease annotation, knowledge integration, and cross-resource comparison of biomedical data [#doid-obofoundry]_. + +**Example Usage**: +Annotate a disease research paper or dataset with DOID terms such as **DOID:2841** for lymphoma or **DOID:9352** for diabetes mellitus, enabling semantic search and integration with biomedical databases and disease-annotation workflows [#doid-github]_ [#doid-obofoundry]_. Metrics & Statistics -------------------------- @@ -136,3 +139,18 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#doid-github] DiseaseOntology. n.d. + "HumanDiseaseOntology." + GitHub repository. + Available at: + `https://github.com/DiseaseOntology/HumanDiseaseOntology `_ + +.. [#doid-obofoundry] OBO Foundry. n.d. + "Human Disease Ontology." + Ontology registry entry. + Available at: + `https://obofoundry.org/ontology/doid.html `_ diff --git a/docs/source/benchmarking/medicine/enm.rst b/docs/source/benchmarking/medicine/enm.rst index 1c463bc..e041956 100644 --- a/docs/source/benchmarking/medicine/enm.rst +++ b/docs/source/benchmarking/medicine/enm.rst @@ -23,10 +23,12 @@ Environmental Noise Measurement Ontology (ENM) ======================================================================================================== -The Environmental Noise Measurement Ontology (ENM) is an application ontology developed as part of the eNanoMapper (https://www.enanomapper.net/), NanoCommons (https://www.nanocommons.eu/), and ACEnano (http://acenano-project.eu/) projects to support toxicological data management for engineered nanomaterials (ENMs). ENM provides a comprehensive vocabulary for describing nanomaterial safety assessment, including experimental procedures, measurement techniques, material properties, and exposure scenarios. The ontology reuses and integrates terms from several established ontologies, such as the Nanoparticle Ontology (NPO), Chemical Information Ontology (CHEMINF), Chemical Entities of Biological Interest (ChEBI), and Environment Ontology (ENVO), to ensure semantic interoperability and data integration. ENM enables standardized annotation of nanomaterial safety data, facilitating data sharing, regulatory compliance, and advanced analysis across research projects and databases. By providing a common framework, ENM supports the development of computational infrastructure for toxicology, risk assessment, and environmental health studies. The ontology is actively maintained and extended to incorporate new concepts and requirements from the nanomaterial safety community. +The Environmental Noise Measurement Ontology (ENM) is an application ontology developed to support toxicological data management and nanomaterial safety assessment for engineered nanomaterials [#enm-doc]_ [#enm-paper]_. It provides a structured vocabulary for describing nanomaterials, experimental procedures, measurement techniques, material properties, exposure scenarios, biological interactions, and safety-related information [#enm-doc]_ [#enm-paper]_. + +ENM was developed in the context of the eNanoMapper project and reuses or extends existing ontologies relevant to nanosafety, including the Nanoparticle Ontology (NPO), Chemical Information Ontology (CHEMINF), Chemical Entities of Biological Interest (ChEBI), and Environment Ontology (ENVO) [#enm-paper]_. The ontology supports semantic annotation, data integration, interoperability, search, and reuse of nanomaterial safety data across research projects and databases [#enm-doc]_ [#enm-paper]_. **Example Usage**: -Annotate a nanotoxicology study with ENM terms to specify the nanomaterial type, measurement methods, exposure conditions, and observed biological effects, enabling cross-study comparison and regulatory reporting. +Annotate a nanotoxicology study with ENM terms to specify the nanomaterial type, measurement method, exposure condition, experimental assay, material property, and observed biological effect, enabling cross-study comparison and integration with nanosafety databases [#enm-doc]_ [#enm-paper]_. Metrics & Statistics -------------------------- @@ -135,3 +137,18 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#enm-doc] eNanoMapper. n.d. + "Ontology." + Available at: + `https://www.enanomapper.net/ontology `_ + +.. [#enm-paper] Hastings, J., Jeliazkova, N., Owen, G., Tsiliki, G., Munteanu, C. R., Steinbeck, C., and Willighagen, E. 2015. + "eNanoMapper: harnessing ontologies to enable data integration for nanomaterial risk assessment." + *Journal of Biomedical Semantics*, 6, Article 10. + DOI: 10.1186/s13326-015-0005-5. + Available at: + `https://pmc.ncbi.nlm.nih.gov/articles/PMC4374589/ `_ diff --git a/docs/source/benchmarking/medicine/mfoem.rst b/docs/source/benchmarking/medicine/mfoem.rst index bf32863..51af4ac 100644 --- a/docs/source/benchmarking/medicine/mfoem.rst +++ b/docs/source/benchmarking/medicine/mfoem.rst @@ -23,10 +23,12 @@ Mental Functioning Ontology of Emotions - Emotion Module (MFOEM) ======================================================================================================== -The Mental Functioning Ontology of Emotions - Emotion Module (MFOEM) is a domain ontology designed to comprehensively represent affective phenomena, including emotions, moods, and their various dimensions and expressions. MFOEM provides a structured vocabulary for describing the bearers of emotions, types of emotions, their parts, and the dimensions along which they vary (such as intensity, duration, and valence). The ontology also covers facial and vocal expressions of emotions and the influence of affective phenomena on human behavior. MFOEM supports semantic annotation of psychological and neuroscientific data, enabling interoperability, data integration, and advanced analysis in affective science research. By providing a standardized framework, MFOEM facilitates cross-study comparison, meta-analysis, and the development of emotion-aware applications in artificial intelligence and human-computer interaction. The ontology is actively maintained and extended to incorporate new findings and requirements from the affective science community. +The Mental Functioning Ontology of Emotions - Emotion Module (MFOEM), also known as the **Emotion Ontology**, is a domain ontology for affective phenomena such as emotions, moods, appraisals, and subjective feelings [#mfoem-github]_ [#mfoem-obofoundry]_. It is a domain specialization of the broader Mental Functioning Ontology and is designed to support interdisciplinary emotion research through unified semantic annotations [#mfoem-obofoundry]_. + +MFOEM provides a structured vocabulary for describing affective phenomena, including the bearers of emotions, types of emotions, parts of emotions, dimensions of variation, facial and vocal expressions, and the role of emotions in influencing human behavior [#mfoem-github]_. By providing standardized terminology, MFOEM supports semantic annotation, interoperability, data integration, and comparison of psychological, neuroscientific, and affective-science datasets [#mfoem-github]_ [#mfoem-obofoundry]_. **Example Usage**: -Annotate a psychological study with MFOEM terms to specify the types of emotions measured (e.g., "fear," "joy"), their intensity, and observed facial expressions, enabling semantic integration and comparison across emotion research datasets. +Annotate a psychological or affective-science study with MFOEM terms to specify emotion types such as fear or joy, emotion dimensions such as intensity or duration, and observed facial or vocal expressions, enabling semantic integration and comparison across emotion research datasets [#mfoem-github]_ [#mfoem-obofoundry]_. Metrics & Statistics -------------------------- @@ -135,3 +137,18 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#mfoem-github] Hastings, J. n.d. + "emotion-ontology." + GitHub repository. + Available at: + `https://github.com/jannahastings/emotion-ontology `_ + +.. [#mfoem-obofoundry] OBO Foundry. n.d. + "Emotion Ontology." + Ontology registry entry. + Available at: + `https://obofoundry.org/ontology/mfoem.html `_ diff --git a/docs/source/benchmarking/medicine/obi.rst b/docs/source/benchmarking/medicine/obi.rst index a115e0a..91a1e1a 100644 --- a/docs/source/benchmarking/medicine/obi.rst +++ b/docs/source/benchmarking/medicine/obi.rst @@ -25,9 +25,12 @@ Ontology for Biomedical Investigations (OBI) ======================================================================================================== -The Ontology for Biomedical Investigations (OBI) provides a comprehensive vocabulary for describing scientific investigations, experimental designs, and biomedical research methodology. It defines over 2500 terms covering assays, devices, protocols, objectives, materials, measurements, and outcomes used in biomedical and life sciences research. OBI enables standardized semantic annotation of experimental workflows, making research methodologies transparent, reproducible, and interoperable across databases and computational systems. The ontology captures hierarchical relationships between experimental concepts, allowing researchers to precisely describe complex experimental designs and data collection procedures. OBI integrates with other biological ontologies (BFO, ChEBI, CHEBI) to provide comprehensive semantic representation of biomedical investigations. +The Ontology for Biomedical Investigations (OBI) is an ontology for describing scientific investigations, experimental designs, and biomedical research methodology [#obi-obofoundry]_ [#obi-paper]_. It provides a structured vocabulary for representing assays, devices, protocols, objectives, materials, measurements, data, and analysis processes used in biomedical and life sciences research [#obi-paper]_. -**Example Usage**: Annotate a microarray experiment with OBI terms such as "assay" for the type of investigation, "microarray device" for equipment, "RNA extraction" for material preparation steps, and measurement-related terms for results. +OBI supports standardized semantic annotation of experimental workflows, enabling interoperability, data integration, comparison, and reuse of biomedical investigation data across databases and computational systems [#obi-obofoundry]_ [#obi-paper]_. By providing a common framework for describing how scientific data are generated, OBI helps make research methods more transparent and reproducible [#obi-paper]_. + +**Example Usage**: +Annotate a microarray experiment with OBI terms to specify the assay type, microarray device, RNA extraction step, protocol, input material, generated data, and measurement results, enabling semantic search and integration with biomedical databases [#obi-obofoundry]_ [#obi-paper]_. Metrics & Statistics -------------------------- @@ -136,3 +139,19 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#obi-obofoundry] OBO Foundry. n.d. + "OBI: Ontology for Biomedical Investigations." + Ontology registry entry. + Available at: + `https://obofoundry.org/ontology/obi.html `_ + +.. [#obi-paper] Bandrowski, A., Brinkman, R., Brochhausen, M., et al. 2016. + "The Ontology for Biomedical Investigations." + *PLOS ONE*, 11(4), e0154556. + DOI: 10.1371/journal.pone.0154556. + Available at: + `https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0154556 `_ diff --git a/docs/source/benchmarking/news_and_media/bbc.rst b/docs/source/benchmarking/news_and_media/bbc.rst index 7510752..0b853fe 100644 --- a/docs/source/benchmarking/news_and_media/bbc.rst +++ b/docs/source/benchmarking/news_and_media/bbc.rst @@ -22,11 +22,12 @@ BBC Ontology (BBC) ======================================================================================================== +The BBC Ontology (BBC) is an ontology used to describe BBC concepts in the BBC linked-data store [#bbc-ontology]_. It represents BBC divisions or products that publish linked data, the platforms for which BBC content is produced, and web documents that publish or are relevant to BBC content [#bbc-ontology]_. -The BBC Ontology (BBC) is a domain ontology developed to represent the structure, relationships, and metadata of BBC web documents, products, and platforms. It provides a standardized vocabulary for describing content types, thematic areas, editorial products, and the connections between them. The ontology enables semantic annotation of BBC content, supporting advanced search, personalized recommendations, and content integration across BBC's digital ecosystem. BBC is designed for extensibility and interoperability, allowing integration with other BBC ontologies and external vocabularies for broader data linking. By providing a common semantic framework, the BBC Ontology facilitates content discovery, analytics, and knowledge management for editorial, educational, and entertainment products. The ontology is maintained as an open resource and is actively extended to support new content types, platforms, and user experiences. +The ontology codifies the logic connecting web documents, BBC products, and platforms [#bbc-ontology]_. It defines key classes such as Product, Platform, WebDocument, WebDocumentCategory, and NewsService, together with properties such as product, platform, primaryContent, primaryContentOf, hasOutput, covers, and servedBy [#bbc-ontology]_. By providing a structured vocabulary, the BBC Ontology supports semantic annotation, content integration, discovery, and linked-data representation across BBC digital services [#bbc-ontology]_. **Example Usage**: -Annotate a BBC web page with BBC Ontology terms to specify its content type (e.g., "NewsArticle"), associated product (e.g., "BBC News"), thematic area (e.g., "Education"), and relationships to related articles or platforms, enabling semantic search and cross-platform content integration. +Annotate a BBC web document with BBC Ontology terms to specify the product it belongs to, such as BBC Sport, the platform it is intended for, such as HighWeb or Mobile, its primary creative work, and its web document category, enabling semantic search and integration across BBC linked-data systems [#bbc-ontology]_. Metrics & Statistics -------------------------- @@ -135,3 +136,11 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#bbc-ontology] BBC. 2012. + "BBC Ontology." + Available at: + `https://iptc.org/thirdparty/bbc-ontologies/bbc.html `_ diff --git a/docs/source/benchmarking/news_and_media/bbcbusiness.rst b/docs/source/benchmarking/news_and_media/bbcbusiness.rst index 1925f04..131fe91 100644 --- a/docs/source/benchmarking/news_and_media/bbcbusiness.rst +++ b/docs/source/benchmarking/news_and_media/bbcbusiness.rst @@ -22,11 +22,12 @@ BBC Business News Ontology (BBCBusiness) ======================================================================================================== +The BBC Business News Ontology (BBCBusiness) is a domain-specific ontology for describing concepts that occur in BBC business news [#bbcbusiness-ontology]_. It provides a structured vocabulary for representing business-news entities such as companies, private companies, public companies, market sectors, shares, stock markets, and market indexes [#bbcbusiness-ontology]_. -The BBC Business News Ontology (BBCBusiness) is a domain-specific ontology designed to represent concepts, entities, and relationships relevant to business news coverage. It provides a structured vocabulary for describing companies, markets, financial instruments, economic indicators, business events, and regulatory developments. BBCBusiness enables semantic annotation of business news articles, supporting advanced search, aggregation, and analytics across news platforms. The ontology is designed for interoperability with other BBC ontologies and external vocabularies, facilitating integration with financial data sources and knowledge graphs. By providing a standardized framework, BBCBusiness supports automated content tagging, trend analysis, and personalized news delivery in business journalism. The ontology is actively maintained and extended to reflect changes in the global business landscape and emerging topics in finance and economics. +BBCBusiness supports semantic annotation and linked-data representation of business news content by defining classes and properties such as **Company**, **Sector**, **Share**, **Market**, **Index**, **companyLocation**, **parentCompany**, **sector**, **hasShare**, **listing**, **shareTicker**, and **marketTicker** [#bbcbusiness-ontology]_. By providing a standardized vocabulary, BBCBusiness supports business-news search, content integration, and semantic linking across BBC news data [#bbcbusiness-ontology]_. **Example Usage**: -Annotate a business news article with BBCBusiness terms to specify the companies involved, market sectors, financial events (e.g., mergers, IPOs), and economic indicators, enabling semantic search and cross-platform business news analysis. +Annotate a business news article with BBCBusiness terms to specify the companies involved, whether they are public or private companies, their market sector, share information, stock-market listings, and ticker symbols, enabling semantic search and cross-platform business news integration [#bbcbusiness-ontology]_. Metrics & Statistics -------------------------- @@ -135,3 +136,11 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#bbcbusiness-ontology] BBC. 2014. + "Business News Ontology." + Available at: + `https://iptc.org/thirdparty/bbc-ontologies/business.html `_ diff --git a/docs/source/benchmarking/news_and_media/bbccms.rst b/docs/source/benchmarking/news_and_media/bbccms.rst index cea33af..312520a 100644 --- a/docs/source/benchmarking/news_and_media/bbccms.rst +++ b/docs/source/benchmarking/news_and_media/bbccms.rst @@ -25,9 +25,12 @@ BBC CMS Ontology (BBCCMS) ======================================================================================================== -The BBC CMS Ontology is a specialized vocabulary for representing and managing relationships between content management systems, creative content, and the entities that BBC produces content about. It defines standardized terms and structures for Content Management Systems (CMS) to interact with linked data platforms, enabling semantic representation of how content relates to real-world concepts. The ontology captures associations between different instances of the same concept across multiple BBC systems, ensuring consistency and linkage of related entities (people, places, organizations, events) referenced in content. BBCCMS facilitates content integration and semantic linking across BBC's diverse content production systems and publishing platforms. The ontology enables sophisticated content discovery and recommendations by providing explicit relationships between content and the concepts it addresses. +The BBC CMS Ontology is an ontology for representing content management systems and their interaction with the BBC Linked Data Platform [#bbccms-ontology]_. It defines how entities and creative works in the BBC triplestore are associated with external BBC content management systems that provide additional information about the same thing or content item [#bbccms-ontology]_. -**Example Usage**: Link a BBC news article or program to BBC Core Concepts (people, organizations, places, events) with BBCCMS terms that establish how the same entity is referenced across different content pieces and editorial domains. +The ontology provides terms for linking BBC concepts, web documents, creative works, and related CMS records, allowing the Linked Data Platform to point users and systems to information stored outside the triplestore [#bbccms-ontology]_. It supports content integration, semantic linking, retrieval, and management of relationships between BBC linked-data entities and the systems that produce or store content [#bbccms-ontology]_. + +**Example Usage**: +Link a BBC entity such as **Manchester United** or a BBC creative work to an external content management system using BBC CMS Ontology terms, so that additional information such as sports statistics or the full body of a creative work can be retrieved from the relevant CMS [#bbccms-ontology]_. Metrics & Statistics -------------------------- @@ -136,3 +139,11 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#bbccms-ontology] BBC. 2012. + "CMS Ontology." + Available at: + `https://iptc.org/thirdparty/bbc-ontologies/cms.html `_ diff --git a/docs/source/benchmarking/news_and_media/bbccoreconcepts.rst b/docs/source/benchmarking/news_and_media/bbccoreconcepts.rst index 3ecf2af..b20f96c 100644 --- a/docs/source/benchmarking/news_and_media/bbccoreconcepts.rst +++ b/docs/source/benchmarking/news_and_media/bbccoreconcepts.rst @@ -24,10 +24,12 @@ BBC Core Concepts Ontology (BBCCoreConcepts) ======================================================================================================== +The BBC Core Concepts Ontology (BBCCoreConcepts) is a generic BBC ontology for representing people, places, events, organisations, and themes that are relevant across BBC content domains [#bbccoreconcepts-ontology]_. It provides a shared semantic model for describing real-world entities that appear in BBC content and supports consistent annotation across different media types, products, and editorial areas [#bbccoreconcepts-ontology]_. -The BBC Core Concepts Ontology is a foundational vocabulary defining generic concepts that are universally relevant across BBC's diverse content domains including people, places, events, organizations, and themes. It provides a shared semantic model for representing real-world entities that frequently appear in BBC content, enabling consistent annotation and discovery across multiple media types and editorial departments. BBCCoreConcepts is designed to be sufficiently generic to serve as a base ontology, allowing domain experts and specialized ontologies to extend it for specific use cases (e.g., athletes for sports content, politicians for news content) through rdfs:subClassOf relationships. The ontology enables semantic interoperability across BBC's content production systems and linked data platforms by providing standardized definitions of common entities. BBCCoreConcepts facilitates sophisticated content linking and discovery by establishing shared semantic representations of entities referenced across diverse BBC programs and services. +BBCCoreConcepts is designed to be generic enough for domain experts to extend it with domain-specific concepts, such as athletes or politicians, using ``rdfs:subClassOf`` relationships [#bbccoreconcepts-ontology]_. By providing common entity classes and relationships, the ontology supports semantic interoperability, content linking, discovery, and reuse across BBC linked-data systems [#bbccoreconcepts-ontology]_. -**Example Usage**: Define domain-specific concepts by creating athlete, musician, or politician subclasses of the generic "Person" concept in BBC Core Concepts, enabling both generic searches and domain-specific searches for related content. +**Example Usage**: +Define domain-specific concepts such as athlete, musician, or politician as subclasses of the generic **Person** concept, enabling both broad searches for people and more specific searches for domain-relevant entities across BBC content [#bbccoreconcepts-ontology]_. Metrics & Statistics -------------------------- @@ -136,3 +138,12 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#bbccoreconcepts-ontology] BBC and IPTC. n.d. + "Core Concepts Ontology." + *BBC ontologies*. + Available at: + `https://iptc.org/thirdparty/bbc-ontologies/index.html `_ diff --git a/docs/source/benchmarking/news_and_media/bbccreativework.rst b/docs/source/benchmarking/news_and_media/bbccreativework.rst index d468a4c..847ffa0 100644 --- a/docs/source/benchmarking/news_and_media/bbccreativework.rst +++ b/docs/source/benchmarking/news_and_media/bbccreativework.rst @@ -22,11 +22,12 @@ BBC Creative Work Ontology (BBCCreativeWork) ======================================================================================================== +The BBC Creative Work Ontology (BBCCreativeWork) is an ontology used to describe creative works produced by the BBC and their associated metadata [#bbccreativework-ontology]_. It provides a structured vocabulary for representing creative works, their specific types such as BlogPost, NewsItem, and Programme, their audiences, and the tags associated with them [#bbccreativework-ontology]_. -The BBC Creative Work Ontology (BBCCreativeWork) is a domain ontology designed to represent creative works produced by the BBC, such as articles, blog posts, news items, programmes, and their associated metadata. It provides a structured vocabulary for describing creative works, their types, audiences, tags, and relationships to other works and entities. BBCCreativeWork supports semantic annotation of content, enabling advanced search, recommendation, and content management across BBC platforms. The ontology is designed for extensibility, allowing integration with other BBC ontologies and external vocabularies for broader data interoperability. By providing standardized terms and relationships, BBCCreativeWork facilitates content linking, audience targeting, and analytics in digital publishing. The ontology is maintained as an open resource and is actively extended to support new content types and publishing requirements. +BBCCreativeWork supports reading and writing creative works in the BBC triplestore and enables semantic annotation, content integration, discovery, and reuse across BBC digital platforms [#bbccreativework-ontology]_. By providing standardized terms for creative content and metadata, the ontology helps connect BBC content with topics, audiences, and related entities in linked-data systems [#bbccreativework-ontology]_. **Example Usage**: -Annotate a BBC news article with BBCCreativeWork terms to specify its type (e.g., "NewsItem"), associated tags (e.g., "climate change"), intended audience (e.g., "general public"), and relationships to related programmes or blog posts, enabling semantic search and personalized content recommendations. +Annotate a BBC news article with BBCCreativeWork terms to specify that it is a **NewsItem**, link it to relevant tags or topics, identify its intended audience, and connect it to related creative works, enabling semantic search and content integration across BBC platforms [#bbccreativework-ontology]_. Metrics & Statistics -------------------------- @@ -135,3 +136,11 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#bbccreativework-ontology] BBC. 2012. + "Creative Work Ontology." + Available at: + `https://iptc.org/thirdparty/bbc-ontologies/creativework.html `_ diff --git a/docs/source/benchmarking/news_and_media/bbcfood.rst b/docs/source/benchmarking/news_and_media/bbcfood.rst index 0548a7c..f89df13 100644 --- a/docs/source/benchmarking/news_and_media/bbcfood.rst +++ b/docs/source/benchmarking/news_and_media/bbcfood.rst @@ -23,10 +23,12 @@ BBC Food Ontology (BBCFood) ======================================================================================================== -The BBC Food Ontology (BBCFood) is a lightweight ontology designed to represent recipes, ingredients, and culinary concepts for publishing food-related data on the web. It provides a structured vocabulary for describing foods, their components, preparation methods, dietary suitability, menus, courses, seasons, and occasions. BBCFood supports semantic annotation of recipes and food content, enabling interoperability and data integration across recipe websites, nutrition databases, and culinary applications. The ontology is designed for extensibility and can be adapted to a wide range of food data publishing use cases beyond the original BBC context. By providing standardized terms and relationships, BBCFood facilitates advanced search, recommendation, and personalized meal planning. The ontology is maintained as an open resource and is compatible with other food and nutrition ontologies for broader data integration. +The BBC Food Ontology (BBCFood) is a lightweight ontology for publishing data about recipes, ingredients, menus, and diets on the web [#bbcfood-ontology]_. It provides a structured vocabulary for describing recipes, the foods they are made from, the foods they create, and the diets, menus, seasons, courses, and occasions for which they may be suitable [#bbcfood-ontology]_. + +BBCFood originates from a BBC use case, but is designed to be applicable to a wide range of recipe-data publishing scenarios across the web [#bbcfood-ontology]_. By providing linked terms and relationships for food content, the ontology supports semantic annotation, interoperability, data integration, search, and reuse of recipe and culinary information [#bbcfood-ontology]_. **Example Usage**: -Annotate a recipe database with BBCFood terms to specify ingredients, preparation steps, dietary restrictions (e.g., "vegetarian"), and suitable occasions (e.g., "holiday dinner"), enabling semantic search and personalized recipe recommendations. +Annotate a recipe database with BBCFood terms to specify recipes, ingredients, menus, diets, courses, seasons, and suitable occasions, enabling semantic search and integration with food and recipe publishing platforms [#bbcfood-ontology]_. Metrics & Statistics -------------------------- @@ -135,3 +137,11 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#bbcfood-ontology] BBC. 2014. + "Food Ontology." + Available at: + `https://iptc.org/thirdparty/bbc-ontologies/fo.html `_ diff --git a/docs/source/benchmarking/news_and_media/bbcpolitics.rst b/docs/source/benchmarking/news_and_media/bbcpolitics.rst index a3b2237..98e30f5 100644 --- a/docs/source/benchmarking/news_and_media/bbcpolitics.rst +++ b/docs/source/benchmarking/news_and_media/bbcpolitics.rst @@ -22,10 +22,12 @@ BBC Politics News Ontology (BBCPolitics) ======================================================================================================== +The BBC Politics Ontology is an ontology for describing politics, especially local government and elections [#bbcpolitics-ontology]_. It was originally designed for UK local and European elections and provides a structured vocabulary for political concepts such as constituencies, councils, elections, political parties, and statistical geographies [#bbcpolitics-ontology]_. -The BBC Politics Ontology provides a formal vocabulary for describing and classifying political concepts, entities, and events as they appear in BBC news coverage. It models key political domain concepts including politicians, political parties, government institutions, legislative processes, electoral systems, and political ideologies. The ontology captures relationships between political entities (e.g., politicians affiliated with parties, parties contesting elections) and enables semantic annotation of news articles, television programs, and online content related to politics. It supports content discovery and automated linking of related political news stories across BBC's diverse platforms and archives. The ontology follows BBC's linked data principles and integrates with other BBC ontologies (People, Places, Organizations) to provide comprehensive semantic context. +The ontology defines classes such as Constituency, Council, Election, PoliticalParty, and StatisticalGeography, and links them to broader BBC Core Concepts such as organisations, events, and places [#bbcpolitics-ontology]*. It also defines relationships such as governsGSS, which relates a political organisation such as a council to the statistical geography it governs [#bbcpolitics-ontology]*. By providing a standardized vocabulary, the BBC Politics Ontology supports semantic annotation, content linking, and discovery of political content in BBC linked-data systems [#bbcpolitics-ontology]_. -**Example Usage**: Annotate a political news article with BBCPolitics terms to identify mentioned politicians (as foaf:Person instances), their party affiliations, relevant legislation being discussed, and electoral contexts. +**Example Usage**: +Annotate a political news article with BBC Politics Ontology terms to specify an election, constituency, council, political party, or governed statistical geography, enabling semantic search and integration with BBC political news and election data [#bbcpolitics-ontology]_. Metrics & Statistics -------------------------- @@ -134,3 +136,11 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#bbcpolitics-ontology] BBC. 2014. + "Politics Ontology." + Available at: + `https://iptc.org/thirdparty/bbc-ontologies/politics.html `_ diff --git a/docs/source/benchmarking/news_and_media/bbcprogrammes.rst b/docs/source/benchmarking/news_and_media/bbcprogrammes.rst index dfb934b..a397bda 100644 --- a/docs/source/benchmarking/news_and_media/bbcprogrammes.rst +++ b/docs/source/benchmarking/news_and_media/bbcprogrammes.rst @@ -25,9 +25,12 @@ BBC Programmes Ontology (BBCProgrammes) ======================================================================================================== -The BBC Programmes Ontology is a specialized vocabulary for describing television and radio programmes, their structure, and broadcast distribution across BBC platforms. It provides standardized terminology for representing programme hierarchies including brands (show franchises), series/seasons (annual or thematic groupings), episodes (individual content pieces), broadcast events (scheduled transmissions), and broadcast services (channels, stations). BBCProgrammes enables precise semantic annotation of broadcast content metadata including airing dates, durations, genres, and relationships between programmes. The ontology facilitates content discovery and programme management across BBC's television and radio platforms by providing standardized semantic structures. BBCProgrammes development was informed by extensive BBC programme data modelling experience, ensuring practical applicability in production systems. +The BBC Programmes Ontology is a vocabulary for describing television and radio programmes, their structure, and their broadcast distribution [#bbcprogrammes-ontology]_. It covers programme brands, series or seasons, episodes, versions of episodes, broadcast events, broadcast services, and related programme information [#bbcprogrammes-ontology]_. -**Example Usage**: Represent a television series using BBCProgrammes terms with brand (Doctor Who), seasons/series (Series 10, Series 11), individual episodes with episode numbers and air dates, and associated broadcast events showing transmission times across different BBC channels. +The ontology is grounded in BBC programme data modelling work and is inspired by the BBC PIPS database schema, which describes how brands, series, episodes, versions, and broadcasts interact with each other [#bbcprogrammes-ontology]_. By providing a standardized semantic structure, the BBC Programmes Ontology supports linked-data publication, programme discovery, metadata integration, and interchange of programme information on the Semantic Web [#bbcprogrammes-ontology]_. + +**Example Usage**: +Represent a television programme using BBC Programmes Ontology terms by linking a brand such as **Doctor Who** to its series, episodes, versions, and broadcast events, including transmission information across BBC channels or services [#bbcprogrammes-ontology]_. Metrics & Statistics -------------------------- @@ -136,3 +139,11 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#bbcprogrammes-ontology] BBC. 2009. + "BBC Programmes Ontology." + Available at: + `https://iptc.org/thirdparty/bbc-ontologies/po.html `_ diff --git a/docs/source/benchmarking/news_and_media/bbcprovenance.rst b/docs/source/benchmarking/news_and_media/bbcprovenance.rst index 47ed233..a7e3ca0 100644 --- a/docs/source/benchmarking/news_and_media/bbcprovenance.rst +++ b/docs/source/benchmarking/news_and_media/bbcprovenance.rst @@ -25,9 +25,12 @@ BBC Provenance News Ontology (BBCProvenance) ======================================================================================================== -The BBC Provenance News Ontology is a specialized vocabulary for capturing and representing provenance metadata about data quality, source attribution, and immediate data providers in RDF knowledge graphs and semantic web applications. It focuses on recording immediate data providers (e.g., which internal BBC team or external service provided data) rather than ultimate sources, enabling accountability and quality tracking in linked data systems. BBCProvenance applies provenance annotations to named graphs (quads in RDF stores), providing a fourth dimension to triples that enables context-aware data management and versioning. The ontology supports data governance and management in linked data platforms by providing standardized provenance metadata structures. BBCProvenance facilitates trust and quality assessment in semantic web applications by enabling transparent tracking of data origins and transformations. +The BBC Provenance News Ontology is a vocabulary for capturing provenance metadata about data in the BBC RDF triple store [#bbcprovenance-ontology]_. It supports data management and auditing tasks by defining types of named graphs used in the BBC quad store and associating them with metadata for managing, validating, and exposing data to BBC services [#bbcprovenance-ontology]_. -**Example Usage**: Annotate geodata in a named graph with BBCProvenance terms indicating that the data was provided by the BBC Locator team (immediate provider) on a specific date, with specific quality assurance level, enabling proper attribution and version tracking. +The ontology focuses on recording the immediate provider of data rather than the ultimate source; for example, it can record that geodata was provided by the BBC Locator team rather than by the original external source [#bbcprovenance-ontology]_. In the BBC Linked Data Platform, this provenance information is applied to contexts or named graphs, where the named graph acts as a fourth part of an RDF triple, forming a quad store [#bbcprovenance-ontology]_. + +**Example Usage**: +Annotate geodata in a named graph with BBC Provenance terms to indicate that the data was provided by the BBC Locator team, enabling attribution, validation, auditing, and management of provenance metadata in BBC linked-data systems [#bbcprovenance-ontology]_. Metrics & Statistics -------------------------- @@ -136,3 +139,11 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#bbcprovenance-ontology] BBC. 2012. + "Provenance Ontology." + Available at: + `https://iptc.org/thirdparty/bbc-ontologies/provenance.html `_ diff --git a/docs/source/benchmarking/news_and_media/bbcsport.rst b/docs/source/benchmarking/news_and_media/bbcsport.rst index b1cd1b4..b0e0a15 100644 --- a/docs/source/benchmarking/news_and_media/bbcsport.rst +++ b/docs/source/benchmarking/news_and_media/bbcsport.rst @@ -23,7 +23,12 @@ BBC Sport Ontology (BBCSport) ======================================================================================================== -The BBC Sport Ontology (BBCSport) is a lightweight ontology designed to represent competitive sports events, tournaments, participants, disciplines, and awards. It provides a structured vocabulary for describing the structure of sports tournaments as a series of events, the participation of agents (teams, athletes), the type of discipline involved, and the awards associated with competitions. BBCSport is designed for interoperability with general event ontologies and supports semantic annotation of sports data for publishing, search, and analytics. The ontology enables integration of sports data across platforms, facilitating advanced queries, personalized recommendations, and automated content generation for sports journalism. BBCSport is extensible and can be adapted to a wide range of sports and event types, supporting both professional and amateur competitions. The ontology is maintained as an open resource and is compatible with other BBC and external ontologies for broader data integration. +The BBC Sport Ontology (BBCSport) is a simple, lightweight ontology for publishing data about competitive sports events [#bbcsport-ontology]_. It provides a structured vocabulary for representing the structure of sports tournaments as a series of events, the competing of agents in a competition, the type of discipline involved in an event, and awards associated with competitions [#bbcsport-ontology]_. + +BBCSport is designed for interoperability with more general event ontologies and draws heavily on the Events Ontology [#bbcsport-ontology]_. Although it originates from a BBC use case, it is intended to be applicable to a wide range of competitive sports event data publishing use cases [#bbcsport-ontology]_. By providing standardized terms for competitions, events, participants, disciplines, and awards, BBCSport supports semantic annotation, linked-data publishing, content discovery, and integration of sports information [#bbcsport-ontology]_. + +**Example Usage**: +Annotate a sports tournament database with BBCSport terms to specify competitions, tournament events, participating teams or athletes, disciplines, and awards, enabling semantic search and integration across sports data publishing platforms [#bbcsport-ontology]_. Metrics & Statistics -------------------------- @@ -133,5 +138,10 @@ Use the following code to import this ontology programmatically: taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations -**Example Usage**: -Annotate a sports tournament database with BBCSport terms to specify the structure of the tournament, participating teams and athletes, event types (e.g., "final match"), and awards (e.g., "gold medal"), enabling semantic search and cross-platform sports analytics. +References +---------- + +.. [#bbcsport-ontology] BBC. n.d. + "Sport Ontology." + Available at: + `https://iptc.org/thirdparty/bbc-ontologies/sport.html `_ diff --git a/docs/source/benchmarking/news_and_media/bbcstoryline.rst b/docs/source/benchmarking/news_and_media/bbcstoryline.rst index 957308f..55e8601 100644 --- a/docs/source/benchmarking/news_and_media/bbcstoryline.rst +++ b/docs/source/benchmarking/news_and_media/bbcstoryline.rst @@ -25,9 +25,12 @@ BBC Storyline Ontology (BBCStoryline) ======================================================================================================== -The BBC Storyline Ontology is a generic semantic framework for describing, organizing, and managing news stories and narrative content across diverse media publishing platforms. It models the core concept of "Storyline" to distinguish between individual news content pieces (articles, reports, videos) and broader editorial narratives representing events or topics in the world. The ontology captures relationships between stories, events, entities (people, places, organizations), and time periods, enabling sophisticated content organization and discovery. BBCStoryline is designed to be flexible and adaptable, supporting different news publishers' approaches to story organization while providing standardized semantic structures. The ontology facilitates semantic linking of content across media platforms and enables advanced search, recommendation, and editorial workflow systems. +The BBC Storyline Ontology is a generic model for describing and organising news storylines [#bbcstoryline-ontology]_. It uses the central concept of Storyline to distinguish between an individual piece of news content, such as an article or report, and the broader editorial perspective on events occurring in the world [#bbcstoryline-ontology]_. -**Example Usage**: Represent a news story about a major political event using BBCStoryline terms to link individual articles (multiple reporters' perspectives), associated entities (politicians, organizations), timeline (chronological events), and related stories covering different aspects of the broader storyline. +The ontology supports the organisation of storyline components, which may be ordered using an index, arranged temporally, or represented as a graph to describe parallel developments [#bbcstoryline-ontology]_. By providing a structured semantic framework, the BBC Storyline Ontology supports content organisation, semantic linking, discovery, and management of news narratives across media publishing platforms [#bbcstoryline-ontology]_. + +**Example Usage**: +Represent a news storyline about a major political event by linking individual articles, reports, related events, entities, time periods, and parallel developments into a broader editorial storyline, enabling semantic search and organisation of related news content [#bbcstoryline-ontology]_. Metrics & Statistics -------------------------- @@ -136,3 +139,11 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#bbcstoryline-ontology] BBC. 2013. + "Storyline Ontology." + Available at: + `https://iptc.org/thirdparty/bbc-ontologies/storyline.html `_ diff --git a/docs/source/benchmarking/news_and_media/bbcwildlife.rst b/docs/source/benchmarking/news_and_media/bbcwildlife.rst index 1e71fb2..5cb367a 100644 --- a/docs/source/benchmarking/news_and_media/bbcwildlife.rst +++ b/docs/source/benchmarking/news_and_media/bbcwildlife.rst @@ -25,7 +25,12 @@ BBC Wildlife Ontology (BBCWildlife) ======================================================================================================== -The BBC Wildlife Ontology is a specialized vocabulary for describing and classifying biological species, taxa, and their ecological characteristics in a machine-readable format. It provides standardized terminology for representing biological classification hierarchies including species names, taxonomic ranks (kingdom, phylum, class, order, family, genus, species), and relationships between taxa. The ontology enables semantic annotation of wildlife information including habitat descriptions, conservation status, behavioral characteristics, geographic distribution, and ecological roles. BBCWildlife supports wildlife content discovery and semantic linking across BBC media platforms and broader biodiversity databases. The ontology facilitates knowledge integration in natural history and biodiversity research by providing standardized semantic representations of organisms and their ecological contexts. **Example Usage**: Annotate a wildlife documentary or encyclopedia entry about a species (e.g., tiger, African elephant) with BBCWildlife terms for taxonomic classification, habitat (forest, savanna, wetland), conservation status (endangered, vulnerable), behavior (predatory, migratory), and geographic distribution. +The BBC Wildlife Ontology is a lightweight ontology for describing biological species and related taxa [#bbcwildlife-ontology]_. It provides a structured vocabulary for describing taxon names and ranks, relationships between taxa, habitats, conservation status, modes of life, behavioural characteristics, and topic relations to web documents or multimedia objects featuring a taxon [#bbcwildlife-ontology]_. + +The ontology was originally designed to support data publishing for the BBC Wildlife Finder application, but it is intended to be applicable to a wider range of biological data publishing use cases [#bbcwildlife-ontology]_. It is designed to interoperate with more specialized ontologies used in taxonomy, ecology, environmental science, and bioinformatics [#bbcwildlife-ontology]_. By providing standardized terms for taxa and wildlife-related information, the BBC Wildlife Ontology supports semantic annotation, linked-data publishing, content discovery, and integration of biodiversity-related information [#bbcwildlife-ontology]_. + +**Example Usage**: +Annotate a wildlife documentary or species entry with BBC Wildlife Ontology terms to specify a taxon, its rank, habitat, conservation status, mode of life, behavioural characteristics, and related multimedia content, enabling semantic search and integration across wildlife and biodiversity data platforms [#bbcwildlife-ontology]_. Metrics & Statistics -------------------------- @@ -134,3 +139,11 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#bbcwildlife-ontology] BBC. 2013. + "Wildlife Ontology." + Available at: + `https://iptc.org/thirdparty/bbc-ontologies/wo.html `_ diff --git a/docs/source/benchmarking/scholarly_knowledge/aiiso.rst b/docs/source/benchmarking/scholarly_knowledge/aiiso.rst index 19abe0c..6f447d0 100644 --- a/docs/source/benchmarking/scholarly_knowledge/aiiso.rst +++ b/docs/source/benchmarking/scholarly_knowledge/aiiso.rst @@ -25,9 +25,9 @@ Academic Institution Internal Structure Ontology (AIISO) ======================================================================================================== -AIISO is a specialized ontology for describing and formalizing the internal organizational structures, hierarchies, and administrative units of academic institutions including universities, colleges, and research centers. It provides standardized vocabulary for representing academic departments, faculties, schools, research groups, and their relationships within an institution's organizational hierarchy. AIISO is designed for integration with complementary ontologies including Participation (for describing role participation), FOAF (for person information), and aiiso-roles (for person roles within institutions). The ontology enables semantic representation of academic organizational information, supporting institutional data integration, organizational mapping, and staff/student management systems. AIISO facilitates interoperability in academic information systems and institutional repositories by providing standardized semantic definitions of academic structures. +AIISO, the Academic Institution Internal Structure Ontology, is a specialized ontology for describing and formalizing the internal organizational structures, hierarchies, and administrative units of academic institutions, including universities, colleges, schools, departments, faculties, and research centers [#aiiso-vocab]_ [#aiiso-schema]_. It provides standardized classes and properties for representing academic organizational units, knowledge groupings, courses, modules, programmes, subjects, and their relationships within an institution's hierarchy [#aiiso-schema]_. AIISO is designed for integration with complementary vocabularies including Participation, FOAF, and AIISO Roles, enabling the description of people and the roles they play within academic institutions [#aiiso-vocab]_. The ontology supports semantic representation of academic organizational information, enabling institutional data integration, organizational mapping, academic resource description, and staff or student information systems [#aiiso-vocab]_ [#aiiso-schema]_. By providing standardized semantic definitions for academic structures, AIISO facilitates interoperability in academic information systems, institutional repositories, and linked open education data [#aiiso-vocab]_. -**Example Usage**: Represent an academic institution's structure with AIISO terms for faculties (Faculty of Science, Faculty of Engineering), departments (Computer Science Department, Physics Department), research groups, and their organizational relationships and hierarchies. +**Example Usage**: Represent an academic institution's structure with AIISO terms for faculties, departments, schools, research centers, programmes, courses, modules, and subjects, such as ``Faculty of Science``, ``Department of Computer Science``, and ``Artificial Intelligence Module``. This enables academic hierarchy modeling, semantic discovery, and integration across institutional information systems [#aiiso-schema]_. Metrics & Statistics -------------------------- @@ -136,3 +136,16 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#aiiso-vocab] Talis Information Ltd. 2008. + "Academic Institution Internal Structure Ontology (AIISO)." + Available at: + `https://vocab.org/aiiso/ `_ + +.. [#aiiso-schema] Talis Information Ltd. 2008. + "Academic Institution Internal Structure Ontology Schema." + Available at: + `https://vocab.org/aiiso/schema-20080514.html `_ diff --git a/docs/source/benchmarking/scholarly_knowledge/cito.rst b/docs/source/benchmarking/scholarly_knowledge/cito.rst index 43b54b1..9bec3c0 100644 --- a/docs/source/benchmarking/scholarly_knowledge/cito.rst +++ b/docs/source/benchmarking/scholarly_knowledge/cito.rst @@ -23,14 +23,14 @@ Citation Typing Ontology (CiTO) ======================================================================================================== -The Citation Typing Ontology (CiTO) is an ontology that enables characterization of the nature or type of citations, both factually and rhetorically. It provides a structured vocabulary for describing citation types, relationships, and contexts, supporting both theoretical and experimental research in scholarly communication. +The Citation Typing Ontology (CiTO) is an ontology that enables characterization of the nature or type of citations, both factually and rhetorically [#cito-spec]_ [#cito-paper]_. It provides a structured vocabulary for describing citation relationships between citing and cited scholarly works, including whether a citation supports, extends, disputes, uses methods from, reviews, or obtains background from another work [#cito-spec]_. CiTO enables citation metadata to describe not only the existence of a citation link, but also the author's citation intent and the scholarly context of that relationship [#cito-spec]_ [#cito-paper]_. -The ontology employs a class-based modeling approach, defining classes for different types of citations, relationships, and contexts, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. CiTO supports the integration of data from various sources, promoting interoperability and data-driven research in scholarly communication. +The ontology employs an OWL-based modeling approach, defining citation properties such as ``cito:cites`` and more specific subproperties for different citation functions [#cito-spec]_. These properties are organized into hierarchies, enabling structured retrieval, reasoning, and analysis of citation networks [#cito-spec]_. CiTO supports integration of citation data from different scholarly sources and publishing platforms, promoting interoperability and data-driven research in scholarly communication [#cito-paper]_. -Typical applications of CiTO include the development of new citation analysis methods, the optimization of citation practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, CiTO enhances collaboration and innovation in the field of scholarly communication. +Typical applications of CiTO include semantic citation annotation, citation network analysis, scholarly knowledge graph construction, citation intent analysis, and integration of bibliographic datasets for advanced analytics and knowledge discovery [#cito-paper]_. By providing a standardized vocabulary and framework, CiTO enhances semantic interoperability and supports richer analysis of how scholarly works relate to one another [#cito-spec]_ [#cito-paper]_. **Example Usage**: -Annotate a research paper with CiTO terms to specify citation types, relationships, and contexts, enabling semantic search and integration with scholarly communication platforms. +Annotate a research paper with CiTO terms to specify citation relationships and contexts, such as ``cito:citesAsEvidence``, ``cito:extends``, ``cito:usesMethodIn``, or ``cito:disagreesWith``. This enables semantic search, citation intent analysis, and integration with scholarly communication platforms [#cito-spec]_ [#cito-paper]_. Metrics & Statistics -------------------------- @@ -139,3 +139,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#cito-spec] SPAR Ontologies. 2018. + "CiTO, the Citation Typing Ontology." + Available at: + `https://sparontologies.github.io/cito/current/cito.html `_ + +.. [#cito-paper] Shotton, David. 2010. + "CiTO, the Citation Typing Ontology." + *Journal of Biomedical Semantics* 1(Suppl 1): S6. + DOI: + `10.1186/2041-1480-1-S1-S6 `_ diff --git a/docs/source/benchmarking/scholarly_knowledge/cso.rst b/docs/source/benchmarking/scholarly_knowledge/cso.rst index 43e1920..ef34cbf 100644 --- a/docs/source/benchmarking/scholarly_knowledge/cso.rst +++ b/docs/source/benchmarking/scholarly_knowledge/cso.rst @@ -25,9 +25,9 @@ Computer Science Ontology (CSO) ======================================================================================================== -The Computer Science Ontology (CSO) is a large-scale semantic resource that provides a comprehensive vocabulary of research areas, topics, and concepts in computer science organized in a hierarchical taxonomy. It covers diverse computing domains including artificial intelligence, software engineering, networking, databases, human-computer interaction, and emerging areas, enabling precise semantic annotation of research contributions. CSO supports sophisticated relationship modeling including superTopicOf (for topic hierarchies), contributesTo (linking topics to solutions), and other domain-relevant relationships enabling knowledge discovery and research mapping. The ontology enables automated research classification, literature organization, and expertise matching by providing standardized semantic definitions of computer science research areas. CSO facilitates semantic interoperability in scholarly information systems, research management platforms, and academic recommendation systems. +The Computer Science Ontology (CSO) is a large-scale semantic resource that provides a comprehensive vocabulary of research areas, topics, and concepts in computer science, organized through semantic relationships and topic hierarchies [#cso-paper]_ [#cso-portal]_. It covers diverse computing domains, including artificial intelligence, software engineering, networking, databases, human-computer interaction, security, information retrieval, and emerging research areas, enabling precise semantic annotation of scholarly contributions [#cso-paper]_. CSO supports relationship modeling through properties such as ``superTopicOf`` for topic hierarchies, ``contributesTo`` for linking research topics to broader research areas, and other domain-relevant relationships that support knowledge discovery and research mapping [#cso-paper]_ [#cso-portal]_. The ontology enables automated research classification, literature organization, topic extraction, expertise matching, trend analysis, and scholarly knowledge graph construction by providing standardized semantic descriptions of computer science research areas [#cso-paper]_. CSO facilitates semantic interoperability in scholarly information systems, research management platforms, academic search engines, and recommendation systems [#cso-portal]_. -**Example Usage**: Annotate a research paper or researcher profile with CSO terms such as "Machine Learning" (main topic), "Deep Learning" (subtopic), "Natural Language Processing" (related topic), and associated research methods and applications to enable semantic discovery of related research. +**Example Usage**: Annotate a research paper or researcher profile with CSO terms such as ``Machine Learning`` as a main topic, ``Deep Learning`` as a subtopic, and ``Natural Language Processing`` as a related topic. This enables semantic discovery of related research, topic-based search, expertise matching, and research landscape analysis [#cso-paper]_ [#cso-portal]_. Metrics & Statistics -------------------------- @@ -136,3 +136,18 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#cso-paper] Salatino, Angelo A., Thiviyan Thanapalasingam, + Andrea Mannocci, Francesco Osborne, and Enrico Motta. 2018. + "The Computer Science Ontology: A Large-Scale Taxonomy of Research Areas." + *The Semantic Web -- ISWC 2018*. + DOI: + `10.1007/978-3-030-00668-6_12 `_ + +.. [#cso-portal] Knowledge Media Institute, The Open University. n.d. + "Computer Science Ontology." + Available at: + `https://cso.kmi.open.ac.uk/ `_ diff --git a/docs/source/benchmarking/scholarly_knowledge/datacite.rst b/docs/source/benchmarking/scholarly_knowledge/datacite.rst index def878c..9f8aed1 100644 --- a/docs/source/benchmarking/scholarly_knowledge/datacite.rst +++ b/docs/source/benchmarking/scholarly_knowledge/datacite.rst @@ -25,9 +25,9 @@ DataCite Ontology (DataCite) ======================================================================================================== -The DataCite Ontology is an RDF-based representation of the DataCite Metadata Schema, providing standardized vocabulary and semantic structure for describing research data and digital objects with comprehensive citation and identification metadata. It enables formal representation of essential dataset properties including creators, titles, publication dates, contributors, funding information, and relationships to other scholarly resources. DataCite provides machine-readable definitions of metadata properties for accurate and consistent identification, retrieval, and citation of diverse digital resources including datasets, software, and research outputs. The ontology supports FAIR data principles by enabling standardized, interoperable representation of dataset metadata in linked data formats (RDF, JSON-LD). DataCite facilitates data discovery, citation tracking, and research impact assessment by providing standardized semantic metadata structures. +The DataCite Ontology is an RDF/OWL-based representation of the DataCite Metadata Schema, providing a standardized semantic structure for describing research data and other digital research objects with citation and identification metadata [#datacite-ontology]_ [#datacite-schema]_. It enables formal representation of essential metadata properties, including identifiers, creators, titles, publishers, publication dates, contributors, subjects, funding information, resource types, and relationships to other scholarly resources [#datacite-schema]_. The ontology allows DataCite metadata properties to be described in RDF, supporting machine-readable representation of resources for accurate identification, retrieval, citation, and linking [#datacite-ontology]_. DataCite metadata supports the description of diverse research outputs, including datasets, software, publications, and other digital objects [#datacite-schema]_. By providing standardized semantic metadata structures, the DataCite Ontology supports data discovery, citation tracking, scholarly linking, research impact assessment, and integration with linked data and FAIR-oriented research infrastructures [#datacite-ontology]_ [#datacite-schema]_. -**Example Usage**: Represent a published research dataset with DataCite ontology terms including persistent identifier (DOI), creators and contributors (with ORCID), publication date, subject areas, funding information, and related publications to enable proper citation and discovery across research repositories. +**Example Usage**: Represent a published research dataset with DataCite ontology terms for its persistent identifier, such as a DOI; creators and contributors, including ORCID identifiers where available; title, publisher, publication date, subject areas, funding information, resource type, and related publications. This enables proper citation, discovery, linking, and reuse across research repositories and scholarly information systems [#datacite-ontology]_ [#datacite-schema]_. Metrics & Statistics -------------------------- @@ -136,3 +136,16 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#datacite-ontology] SPAR Ontologies. n.d. + "The DataCite Ontology." + Available at: + `https://sparontologies.github.io/datacite/current/datacite.html `_ + +.. [#datacite-schema] DataCite. n.d. + "DataCite Metadata Schema." + Available at: + `https://schema.datacite.org/ `_ diff --git a/docs/source/benchmarking/scholarly_knowledge/dcat.rst b/docs/source/benchmarking/scholarly_knowledge/dcat.rst index 566fb23..5595064 100644 --- a/docs/source/benchmarking/scholarly_knowledge/dcat.rst +++ b/docs/source/benchmarking/scholarly_knowledge/dcat.rst @@ -23,14 +23,14 @@ Data Catalog Vocabulary (DCAT) ======================================================================================================== -The Data Catalog Vocabulary (DCAT) is an RDF vocabulary designed to facilitate interoperability between data catalogs published on the Web. It provides a structured vocabulary for describing datasets, data services, and catalogs, supporting both theoretical and experimental research in data management. +The Data Catalog Vocabulary (DCAT) is an RDF vocabulary designed to facilitate interoperability between data catalogs published on the Web [#dcat-version3]_ [#dcat-cdif]_. It provides a structured semantic model for describing catalogs, datasets, dataset series, data services, distributions, catalog records, and related metadata, enabling dataset descriptions to be shared and processed in a machine-readable form [#dcat-version3]_. DCAT supports dataset discovery by allowing metadata from different catalogs to be aggregated, searched, exchanged, and reused through a common vocabulary [#dcat-version3]_ [#dcat-cdif]_. -The ontology employs a class-based modeling approach, defining classes for different types of datasets, data services, and catalogs, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. DCAT supports the integration of data from various sources, promoting interoperability and data-driven research in data management. +The ontology defines classes and properties for describing catalog resources, including dataset titles, descriptions, publishers, themes, keywords, access URLs, download URLs, formats, licenses, temporal coverage, spatial coverage, and relationships between datasets, distributions, and services [#dcat-version3]_. These structured metadata descriptions support efficient retrieval, catalog integration, and interoperability across heterogeneous data infrastructures [#dcat-cdif]_. -Typical applications of DCAT include the development of new data cataloging methods, the optimization of data management practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, DCAT enhances collaboration and innovation in the field of data management. +Typical applications of DCAT include open data portals, research data repositories, government data catalogs, enterprise data management platforms, and cross-domain metadata exchange systems [#dcat-version3]_ [#dcat-cdif]_. By providing a standardized semantic framework, DCAT enhances data discoverability, metadata interoperability, catalog integration, and reuse across diverse data management environments [#dcat-version3]_. **Example Usage**: -Annotate a data catalog with DCAT terms to specify dataset types, data services, and catalog structures, enabling semantic search and integration with data management platforms. +Annotate a data catalog with DCAT terms to describe datasets, data services, distributions, catalog records, publishers, access URLs, licenses, formats, and thematic categories. This enables semantic search, metadata aggregation, federated catalog discovery, and integration with data management platforms [#dcat-version3]_ [#dcat-cdif]_. Metrics & Statistics -------------------------- @@ -139,3 +139,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#dcat-version3] W3C. 2024. + "Data Catalog Vocabulary (DCAT) - Version 3." + W3C Recommendation. + Available at: + `https://www.w3.org/TR/vocab-dcat-3/ `_ + +.. [#dcat-cdif] Cross-Domain Interoperability Framework. n.d. + "DCAT Metadata." + Available at: + `https://cross-domain-interoperability-framework.github.io/cdifbook/metadata/dcat.html `_ diff --git a/docs/source/benchmarking/scholarly_knowledge/duo.rst b/docs/source/benchmarking/scholarly_knowledge/duo.rst index d625ae8..a9096b9 100644 --- a/docs/source/benchmarking/scholarly_knowledge/duo.rst +++ b/docs/source/benchmarking/scholarly_knowledge/duo.rst @@ -25,9 +25,9 @@ Data Use Ontology (DUO) ======================================================================================================== -The Data Use Ontology (DUO) is a comprehensive vocabulary for formally representing and managing data use restrictions, permissions, and conditions that govern access to and usage of biomedical and life sciences research data. It provides standardized definitions of data use constraints including disease-specific research restrictions, commercial use prohibitions, publication acknowledgment requirements, and ethical/policy-based limitations. DUO enables data stewards and repositories to precisely specify how datasets can be used, facilitating automated enforcement of data use agreements and supporting secondary data reuse in compliant ways. The ontology bridges the gap between legal/ethical restrictions and technical implementations by providing machine-readable representations of complex data use policies. DUO supports FAIR data principles by enabling discoverable, interoperable, and reusable data through clear expression of usage conditions. +The Data Use Ontology (DUO) is a controlled vocabulary and ontology for formally representing data use restrictions, permissions, and conditions that govern access to and use of biomedical, clinical, and life sciences research data [#duo-obofoundry]_ [#duo-paper]_. DUO provides standardized terms for describing data use conditions, including general research use, health or medical research use, disease-specific restrictions, population-origin restrictions, commercial use limitations, publication requirements, and ethics or policy-based conditions [#duo-obofoundry]_ [#duo-paper]_. It enables data stewards, repositories, and data access committees to precisely specify how datasets may be used, supporting responsible secondary reuse and helping match researcher requests with dataset permissions [#duo-paper]_. DUO bridges policy and technical implementation by expressing complex data use conditions in a human-readable and machine-readable form [#duo-paper]_. By providing structured data use terms, DUO supports discoverability, interoperability, compliant reuse, and FAIR-oriented management of sensitive research datasets [#duo-obofoundry]_ [#duo-paper]_. -**Example Usage**: Annotate a biomedical research dataset with DUO terms to specify permissions (medical research only), restrictions (no commercial use), and requirements (publication acknowledgment, return of results to participants) enabling automated compliance checking and appropriate data sharing decisions. +**Example Usage**: Annotate a biomedical research dataset with DUO terms to specify permissions such as ``general research use`` or ``health/medical/biomedical research``, restrictions such as ``no commercial use`` or disease-specific use, and requirements such as publication acknowledgment. This enables data access review, automated compliance checking, and appropriate data sharing decisions [#duo-obofoundry]_ [#duo-paper]_. Metrics & Statistics -------------------------- @@ -136,3 +136,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#duo-obofoundry] OBO Foundry. n.d. + "Data Use Ontology." + Available at: + `https://obofoundry.org/ontology/duo.html `_ + +.. [#duo-paper] Lawson, James, et al. 2021. + "The Data Use Ontology to streamline responsible access to human biomedical datasets." + *Cell Genomics* 1(2): 100028. + DOI: + `10.1016/j.xgen.2021.100028 `_ diff --git a/docs/source/benchmarking/scholarly_knowledge/eurio.rst b/docs/source/benchmarking/scholarly_knowledge/eurio.rst index 099090c..78cc571 100644 --- a/docs/source/benchmarking/scholarly_knowledge/eurio.rst +++ b/docs/source/benchmarking/scholarly_knowledge/eurio.rst @@ -23,14 +23,14 @@ EUropean Research Information Ontology (EURIO) ======================================================================================================== -The EUropean Research Information Ontology (EURIO) conceptualizes, formally encodes, and makes available in an open, structured, and machine-readable format data about research projects funded by the EU's framework programmes for research and innovation. It provides a structured vocabulary for representing research projects, funding information, and related data, supporting both theoretical and experimental research in research information management. +The EUropean Research Information Ontology (EURIO) conceptualizes, formally encodes, and makes available data about research projects funded by the European Union's framework programmes for research and innovation in an open, structured, and machine-readable format [#eurio-euvoc]_ [#eurio-paper]_. EURIO was developed for CORDIS and the Publications Office of the European Union to represent research project information as semantic data, improving its visibility, accessibility, interoperability, and reuse [#eurio-paper]_. It provides a structured vocabulary for describing research projects, funding schemes, grants, organizations, persons, project results, publications, and related research information [#eurio-euvoc]_ [#eurio-paper]_. -The ontology employs a class-based modeling approach, defining classes for different types of research projects, funding information, and related data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. EURIO supports the integration of data from various sources, promoting interoperability and data-driven research in research information management. +The ontology uses a class-based semantic modeling approach, defining classes and properties for research projects, participants, funding information, outputs, and administrative metadata [#eurio-paper]_. These concepts allow EU-funded research information to be organized, linked, queried, and integrated with other semantic resources and reference data assets [#eurio-euvoc]_ [#eurio-paper]_. EURIO supports interoperability in research information management by enabling project data from CORDIS and related sources to be represented consistently in linked data formats [#eurio-euvoc]_. -Typical applications of EURIO include the development of new research information management methods, the optimization of research project management practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, EURIO enhances collaboration and innovation in the field of research information management. +Typical applications of EURIO include semantic publication of EU research project data, research information management, project discovery, funding analysis, institutional reporting, knowledge graph construction, and integration of research outputs across platforms [#eurio-paper]_. By providing a standardized ontology for EU-funded research information, EURIO supports data discovery, analytics, interoperability, and knowledge sharing across research information systems [#eurio-euvoc]_ [#eurio-paper]_. **Example Usage**: -Annotate a research project with EURIO terms to specify project types, funding information, and related data, enabling semantic search and integration with research information management platforms. +Annotate an EU-funded research project with EURIO terms to specify the project title, acronym, grant information, funding programme, participating organizations, researchers, project duration, deliverables, publications, and related results. This enables semantic search, project discovery, funding analysis, and integration with research information management platforms [#eurio-euvoc]_ [#eurio-paper]_. Metrics & Statistics -------------------------- @@ -139,3 +139,16 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#eurio-euvoc] Publications Office of the European Union. n.d. + "EUropean Research Information Ontology (EURIO)." + Available at: + `https://op.europa.eu/en/web/eu-vocabularies/eurio `_ + +.. [#eurio-paper] Publications Office of the European Union. 2020. + "EURIO: an ontology for publishing research projects' data." + Available at: + `https://publications.europa.eu/resource/cellar/369859bb-3611-11eb-b27b-01aa75ed71a1.0001.01/DOC_1 `_ diff --git a/docs/source/benchmarking/scholarly_knowledge/expo.rst b/docs/source/benchmarking/scholarly_knowledge/expo.rst index 85683c5..0997eb8 100644 --- a/docs/source/benchmarking/scholarly_knowledge/expo.rst +++ b/docs/source/benchmarking/scholarly_knowledge/expo.rst @@ -23,14 +23,14 @@ Ontology of Scientific Experiments (EXPO) ======================================================================================================== -The Ontology of Scientific Experiments (EXPO) formalizes generic knowledge about scientific experimental design, methodology, and results representation. It provides a structured vocabulary for representing scientific experiments, methodologies, and results, supporting both theoretical and experimental research in scientific experimentation. +The Ontology of Scientific Experiments (EXPO) formalizes generic knowledge about scientific experimental design, methodology, and results representation [#expo-sourceforge]_ [#expo-paper]_. It provides a structured OWL-based vocabulary for representing experiments, experimental goals, experimental design, methods, actions, objects, equipment, results, conclusions, and related scientific context [#expo-sourceforge]_ [#expo-paper]_. EXPO is intended to support the formal description of experiments for efficient analysis, annotation, sharing, comparison, and reuse of experimental results across scientific domains [#expo-paper]_. The ontology links the Suggested Upper Merged Ontology (SUMO) with subject-specific ontologies of experiments, allowing general experimental concepts to be specialized for particular domains [#expo-paper]_. EXPO has been demonstrated across different experimental domains, including high-energy physics and phylogenetics, showing its usefulness for making experimental goals, structures, and assumptions more explicit [#expo-paper]_. -The ontology employs a class-based modeling approach, defining classes for different types of experiments, methodologies, and results, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. EXPO supports the integration of data from various sources, promoting interoperability and data-driven research in scientific experimentation. +The ontology uses a class-based modeling approach, defining concepts for experiment types, experimental goals, methodologies, experimental actions, experimental objects, equipment, results, and conclusions [#expo-sourceforge]_ [#expo-paper]_. These concepts are organized through hierarchical and relational structures, enabling semantic annotation, retrieval, comparison, and analysis of experimental knowledge [#expo-sourceforge]_. EXPO supports interoperability by providing a common ontology for describing experiments across disciplines, while still allowing extension through domain-specific experimental ontologies [#expo-paper]_. -Typical applications of EXPO include the development of new experimental design methods, the optimization of experimental methodologies, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, EXPO enhances collaboration and innovation in the field of scientific experimentation. +Typical applications of EXPO include semantic annotation of scientific experiments, integration of experimental metadata from different sources, comparison of experimental designs, representation of methods and results, and support for reproducibility and knowledge discovery [#expo-sourceforge]_ [#expo-paper]_. By providing a standardized vocabulary and framework, EXPO enhances sharing, analysis, and reuse of experimental knowledge across scientific communities [#expo-paper]_. **Example Usage**: -Annotate a scientific experiment with EXPO terms to specify experiment types, methodologies, and results, enabling semantic search and integration with scientific experimentation platforms. +Annotate a scientific experiment with EXPO terms to specify its experimental goal, design, methodology, experimental object, equipment, actions, results, and conclusions. This enables semantic search, experiment comparison, reproducibility support, and integration with scientific experimentation platforms [#expo-sourceforge]_ [#expo-paper]_. Metrics & Statistics -------------------------- @@ -139,3 +139,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#expo-sourceforge] EXPO. n.d. + "EXPO: Ontology of Scientific Experiments." + Available at: + `https://expo.sourceforge.net/ `_ + +.. [#expo-paper] Soldatova, Larisa N., and Ross D. King. 2006. + "An ontology of scientific experiments." + *Journal of The Royal Society Interface* 3(11): 795--803. + DOI: + `10.1098/rsif.2006.0134 `_ diff --git a/docs/source/benchmarking/scholarly_knowledge/framester.rst b/docs/source/benchmarking/scholarly_knowledge/framester.rst index 735e981..2dad1f8 100644 --- a/docs/source/benchmarking/scholarly_knowledge/framester.rst +++ b/docs/source/benchmarking/scholarly_knowledge/framester.rst @@ -23,14 +23,14 @@ Framester Ontology (Framester) ======================================================================================================== -Framester is a frame-based ontological resource acting as a hub between linguistic resources such as FrameNet, WordNet, VerbNet, BabelNet, DBpedia, Yago, DOLCE-Zero, and leveraging this wealth of links to create an interoperable predicate space formalized according to frame semantics and semiotics. Framester uses WordNet and FrameNet at its core, expands it to other resources transitively, and represents them in a formal version of frame semantics. It provides a structured vocabulary for representing linguistic resources, supporting both theoretical and experimental research in linguistics. +Framester is a frame-based ontological resource that acts as a hub between linguistic and factual resources such as FrameNet, WordNet, VerbNet, BabelNet, DBpedia, YAGO, and DOLCE-Zero [#framester-home]_ [#framester-paper]_. It uses WordNet and FrameNet as core resources, expands connections to other resources transitively, and represents them in a formal version of frame semantics [#framester-paper]_. By leveraging links across lexical, ontological, and linked data resources, Framester creates an interoperable predicate space that supports linguistic linked data integration, frame-based knowledge representation, and semantic reasoning [#framester-home]_ [#framester-paper]_. -The ontology employs a class-based modeling approach, defining classes for different types of linguistic resources, frames, and relationships, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. Framester supports the integration of data from various sources, promoting interoperability and data-driven research in linguistics. +The ontology provides a structured framework for representing frames, frame elements, lexical units, semantic roles, synsets, and relationships between linguistic and factual resources [#framester-paper]_. Framester applies a formal treatment of Fillmore-style frame semantics, enabling OWL querying and reasoning over a large frame-based knowledge graph [#framester-paper]_. It also supports applications such as word frame disambiguation, semantic role labeling, knowledge graph construction from text, and integration of heterogeneous linguistic datasets [#framester-paper]_. -Typical applications of Framester include the development of new linguistic resource integration methods, the optimization of linguistic data management practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, Framester enhances collaboration and innovation in the field of linguistics. +Typical applications of Framester include linguistic resource alignment, semantic annotation, natural language understanding, frame detection, semantic role analysis, and enrichment of knowledge graphs with frame-based meaning representations [#framester-home]_ [#framester-paper]_. By providing a shared frame-oriented semantic layer, Framester improves interoperability between lexical resources and supports advanced research in computational linguistics, semantic web technologies, and knowledge discovery [#framester-paper]_. **Example Usage**: -Annotate a linguistic dataset with Framester terms to specify linguistic resources, frames, and relationships, enabling semantic search and integration with linguistic information management platforms. +Annotate a linguistic dataset with Framester terms to represent evoked frames, lexical units, semantic roles, frame elements, and mappings to resources such as FrameNet, WordNet, VerbNet, BabelNet, DBpedia, and YAGO. This enables semantic search, frame-based reasoning, word frame disambiguation, and integration with linguistic linked data platforms [#framester-home]_ [#framester-paper]_. Metrics & Statistics -------------------------- @@ -139,3 +139,18 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +eferences +---------- + +.. [#framester-home] Framester. n.d. + "Framester." + Available at: + `https://etna.istc.cnr.it/framester_web/ `_ + +.. [#framester-paper] Gangemi, Aldo, Mehwish Alam, Luigi Asprino, + Valentina Presutti, and Diego Reforgiato Recupero. 2016. + "Framester: A Wide Coverage Linguistic Linked Data Hub." + *The Semantic Web: ESWC 2016 Satellite Events / EKAW 2016*, Lecture Notes in Computer Science. + Available at: + `https://www.fiz-karlsruhe.de/sites/default/files/FIZ/Dokumente/Forschung/ISE/Publications/EKAW-16.pdf `_ diff --git a/docs/source/benchmarking/scholarly_knowledge/frapo.rst b/docs/source/benchmarking/scholarly_knowledge/frapo.rst index 86ed947..ac35abe 100644 --- a/docs/source/benchmarking/scholarly_knowledge/frapo.rst +++ b/docs/source/benchmarking/scholarly_knowledge/frapo.rst @@ -23,14 +23,14 @@ Funding, Research Administration and Projects Ontology (FRAPO) ======================================================================================================== -The Funding, Research Administration and Projects Ontology (FRAPO) is an ontology for describing the administrative information of research projects, e.g., grant applications, funding bodies, project partners, etc. It provides a structured vocabulary for representing research administration, funding, and project information, supporting both theoretical and experimental research in research administration. +The Funding, Research Administration and Projects Ontology (FRAPO) is an ontology for describing administrative information relating to grant funding and research projects [#frapo-spar]_ [#frapo-github]_. It provides a structured vocabulary for representing grant applications, funding bodies, research projects, project partners, project-related roles, and other administrative information commonly managed in Current Research Information Systems (CRIS) [#frapo-spar]_. FRAPO is CERIF-compliant and written in OWL 2 DL, making it suitable for representing research administration information in semantic web and linked data environments [#frapo-github]_. -The ontology employs a class-based modeling approach, defining classes for different types of research administration, funding, and project information, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. FRAPO supports the integration of data from various sources, promoting interoperability and data-driven research in research administration. +The ontology defines classes and properties for modeling funding, project administration, organizations, agents, and project-related relationships [#frapo-spar]_. It imports FOAF and is designed to work with related SPAR ontologies such as SCoRO for scholarly roles and FaBiO for documents such as grant applications, project plans, project reports, datasets, and journal articles [#frapo-spar]_. This allows FRAPO to represent research administration metadata while linking it to people, organizations, roles, outputs, and related documents [#frapo-spar]_. -Typical applications of FRAPO include the development of new research administration methods, the optimization of research project management practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, FRAPO enhances collaboration and innovation in the field of research administration. +Typical applications of FRAPO include research information management, grant administration, project reporting, Current Research Information Systems, funding analysis, institutional reporting, and integration of research project metadata across repositories and administrative systems [#frapo-spar]_ [#frapo-github]_. By providing a standardized semantic framework, FRAPO enhances interoperability, data integration, and knowledge discovery in research administration and project management contexts [#frapo-github]_. **Example Usage**: -Annotate a research project with FRAPO terms to specify administrative information, funding details, and project partners, enabling semantic search and integration with research administration platforms. +Annotate a research project with FRAPO terms to specify the funding body, grant application, project partners, administrative roles, project status, and related project documents. This enables semantic search, grant tracking, institutional reporting, and integration with research administration platforms and CRIS systems [#frapo-spar]_ [#frapo-github]_. Metrics & Statistics -------------------------- @@ -139,3 +139,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#frapo-spar] SPAR Ontologies. n.d. + "FRAPO, the Funding, Research Administration and Projects Ontology." + Available at: + `https://sparontologies.github.io/frapo/current/frapo.html `_ + +.. [#frapo-github] SPAR Ontologies. n.d. + "Funding, Research Administration and Projects Ontology (FRAPO)." + GitHub Repository. + Available at: + `https://github.com/sparontologies/frapo `_ diff --git a/docs/source/benchmarking/scholarly_knowledge/frbroo.rst b/docs/source/benchmarking/scholarly_knowledge/frbroo.rst index f598fb4..10c8593 100644 --- a/docs/source/benchmarking/scholarly_knowledge/frbroo.rst +++ b/docs/source/benchmarking/scholarly_knowledge/frbroo.rst @@ -23,14 +23,14 @@ Functional Requirements for Bibliographic Records - object-oriented (FRBRoo) ======================================================================================================== -The FRBRoo (Functional Requirements for Bibliographic Records - object-oriented) initiative is a joint effort of the CIDOC Conceptual Reference Model and Functional Requirements for Bibliographic Records international working groups to establish a formal ontology intended to capture and represent the underlying semantics of bibliographic information and to facilitate the integration, mediation, and interchange of bibliographic and museum information. It provides a structured vocabulary for representing bibliographic records, concepts, and relationships, supporting both theoretical and experimental research in bibliographic information management. +FRBRoo (Functional Requirements for Bibliographic Records - object-oriented) is a formal ontology developed through the harmonization of the FRBR family of bibliographic conceptual models with the CIDOC Conceptual Reference Model (CIDOC CRM) [#frbroo-cidoc]_ [#frbroo-ifla]_. It is intended to capture and represent the underlying semantics of bibliographic information and to facilitate the integration, mediation, and interchange of bibliographic and museum information [#frbroo-cidoc]_. FRBRoo provides an object-oriented conceptual model for representing bibliographic entities, works, expressions, manifestations, items, agents, creation processes, publication events, identifiers, and relationships between cultural heritage resources [#frbroo-ifla]_. -The ontology employs a class-based modeling approach, defining classes for different types of bibliographic records, concepts, and relationships, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. FRBRoo supports the integration of data from various sources, promoting interoperability and data-driven research in bibliographic information management. +The ontology reuses appropriate parts of CIDOC CRM and maps the entity-relationship models of the FRBR family into an object-oriented form [#frbroo-cidoc]_. This allows bibliographic information to be modeled together with museum and cultural heritage information using a shared semantic framework [#frbroo-cidoc]_ [#frbroo-ifla]_. FRBRoo supports semantic interoperability by making the structure and meaning of bibliographic records more explicit, enabling richer querying, reasoning, data mediation, and integration across library, archive, and museum systems [#frbroo-cidoc]_. -Typical applications of FRBRoo include the development of new bibliographic information management methods, the optimization of bibliographic record management practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, FRBRoo enhances collaboration and innovation in the field of bibliographic information management. +Typical applications of FRBRoo include bibliographic data modeling, cultural heritage knowledge graphs, library and museum data integration, semantic cataloging, authority data linking, and interoperability between bibliographic and museum information systems [#frbroo-ifla]_. By providing a formal ontology aligned with CIDOC CRM, FRBRoo supports the exchange, interpretation, and reuse of bibliographic and cultural heritage metadata across heterogeneous repositories [#frbroo-cidoc]_ [#frbroo-ifla]_. **Example Usage**: -Annotate a bibliographic record with FRBRoo terms to specify record types, concepts, and relationships, enabling semantic search and integration with bibliographic information management platforms. +Annotate a bibliographic record with FRBRoo terms to represent a literary work, its expressions, manifestations, physical items, creators, publication events, identifiers, and relationships to museum or archival objects. This enables semantic search, cultural heritage data integration, and cross-domain discovery across library and museum information systems [#frbroo-cidoc]_ [#frbroo-ifla]_. Metrics & Statistics -------------------------- @@ -139,3 +139,16 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#frbroo-cidoc] International Working Group on FRBR and CIDOC CRM Harmonisation. 2015. + "Definition of Object-Oriented FRBR." + Available at: + `https://cidoc-crm.org/sites/default/files/FRBRoo_V3.0.pdf `_ + +.. [#frbroo-ifla] IFLA. n.d. + "FRBRoo model." + Available at: + `https://www.iflastandards.info/fr/frbr/frbroo.html `_ diff --git a/docs/source/benchmarking/scholarly_knowledge/lexinfo.rst b/docs/source/benchmarking/scholarly_knowledge/lexinfo.rst index 6943222..5cb85e3 100644 --- a/docs/source/benchmarking/scholarly_knowledge/lexinfo.rst +++ b/docs/source/benchmarking/scholarly_knowledge/lexinfo.rst @@ -23,14 +23,14 @@ LexInfo (LexInfo) ======================================================================================================== -LexInfo allows us to associate linguistic information to elements in an ontology with respect to any level of linguistic description and expressivity. It provides a structured vocabulary for representing linguistic information, supporting both theoretical and experimental research in linguistics. +LexInfo is an ontology for associating linguistic information with elements in an ontology at different levels of linguistic description and expressivity [#lexinfo-ontology]_ [#lexinfo-paper]_. It was originally developed to support the lemon model and is now used as a data category ontology for OntoLex-Lemon, providing linguistic categories for describing lexical resources in RDF relative to ontologies [#lexinfo-ontology]_. LexInfo enables the representation of linguistic information such as parts of speech, grammatical gender, number, case, syntactic frames, subcategorization patterns, and other morphosyntactic and lexical properties [#lexinfo-ontology]_ [#lexinfo-paper]_. -The ontology employs a class-based modeling approach, defining classes for different types of linguistic information, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. LexInfo supports the integration of data from various sources, promoting interoperability and data-driven research in linguistics. +The ontology supports ontology-lexicon interfaces by making it possible to connect ontology entities, such as classes, properties, and individuals, with their lexical realizations and linguistic descriptions [#lexinfo-paper]_. This is important for ontology-based information extraction, ontology learning from text, question answering, ontology verbalization, lexical data publication, and multilingual linked data applications [#lexinfo-paper]_. LexInfo provides a reusable semantic vocabulary for describing linguistic features consistently across lexical resources, supporting interoperability between ontologies, lexicons, and natural language processing systems [#lexinfo-ontology]_. -Typical applications of LexInfo include the development of new linguistic information management methods, the optimization of linguistic data management practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, LexInfo enhances collaboration and innovation in the field of linguistics. +Typical applications of LexInfo include semantic annotation of lexical entries, modeling linguistic features in OntoLex-Lemon lexicons, integrating heterogeneous lexical datasets, supporting ontology verbalization, and enriching knowledge graphs with linguistic metadata [#lexinfo-ontology]_ [#lexinfo-paper]_. By providing a standardized vocabulary for linguistic data categories, LexInfo enhances interoperability and reuse in computational linguistics, semantic web, and ontology engineering workflows [#lexinfo-paper]_. **Example Usage**: -Annotate an ontology with LexInfo terms to specify linguistic information, enabling semantic search and integration with linguistic information management platforms. +Annotate an ontology-linked lexical entry with LexInfo terms to specify its part of speech, grammatical number, gender, syntactic behavior, or subcategorization frame. This enables semantic search, ontology verbalization, multilingual lexical data integration, and use of lexical resources in natural language processing applications [#lexinfo-ontology]_ [#lexinfo-paper]_. Metrics & Statistics -------------------------- @@ -139,3 +139,19 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#lexinfo-ontology] OntoLex Community Group. n.d. + "LexInfo: Data Category Ontology for OntoLex-Lemon." + GitHub Repository. + Available at: + `https://github.com/ontolex/lexinfo `_ + +.. [#lexinfo-paper] Cimiano, Philipp, Paul Buitelaar, + John McCrae, and Michael Sintek. 2011. + "LexInfo: A Declarative Model for the Lexicon-Ontology Interface." + *Journal of Web Semantics* 9(1): 29--51. + DOI: + `10.1016/j.websem.2010.11.001 `_ diff --git a/docs/source/benchmarking/scholarly_knowledge/metadata4ing.rst b/docs/source/benchmarking/scholarly_knowledge/metadata4ing.rst index af359c6..f3f1138 100644 --- a/docs/source/benchmarking/scholarly_knowledge/metadata4ing.rst +++ b/docs/source/benchmarking/scholarly_knowledge/metadata4ing.rst @@ -23,14 +23,14 @@ Metadata for Intelligent Engineering (Metadata4Ing) ======================================================================================================== -The ontology Metadata4Ing provides a framework for the semantic description of research data and of the whole data generation process, embracing the object of investigation, all sample and data manipulation methods and tools, the data files themselves, and the roles of persons and institutions. It provides a structured vocabulary for representing research data, processes, and roles, supporting both theoretical and experimental research in intelligent engineering. +The Metadata4Ing ontology provides a framework for the semantic description of research data and the complete data generation process, with a particular focus on engineering sciences and related disciplines [#m4i-nfdi]_. It covers the object of investigation, sample and data manipulation methods, tools, generated data files, and the roles of persons and institutions involved in the research process [#m4i-nfdi]_. Metadata4Ing supports the structured description of experiments, simulations, observations, workflows, and data-processing activities, enabling research data and its provenance context to be represented in a machine-readable form [#m4i-nfdi]_. -The ontology employs a class-based modeling approach, defining classes for different types of research data, processes, and roles, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. Metadata4Ing supports the integration of data from various sources, promoting interoperability and data-driven research in intelligent engineering. +The ontology uses a class-based modeling approach to describe research data, methods, tools, projects, organizations, people, roles, variables, and generated outputs [#m4i-nfdi]_. These semantic descriptions support data retrieval, interpretation, comparison, reuse, and integration across engineering research datasets and infrastructures [#m4i-nfdi]_. Metadata4Ing is especially useful for research data management because it helps document not only the final dataset, but also how the data was created, processed, and contextualized [#m4i-nfdi]_. -Typical applications of Metadata4Ing include the development of new research data management methods, the optimization of research data generation processes, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, Metadata4Ing enhances collaboration and innovation in the field of intelligent engineering. +Typical applications of Metadata4Ing include research data management, documentation of data generation workflows, semantic annotation of engineering datasets, FAIR data publication, provenance tracking, and knowledge graph construction for research processes [#m4i-nfdi]_. By providing a standardized vocabulary and framework, Metadata4Ing enhances interoperability, reproducibility, collaboration, and data-driven research in engineering and related scientific domains [#m4i-nfdi]_. **Example Usage**: -Annotate a research dataset with Metadata4Ing terms to specify data types, processes, and roles, enabling semantic search and integration with research data management platforms. +Annotate an engineering research dataset with Metadata4Ing terms to specify the object of investigation, experimental or simulation method, tools used, generated data files, variables, responsible persons, institutional roles, and related project context. This enables semantic search, reproducibility, provenance tracking, and integration with research data management platforms [#m4i-nfdi]_. Metrics & Statistics -------------------------- @@ -139,3 +139,11 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#m4i-nfdi] NFDI4Ing. n.d. + "Metadata4Ing." + Available at: + `https://nfdi4ing.de/m4i/ `_ diff --git a/docs/source/benchmarking/scholarly_knowledge/nfdicore.rst b/docs/source/benchmarking/scholarly_knowledge/nfdicore.rst index f975ace..7d442aa 100644 --- a/docs/source/benchmarking/scholarly_knowledge/nfdicore.rst +++ b/docs/source/benchmarking/scholarly_knowledge/nfdicore.rst @@ -23,14 +23,14 @@ National Research Data Infrastructure Ontology (NFDIcore) ======================================================================================================== -The National Research Data Infrastructure (NFDI) initiative has led to the formation of various consortia, each focused on developing a research data infrastructure tailored to its specific domain. To ensure interoperability across these consortia, the NFDIcore ontology has been developed as a mid-level ontology for representing metadata related to NFDI resources, including individuals, organizations, projects, data portals, and more. It provides a structured vocabulary for representing research data infrastructure, supporting both theoretical and experimental research in research data management. +The NFDIcore ontology is a mid-level ontology developed to support interoperability across the consortia of the National Research Data Infrastructure (NFDI) [#nfdicore-docs]_ [#nfdicore-github]_. It represents metadata related to NFDI resources, including individuals, organizations, projects, data portals, datasets, services, and other research infrastructure entities [#nfdicore-github]_. NFDIcore helps provide a shared semantic structure for describing the organization of NFDI and the research data resources made available by its project partners [#nfdicore-docs]_. -The ontology employs a class-based modeling approach, defining classes for different types of research data infrastructure, metadata, and related entities, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. NFDIcore supports the integration of data from various sources, promoting interoperability and data-driven research in research data management. +The ontology uses a class-based modeling approach to define reusable concepts and relations for research data infrastructure, metadata, organizations, projects, persons, services, repositories, and related resources [#nfdicore-docs]_. As a mid-level ontology, it bridges general upper-level concepts with more specific domain ontologies, supporting both flexibility and consistency across different NFDI consortia [#nfdicore-docs]_. This enables heterogeneous research data infrastructure metadata to be described, linked, queried, and integrated in a machine-readable form [#nfdicore-github]_. -Typical applications of NFDIcore include the development of new research data infrastructure methods, the optimization of research data management practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, NFDIcore enhances collaboration and innovation in the field of research data management. +Typical applications of NFDIcore include research data infrastructure documentation, metadata integration, semantic annotation of NFDI resources, data portal description, project and organization modeling, knowledge graph construction, and cross-consortium interoperability [#nfdicore-docs]_ [#nfdicore-github]_. By providing a standardized semantic framework, NFDIcore supports data discovery, integration, collaboration, and knowledge sharing across research data management platforms [#nfdicore-docs]_. **Example Usage**: -Annotate a research data infrastructure project with NFDIcore terms to specify infrastructure types, metadata, and related entities, enabling semantic search and integration with research data management platforms. +Annotate an NFDI research data infrastructure project with NFDIcore terms to describe participating organizations, researchers, projects, datasets, data portals, services, software repositories, and related metadata. This enables semantic search, cross-consortium integration, and interoperability with research data management platforms [#nfdicore-docs]_ [#nfdicore-github]_. Metrics & Statistics -------------------------- @@ -139,3 +139,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#nfdicore-docs] FIZ Karlsruhe. n.d. + "NFDIcore Ontology." + Available at: + `https://ise-fizkarlsruhe.github.io/nfdicore/ `_ + +.. [#nfdicore-github] ISE-FIZKarlsruhe. n.d. + "NFDI Core Ontology." + GitHub Repository. + Available at: + `https://github.com/ISE-FIZKarlsruhe/nfdicore `_ diff --git a/docs/source/benchmarking/scholarly_knowledge/oboe.rst b/docs/source/benchmarking/scholarly_knowledge/oboe.rst index e63e15e..37a2b7f 100644 --- a/docs/source/benchmarking/scholarly_knowledge/oboe.rst +++ b/docs/source/benchmarking/scholarly_knowledge/oboe.rst @@ -22,15 +22,14 @@ Extensible Observation Ontology (OBOE) ======================================================================================================== +The Extensible Observation Ontology (OBOE) is a formal ontology for capturing the semantics of scientific observations and measurements [#oboe-github]_ [#oboe-bioportal]_. It supports researchers in adding detailed semantic annotations to scientific data, thereby clarifying the meaning of observations, measured values, entities, characteristics, standards, and protocols [#oboe-bioportal]_. OBOE provides a generic conceptual framework for describing observational datasets, especially datasets consisting of observations and measurements [#oboe-github]_. -The Extensible Observation Ontology (OBOE) is a formal ontology for capturing the semantics of scientific observation and measurement. The ontology supports researchers to add detailed semantic annotations to scientific data, thereby clarifying the inherent meaning of scientific observations. It provides a structured vocabulary for representing scientific observations, measurements, and related data, supporting both theoretical and experimental research in scientific observation. +The ontology uses a class-based modeling approach, defining core concepts such as ``Observation``, ``Measurement``, ``Entity``, ``Characteristic``, ``Standard``, and ``Protocol`` [#oboe-bioportal]_. In OBOE, an observation is an event in which one or more measurements are taken; a measurement records the measured value of a characteristic of an entity; and standards provide units or controlled vocabularies for interpreting those values [#oboe-bioportal]_. OBOE can also represent contextual information such as space and time, as well as dependencies between observations, including nested experimental observations [#oboe-bioportal]_. -The ontology employs a class-based modeling approach, defining classes for different types of observations, measurements, and related data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. OBOE supports the integration of data from various sources, promoting interoperability and data-driven research in scientific observation. - -Typical applications of OBOE include the development of new observation and measurement methods, the optimization of scientific observation practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, OBOE enhances collaboration and innovation in the field of scientific observation. +Typical applications of OBOE include semantic annotation of observational datasets, measurement metadata modeling, dataset discovery, interoperability across scientific data repositories, and support for data interpretation and reuse [#oboe-github]_ [#oboe-bioportal]_. By providing a standardized and extensible framework for describing observations and measurements, OBOE helps make scientific data more interpretable, comparable, searchable, and reusable across research domains [#oboe-bioportal]_. **Example Usage**: -Annotate a scientific observation dataset with OBOE terms to specify observation types, measurements, and related data, enabling semantic search and integration with scientific observation platforms. +Annotate a scientific observation dataset with OBOE terms to specify the observed entity, measured characteristic, measurement value, unit or standard, protocol, and contextual information such as location and time. This enables semantic search, dataset integration, measurement interpretation, and reuse across scientific observation platforms [#oboe-github]_ [#oboe-bioportal]_. Metrics & Statistics -------------------------- @@ -139,3 +138,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#oboe-github] NCEAS. n.d. + "OBOE: The Extensible Observation Ontology." + GitHub Repository. + Available at: + `https://github.com/NCEAS/oboe `_ + +.. [#oboe-bioportal] NCBO BioPortal. 2019. + "The Extensible Observation Ontology." + Available at: + `https://bioportal.bioontology.org/ontologies/OBOE `_ diff --git a/docs/source/benchmarking/scholarly_knowledge/opmw.rst b/docs/source/benchmarking/scholarly_knowledge/opmw.rst index 2eb6f25..86e7449 100644 --- a/docs/source/benchmarking/scholarly_knowledge/opmw.rst +++ b/docs/source/benchmarking/scholarly_knowledge/opmw.rst @@ -24,10 +24,14 @@ Open Provenance Model for Workflows (OPMW) ======================================================================================================== +OPMW, the Open Provenance Model for Workflows, is an ontology for the semantic description of computational workflow templates and workflow execution traces [#opmw-ontology]_ [#opmw-publications]_. It is based on the Open Provenance Model and was designed as an OPM profile, extending and reusing OPM's core ontologies, including OPMV and OPMO, while later aligning with W3C PROV for provenance representation [#opmw-ontology]_. OPMW provides vocabulary for describing workflow templates, workflow steps, input and output artifacts, execution accounts, execution processes, agents, parameter values, software components, and provenance links that track data flow and transformations [#opmw-ontology]_. -OPMW is a specialized ontology for comprehensive semantic description of computational workflow traces, executions, and their templates based on the Open Provenance Model (OPM) framework. It provides vocabulary for describing workflow components including processes, inputs/outputs, agents, and execution steps, along with provenance information tracking data flow and transformations. OPMW is designed as an OPM profile, extending and reusing OPM's core ontologies OPMV (OPM-Vocabulary) and OPMO (OPM-Ontology) to provide workflow-specific semantics. The ontology enables systematic documentation and sharing of scientific workflows, supporting reproducibility and reuse in data-intensive research disciplines. OPMW facilitates workflow management systems and scientific computing platforms by providing standardized provenance representations. +The ontology supports systematic documentation and sharing of scientific workflows by representing both the prospective structure of a workflow and the retrospective provenance of its execution [#opmw-ontology]_ [#opmw-publications]_. This makes it useful for describing how computational results were produced, which input datasets and tools were used, what intermediate outputs were generated, and how workflow steps were connected [#opmw-ontology]_. OPMW therefore supports reproducibility, reuse, workflow comparison, provenance querying, and publication of workflow metadata as Linked Data [#opmw-publications]_. -**Example Usage**: Annotate a bioinformatics workflow with OPMW terms to describe workflow steps (sequence alignment, variant calling), inputs (raw sequencing data), outputs (VCF files), and provenance tracking which software tools were used, parameter settings, and intermediate data transformations. +Typical applications of OPMW include scientific workflow documentation, workflow repository metadata, provenance tracking, reproducibility support, data-intensive research, and integration between workflow management systems and semantic web platforms [#opmw-ontology]_ [#opmw-publications]_. By providing a standardized provenance model for workflows, OPMW helps researchers publish, discover, compare, and reuse computational workflows and their execution traces [#opmw-publications]_. + +**Example Usage**: +Annotate a bioinformatics workflow with OPMW terms to describe workflow steps such as sequence alignment and variant calling, inputs such as raw sequencing data, outputs such as VCF files, execution agents, software tools, parameter settings, intermediate artifacts, and provenance relationships. This enables workflow reproducibility, semantic search, provenance tracking, and reuse across scientific computing platforms [#opmw-ontology]_ [#opmw-publications]_. Metrics & Statistics -------------------------- @@ -136,3 +140,16 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#opmw-ontology] OPMW. 2014. + "The OPMW-PROV Ontology." + Available at: + `https://www.opmw.org/ontology/ `_ + +.. [#opmw-publications] OPMW. n.d. + "OPMW-PROV: The Open Provenance Model for Workflows - Publications." + Available at: + `https://www.opmw.org/publications.html `_ diff --git a/docs/source/benchmarking/scholarly_knowledge/pplan.rst b/docs/source/benchmarking/scholarly_knowledge/pplan.rst index fa49fe6..6e303ed 100644 --- a/docs/source/benchmarking/scholarly_knowledge/pplan.rst +++ b/docs/source/benchmarking/scholarly_knowledge/pplan.rst @@ -23,14 +23,14 @@ Ontology for Provenance and Plans (P-Plan) ======================================================================================================== -The Ontology for Provenance and Plans (P-Plan) is an extension of the PROV-O ontology [PROV-O] created to represent the plans that guided the execution of scientific processes. P-Plan describes how the plans are composed and their correspondence to provenance records that describe the execution itself. It provides a structured vocabulary for representing plans, provenance, and related data, supporting both theoretical and experimental research in provenance and planning. +The Ontology for Provenance and Plans (P-Plan) is an extension of the PROV-O ontology created to represent the plans that guide the execution of scientific processes [#pplan-ontology]_. P-Plan describes how plans are composed and how their elements correspond to provenance records that describe the execution itself [#pplan-ontology]_. It provides a structured vocabulary for representing plans, steps, variables, activities, entities, agents, and the relationships between abstract process descriptions and concrete executions [#pplan-ontology]_. -The ontology employs a class-based modeling approach, defining classes for different types of plans, provenance, and related data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. P-Plan supports the integration of data from various sources, promoting interoperability and data-driven research in provenance and planning. +The ontology uses a class-based modeling approach, defining concepts such as ``p-plan:Plan``, ``p-plan:Step``, and ``p-plan:Variable``, together with links to execution-level provenance entities and activities [#pplan-ontology]_. This allows researchers to distinguish between the intended structure of a process and the actual provenance trace generated when that process is executed [#pplan-ontology]_. P-Plan supports interoperability between workflow descriptions, scientific process models, and provenance records by connecting planned process structures with their corresponding execution data [#pplan-ontology]_. -Typical applications of P-Plan include the development of new provenance and planning methods, the optimization of scientific process execution, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, P-Plan enhances collaboration and innovation in the field of provenance and planning. +Typical applications of P-Plan include scientific workflow documentation, provenance tracking, process reproducibility, workflow comparison, experiment reporting, and integration of provenance data across computational research systems [#pplan-ontology]_. By connecting plans with execution provenance, P-Plan helps researchers understand how results were produced, which steps were followed, and how abstract methods correspond to concrete process executions [#pplan-ontology]_. **Example Usage**: -Annotate a scientific process with P-Plan terms to specify plans, provenance, and related data, enabling semantic search and integration with provenance and planning platforms. +Annotate a scientific process with P-Plan terms to describe the overall plan, individual steps, input and output variables, responsible agents, and the corresponding execution activities and entities generated during execution. This enables semantic search, provenance tracking, reproducibility analysis, and integration with workflow and provenance management platforms [#pplan-ontology]_. Metrics & Statistics -------------------------- @@ -139,3 +139,11 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#pplan-ontology] OPMW. 2014. + "The P-Plan Ontology." + Available at: + `https://www.opmw.org/model/p-plan/ `_ diff --git a/docs/source/benchmarking/scholarly_knowledge/premon.rst b/docs/source/benchmarking/scholarly_knowledge/premon.rst index c75fe65..7e2aaa7 100644 --- a/docs/source/benchmarking/scholarly_knowledge/premon.rst +++ b/docs/source/benchmarking/scholarly_knowledge/premon.rst @@ -23,14 +23,14 @@ Pre-Modern Ontology (PreMOn) ======================================================================================================== -The Pre-Modern Ontology (PreMOn) is an extension of lemon (W3C Ontology Lexicon Community Group, 2015) for representing predicate models and their mappings. The Core Module of the PreMOn Ontology defines the main abstractions for modelling semantic classes with their semantic roles, mappings between different predicate models, and annotations. It provides a structured vocabulary for representing predicate models, supporting both theoretical and experimental research in linguistics. +The Pre-Modern Ontology (PreMOn) is an extension of lemon (W3C Ontology Lexicon Community Group, 2015) for representing predicate models and their mappings [#premon-paper]_. The Core Module of the PreMOn Ontology defines the main abstractions for modelling semantic classes with their semantic roles, mappings between different predicate models, and annotations [#premon-paper]_. It provides a structured vocabulary for representing predicate models, supporting both theoretical and experimental research in linguistics [#premon-paper]_. -The ontology employs a class-based modeling approach, defining classes for different types of predicate models, semantic roles, and annotations, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. PreMOn supports the integration of data from various sources, promoting interoperability and data-driven research in linguistics. +The ontology employs a class-based modeling approach, defining classes for different types of predicate models, semantic roles, and annotations, along with properties to describe their characteristics and interactions [#premon-paper]_. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis [#premon-paper]_. PreMOn supports the integration of data from various sources, promoting interoperability and data-driven research in linguistics [#premon-paper]_. -Typical applications of PreMOn include the development of new predicate model representation methods, the optimization of linguistic data management practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, PreMOn enhances collaboration and innovation in the field of linguistics. +Typical applications of PreMOn include the development of new predicate model representation methods, the optimization of linguistic data management practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery [#premon-paper]_. By providing a standardized vocabulary and framework, PreMOn enhances collaboration and innovation in the field of linguistics [#premon-paper]_. **Example Usage**: -Annotate a linguistic dataset with PreMOn terms to specify predicate models, semantic roles, and annotations, enabling semantic search and integration with linguistic information management platforms. +Annotate a linguistic dataset with PreMOn terms to specify predicate models, semantic roles, and annotations, enabling semantic search and integration with linguistic information management platforms [#premon-paper]_. Metrics & Statistics -------------------------- @@ -139,3 +139,13 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#premon-paper] Corcoglioniti, Francesco, Marco Rospocher, + Alessio Palmero Aprosio, and Sara Tonelli. 2016. + "PreMOn: A Lemon Extension for Exposing Predicate Models as Linked Data." + *Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)*. + Available at: + `https://aclanthology.org/L16-1141/ `_ diff --git a/docs/source/benchmarking/scholarly_knowledge/sepio.rst b/docs/source/benchmarking/scholarly_knowledge/sepio.rst index ead28d9..53c0c92 100644 --- a/docs/source/benchmarking/scholarly_knowledge/sepio.rst +++ b/docs/source/benchmarking/scholarly_knowledge/sepio.rst @@ -22,15 +22,14 @@ Scientific Evidence and Provenance Information Ontology (SEPIO) ======================================================================================================== +The Scientific Evidence and Provenance Information Ontology (SEPIO) is an ontology for representing scientific evidence and provenance information, especially in relation to scientific claims [#sepio-github]_ [#sepio-bioportal]_. SEPIO provides a structured vocabulary for describing claims, evidence lines, information items, methods, tools, agents, and the provenance relationships involved in the creation and evaluation of scientific assertions [#sepio-bioportal]_. -The Scientific Evidence and Provenance Information Ontology (SEPIO) is in its early stages of development, undergoing iterative refinement as new requirements emerge and alignment with existing standards is explored. SEPIO provides a structured vocabulary for representing scientific evidence, provenance, and related data, supporting both theoretical and experimental research in scientific evidence and provenance. +The ontology employs a class-based modeling approach, defining classes for different types of scientific evidence, provenance, claims, evidence lines, and related information objects, along with properties to describe their relationships and interactions [#sepio-github]_ [#sepio-bioportal]_. Hierarchies and relations are used to organize evidence and provenance information, enabling structured retrieval, analysis, and comparison of scientific claims [#sepio-bioportal]_. SEPIO supports the integration of evidence and provenance metadata from various sources, promoting interoperability and data-driven research in scientific evidence representation [#sepio-github]_. -The ontology employs a class-based modeling approach, defining classes for different types of scientific evidence, provenance, and related data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. SEPIO supports the integration of data from various sources, promoting interoperability and data-driven research in scientific evidence and provenance. - -Typical applications of SEPIO include the development of new scientific evidence and provenance representation methods, the optimization of scientific evidence management practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, SEPIO enhances collaboration and innovation in the field of scientific evidence and provenance. +Typical applications of SEPIO include scientific claim annotation, evidence modeling, provenance tracking, data integration, curation, knowledge discovery, and manual or computational evaluation of scientific claims [#sepio-bioportal]_. By providing a standardized vocabulary and framework, SEPIO enhances interoperability and supports richer analysis of scientific evidence and provenance across research data platforms [#sepio-github]_ [#sepio-bioportal]_. **Example Usage**: -Annotate a scientific study with SEPIO terms to specify scientific evidence, provenance, and related data, enabling semantic search and integration with scientific evidence and provenance platforms. +Annotate a scientific study with SEPIO terms to specify a scientific claim, the evidence lines supporting or evaluating it, the information items used as evidence, and the methods, tools, and agents involved in producing that evidence. This enables semantic search, provenance tracking, claim evaluation, and integration with scientific evidence platforms [#sepio-github]_ [#sepio-bioportal]_. Metrics & Statistics -------------------------- @@ -139,3 +138,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#sepio-github] Monarch Initiative. n.d. + "Scientific Evidence and Provenance Information Ontology." + GitHub Repository. + Available at: + `https://github.com/monarch-initiative/SEPIO-ontology `_ + +.. [#sepio-bioportal] NCBO BioPortal. 2023. + "Scientific Evidence and Provenance Information Ontology." + Available at: + `https://bioportal.bioontology.org/ontologies/SEPIO `_ diff --git a/docs/source/benchmarking/scholarly_knowledge/spdocument.rst b/docs/source/benchmarking/scholarly_knowledge/spdocument.rst index ab9399a..7193fa5 100644 --- a/docs/source/benchmarking/scholarly_knowledge/spdocument.rst +++ b/docs/source/benchmarking/scholarly_knowledge/spdocument.rst @@ -22,15 +22,14 @@ SMART Protocols Ontology: Document Module (SP-Document) ======================================================================================================== +SMART Protocols Ontology: Document Module is an ontology designed to represent metadata used to report an experimental protocol [#sp-document]_ [#sp-github]_. It provides a structured vocabulary for representing experimental protocol documents, protocol metadata, and related descriptive information, supporting semantic documentation of experimental protocols [#sp-document]_. -SMART Protocols Ontology: Document Module is an ontology designed to represent metadata used to report an experimental protocol. It provides a structured vocabulary for representing experimental protocols, metadata, and related data, supporting both theoretical and experimental research in experimental protocol documentation. +The ontology employs a class-based modeling approach, defining classes for protocol-related metadata such as protocol title, identifier, authorship, application, provenance, reagents, instruments, samples, objectives, and other information needed to report an experimental protocol [#sp-document]_ [#sp-github]_. Hierarchies and properties are used to organize protocol-document information into structured categories, enabling retrieval, comparison, and analysis of experimental protocol metadata [#sp-document]_. SP-Document supports the integration of protocol descriptions from different sources, promoting interoperability and data-driven research in experimental protocol documentation [#sp-github]_. -The ontology employs a class-based modeling approach, defining classes for different types of experimental protocols, metadata, and related data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. SP-Document supports the integration of data from various sources, promoting interoperability and data-driven research in experimental protocol documentation. - -Typical applications of SP-Document include the development of new experimental protocol documentation methods, the optimization of experimental protocol management practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, SP-Document enhances collaboration and innovation in the field of experimental protocol documentation. +Typical applications of SP-Document include experimental protocol reporting, protocol metadata standardization, semantic annotation of protocol documents, protocol repository development, protocol discovery, and integration of protocol descriptions with scientific workflow and laboratory information systems [#sp-document]_ [#sp-github]_. By providing a standardized vocabulary and framework, SP-Document enhances interoperability, reuse, and semantic search in the field of experimental protocol documentation [#sp-document]_. **Example Usage**: -Annotate an experimental protocol with SP-Document terms to specify protocol types, metadata, and related data, enabling semantic search and integration with experimental protocol documentation platforms. +Annotate an experimental protocol with SP-Document terms to specify the protocol title, identifier, authors, application, provenance, reagents, instruments, samples, objectives, and related metadata. This enables semantic search, protocol comparison, metadata integration, and use within experimental protocol documentation platforms [#sp-document]_ [#sp-github]_. Metrics & Statistics -------------------------- @@ -139,3 +138,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#sp-document] SMART Protocols. 2013. + "SMART Protocols Ontology: Document Module." + Available at: + `https://vocab.linkeddata.es/SMARTProtocols/myDocumentation_SPdoc_18Abril2017/index_SPdoc_V4.0.html `_ + +.. [#sp-github] Ontology Engineering Group, Universidad Politécnica de Madrid. n.d. + "SMART-Protocols." + GitHub Repository. + Available at: + `https://github.com/oeg-upm/SMART-Protocols `_ diff --git a/docs/source/benchmarking/scholarly_knowledge/spworkflow.rst b/docs/source/benchmarking/scholarly_knowledge/spworkflow.rst index 03eb805..1c134d7 100644 --- a/docs/source/benchmarking/scholarly_knowledge/spworkflow.rst +++ b/docs/source/benchmarking/scholarly_knowledge/spworkflow.rst @@ -23,14 +23,14 @@ SMART Protocols Ontology: Workflow Module (SP-Workflow) ======================================================================================================== -SP-Workflow module represents: i) the executable elements of a protocol; ii) the experimental actions and material entities that participate in instructions (sample/specimen, organisms, reagents, instruments); and iii) the order of execution of the instructions. It provides a structured vocabulary for representing workflows, experimental actions, and related data, supporting both theoretical and experimental research in workflow management. +SP-Workflow module represents: i) the executable elements of a protocol; ii) the experimental actions and material entities that participate in instructions, such as samples/specimens, organisms, reagents, and instruments; and iii) the order of execution of the instructions [#sp-workflow]_ [#sp-github]_. It provides a structured vocabulary for representing workflows, experimental actions, protocol instructions, material entities, and related data, supporting semantic representation of experimental protocol execution [#sp-workflow]_. -The ontology employs a class-based modeling approach, defining classes for different types of workflows, experimental actions, and related data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. SP-Workflow supports the integration of data from various sources, promoting interoperability and data-driven research in workflow management. +The ontology employs a class-based modeling approach, defining classes for different types of protocol instructions, experimental actions, workflow elements, samples/specimens, organisms, reagents, instruments, and related data, along with properties to describe their characteristics and interactions [#sp-workflow]_. Hierarchies and relations are used to organize workflow concepts into structured categories, enabling retrieval, comparison, and analysis of experimental protocol workflows [#sp-workflow]_. SP-Workflow supports the integration of protocol workflow information from various sources, promoting interoperability and data-driven research in experimental workflow management [#sp-github]_. -Typical applications of SP-Workflow include the development of new workflow management methods, the optimization of experimental workflows, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, SP-Workflow enhances collaboration and innovation in the field of workflow management. +Typical applications of SP-Workflow include the semantic representation of experimental protocol execution, documentation of protocol instructions, optimization of experimental workflows, workflow comparison, protocol retrieval, and integration of workflow descriptions with laboratory information systems and workflow management platforms [#sp-workflow]_ [#sp-github]_. By providing a standardized vocabulary and framework, SP-Workflow enhances interoperability, reuse, and semantic search in the field of experimental workflow documentation [#sp-workflow]_. **Example Usage**: -Annotate an experimental workflow with SP-Workflow terms to specify workflow steps, experimental actions, and related data, enabling semantic search and integration with workflow management platforms. +Annotate an experimental workflow with SP-Workflow terms to specify protocol instructions, workflow steps, experimental actions, samples/specimens, organisms, reagents, instruments, and the order in which instructions should be executed. This enables semantic search, workflow comparison, protocol reuse, and integration with workflow management platforms [#sp-workflow]_ [#sp-github]_. Metrics & Statistics -------------------------- @@ -139,3 +139,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#sp-workflow] SMART Protocols. 2013. + "SMART Protocols Ontology: Workflow Module." + Available at: + `https://vocab.linkeddata.es/SMARTProtocols/myDocumentation_SPwf_19Abril2017/index_SPwf_V4.0.html `_ + +.. [#sp-github] Ontology Engineering Group, Universidad Politécnica de Madrid. n.d. + "SMART-Protocols." + GitHub Repository. + Available at: + `https://github.com/oeg-upm/SMART-Protocols `_ diff --git a/docs/source/benchmarking/scholarly_knowledge/swo.rst b/docs/source/benchmarking/scholarly_knowledge/swo.rst index af35f5d..75e4a81 100644 --- a/docs/source/benchmarking/scholarly_knowledge/swo.rst +++ b/docs/source/benchmarking/scholarly_knowledge/swo.rst @@ -23,14 +23,14 @@ Software Ontology (SWO) ======================================================================================================== -The Software Ontology (SWO) is a resource for describing software tools, their types, tasks, versions, provenance, and associated data. It contains detailed information on licensing and formats as well as software applications themselves, mainly (but not limited) to the bioinformatics community. It provides a structured vocabulary for representing software tools, supporting both theoretical and experimental research in software management. +The Software Ontology (SWO) is a resource for describing software tools, their types, tasks, versions, provenance, and associated data [#swo-obofoundry]_ [#swo-paper]_. It contains detailed information on licensing and formats as well as software applications themselves, mainly, but not limited to, the bioinformatics community [#swo-obofoundry]_. It provides a structured vocabulary for representing software tools, supporting research in software description, reproducibility, data analysis, curation, and digital preservation [#swo-paper]_. -The ontology employs a class-based modeling approach, defining classes for different types of software tools, tasks, and associated data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. SWO supports the integration of data from various sources, promoting interoperability and data-driven research in software management. +The ontology employs a class-based modeling approach, defining classes for different types of software tools, tasks, versions, licenses, formats, and associated data, along with properties to describe their characteristics and interactions [#swo-obofoundry]_ [#swo-paper]_. Hierarchies are used to organize software-related concepts into categories, enabling efficient retrieval, comparison, and analysis [#swo-obofoundry]_. SWO supports the integration of software metadata from various sources, promoting interoperability and data-driven research in software management and biomedical data analysis [#swo-paper]_. -Typical applications of SWO include the development of new software management methods, the optimization of software tool usage, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, SWO enhances collaboration and innovation in the field of software management. +Typical applications of SWO include software tool annotation, software cataloging, reproducibility support, biomedical data analysis documentation, digital preservation, and integration of software metadata across repositories and research platforms [#swo-paper]_. By providing a standardized vocabulary and framework, SWO enhances interoperability, reuse, and knowledge discovery in the field of software management [#swo-obofoundry]_ [#swo-paper]_. **Example Usage**: -Annotate a software tool with SWO terms to specify tool types, tasks, and associated data, enabling semantic search and integration with software management platforms. +Annotate a software tool with SWO terms to specify its tool type, task, version, license, input and output data formats, provenance, and associated data. This enables semantic search, reproducibility tracking, and integration with software management platforms [#swo-obofoundry]_ [#swo-paper]_. Metrics & Statistics -------------------------- @@ -139,3 +139,18 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + + +References +---------- + +.. [#swo-obofoundry] OBO Foundry. n.d. + "Software ontology." + Available at: + `https://obofoundry.org/ontology/swo.html `_ + +.. [#swo-paper] Malone, James, et al. 2014. + "The Software Ontology (SWO): a resource for reproducibility in biomedical data analysis, curation and digital preservation." + *Journal of Biomedical Semantics* 5: 25. + DOI: + `10.1186/2041-1480-5-25 `_ diff --git a/docs/source/benchmarking/scholarly_knowledge/tribain.rst b/docs/source/benchmarking/scholarly_knowledge/tribain.rst index f59d0a7..f1fd127 100644 --- a/docs/source/benchmarking/scholarly_knowledge/tribain.rst +++ b/docs/source/benchmarking/scholarly_knowledge/tribain.rst @@ -22,15 +22,14 @@ Tribology and Artificial Intelligence Ontology (TribAIn) ======================================================================================================== +TribAIn is an ontology for the description of tribological experiments and their results [#tribain-github]_ [#tribain-paper]_. It is designed to be used in the context of the TribAIn project, which aims to develop a knowledge-based system for the design and analysis of tribological systems [#tribain-paper]_. It provides a structured vocabulary for representing tribological experiments, experimental setups, methodological background knowledge, measurements, analyses, interpretations, and related data, supporting research in tribology [#tribain-github]_ [#tribain-paper]_. -TribAIn is an ontology for the description of tribological experiments and their results. It is designed to be used in the context of the TribAIn project, which aims to develop a knowledge-based system for the design of tribological systems. It provides a structured vocabulary for representing tribological experiments, results, and related data, supporting both theoretical and experimental research in tribology. +The ontology employs a class-based modeling approach, defining classes for different types of tribological experiments, experimental setups, results, measurements, analyses, and related data, along with properties to describe their characteristics and interactions [#tribain-paper]_. Hierarchies are used to organize classes into categories, enabling efficient data retrieval, comparison, and analysis [#tribain-github]_. TribAIn supports the integration of data from various sources, including natural-language texts and tabular data, promoting interoperability and data-driven research in tribology [#tribain-paper]_. -The ontology employs a class-based modeling approach, defining classes for different types of tribological experiments, results, and related data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. TribAIn supports the integration of data from various sources, promoting interoperability and data-driven research in tribology. - -Typical applications of TribAIn include the development of new tribological experiment methods, the optimization of tribological system design, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, TribAIn enhances collaboration and innovation in the field of tribology. +Typical applications of TribAIn include the documentation of tribological experiments, semantic annotation of experimental setups and results, reuse and comparison of tribological knowledge, optimization of tribological system design, and integration of diverse datasets to support advanced analytics and knowledge discovery [#tribain-github]_ [#tribain-paper]_. By providing a standardized vocabulary and framework, TribAIn enhances collaboration, interoperability, and knowledge reuse in the field of tribology [#tribain-paper]_. **Example Usage**: -Annotate a tribological experiment with TribAIn terms to specify experiment types, results, and related data, enabling semantic search and integration with tribology research platforms. +Annotate a tribological experiment with TribAIn terms to specify the experimental setup, contact bodies, lubricant, testing conditions, measurements, analysis results, and interpretations. This enables semantic search, comparison of experimental results, and integration with tribology research platforms [#tribain-github]_ [#tribain-paper]_. Metrics & Statistics -------------------------- @@ -139,3 +138,18 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#tribain-github] Kügler, Patricia. n.d. + "tribAIn: Ontology for scientific experiments in the domain of tribology." + GitHub Repository. + Available at: + `https://github.com/snow0815/tribAIn `_ + +.. [#tribain-paper] Kügler, Patricia, and Marian Wartzack. 2020. + "tribAIn—Towards an Explicit Specification of Shared Tribological Understanding." + *Applied Sciences* 10(13): 4421. + DOI: + `10.3390/app10134421 `_ diff --git a/docs/source/benchmarking/scholarly_knowledge/voaf.rst b/docs/source/benchmarking/scholarly_knowledge/voaf.rst index 106bdd4..b8e6617 100644 --- a/docs/source/benchmarking/scholarly_knowledge/voaf.rst +++ b/docs/source/benchmarking/scholarly_knowledge/voaf.rst @@ -22,15 +22,14 @@ Vocabulary of a Friend (VOAF) ======================================================================================================== +The Vocabulary of a Friend (VOAF) is a vocabulary specification providing elements allowing the description of vocabularies, such as RDFS vocabularies or OWL ontologies [#voaf-spec]_. It is based on Dublin Core and VoID, and provides a structured vocabulary for representing vocabulary metadata, supporting vocabulary description, discovery, reuse, and management [#voaf-spec]_. -The Vocabulary of a Friend (VOAF) is a vocabulary specification providing elements allowing the description of vocabularies (RDFS vocabularies or OWL ontologies). It is based on Dublin Core and VOID. VOAF provides a structured vocabulary for representing vocabularies, supporting both theoretical and experimental research in vocabulary management. +The ontology employs a class-based modeling approach, defining classes for vocabularies and related vocabulary elements, along with properties to describe their characteristics and relationships [#voaf-spec]_. VOAF provides properties for expressing how vocabularies relate to one another, including whether they rely on, extend, specialize, annotate, or otherwise link to other vocabularies [#voaf-spec]_. These relationships enable structured retrieval, dependency tracking, and analysis of vocabulary networks [#voaf-spec]_. -The ontology employs a class-based modeling approach, defining classes for different types of vocabularies, elements, and relationships, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. VOAF supports the integration of data from various sources, promoting interoperability and data-driven research in vocabulary management. - -Typical applications of VOAF include the development of new vocabulary management methods, the optimization of vocabulary usage, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, VOAF enhances collaboration and innovation in the field of vocabulary management. +Typical applications of VOAF include vocabulary documentation, vocabulary cataloging, ontology metadata management, linked data vocabulary discovery, vocabulary dependency analysis, and integration of vocabulary descriptions across semantic web platforms [#voaf-spec]_. By providing a standardized vocabulary and framework, VOAF enhances interoperability, reuse, and knowledge sharing in the field of vocabulary management [#voaf-spec]_. **Example Usage**: -Annotate a vocabulary with VOAF terms to specify vocabulary types, elements, and relationships, enabling semantic search and integration with vocabulary management platforms. +Annotate a vocabulary with VOAF terms to specify its type, metadata, reused vocabularies, extensions, specializations, and relationships to other vocabularies. This enables semantic search, vocabulary discovery, dependency analysis, and integration with vocabulary management platforms [#voaf-spec]_. Metrics & Statistics -------------------------- @@ -139,3 +138,11 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#voaf-spec] VOAF. n.d. + "Vocabulary of a Friend." + Available at: + `http://purl.org/vocommons/voaf `_ diff --git a/docs/source/benchmarking/scholarly_knowledge/wild.rst b/docs/source/benchmarking/scholarly_knowledge/wild.rst index 24f0b3b..b2ba343 100644 --- a/docs/source/benchmarking/scholarly_knowledge/wild.rst +++ b/docs/source/benchmarking/scholarly_knowledge/wild.rst @@ -24,10 +24,9 @@ Workflows in Linked Data (WiLD) ======================================================================================================== +WiLD is an ontology for describing, specifying, monitoring, and executing workflows in Linked Data environments [#wild-home]_ [#wild-paper]_. It provides a structured vocabulary for representing workflow models, workflow instances, activities, execution order, data flow, and dependencies between workflow components [#wild-paper]_. WiLD focuses on workflows whose components interact through read-write Linked Data interfaces, making workflow descriptions machine-readable and suitable for semantic web applications [#wild-paper]_. The ontology supports the formal representation of workflow structure and execution semantics, helping systems document, exchange, monitor, and reuse workflow descriptions in a consistent way [#wild-home]_ [#wild-paper]_. By providing a linked data-based workflow model, WiLD can support reproducibility, automation, workflow sharing, and semantic integration of computational or data-processing processes [#wild-paper]_. -WiLD is a specialized ontology for formal semantic description and representation of computational workflows and scientific data processing pipelines using linked data and semantic web technologies. It provides standardized vocabulary for describing workflow components including workflow steps, data flows, tool/service invocations, and their interdependencies in a machine-readable format. WiLD enables interoperable workflow specification and sharing across diverse scientific computing platforms and workflow management systems by providing uniform semantic representations. The ontology captures essential workflow metadata including execution parameters, input/output specifications, and constraints, supporting workflow automation and optimization. WiLD facilitates reproducible science by enabling systematic documentation of computational methods and data processing procedures in linked data formats. - -**Example Usage**: Represent a bioinformatics data processing pipeline using WiLD terms to describe sequential workflow steps (quality control, alignment, variant calling), input datasets, tool invocations (with parameters), and output data products to enable workflow reproducibility and reuse. +**Example Usage**: Represent a bioinformatics data-processing pipeline using WiLD terms to describe sequential workflow steps such as quality control, alignment, and variant calling, together with input datasets, execution order, tool or service invocations, intermediate data, and output data products. This enables workflow documentation, semantic search, monitoring, reproducibility, and reuse across Linked Data-based workflow systems [#wild-home]_ [#wild-paper]_. Metrics & Statistics -------------------------- @@ -136,3 +135,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#wild-home] WiLD. n.d. + "WiLD - Workflows in Linked Data." + Available at: + `https://purl.org/wild `_ + +.. [#wild-paper] Käfer, Tobias, and Andreas Harth. 2018. + "Specifying, Monitoring, and Executing Workflows in Linked Data Environments." + *The Semantic Web -- ISWC 2018*. + Available at: + `https://arxiv.org/pdf/1804.05044 `_ diff --git a/docs/source/benchmarking/social_sciences/as2.rst b/docs/source/benchmarking/social_sciences/as2.rst index 78d0c39..07c646b 100644 --- a/docs/source/benchmarking/social_sciences/as2.rst +++ b/docs/source/benchmarking/social_sciences/as2.rst @@ -23,10 +23,10 @@ Activity Streams 2.0 Ontology (AS2) ======================================================================================================== -The Activity Streams 2.0 Ontology (AS2) is a W3C standard vocabulary for describing social activities, actions, and interactions on the web. It provides a set of classes and properties for modeling activities such as posting, liking, sharing, following, and commenting, as well as the actors, objects, and targets involved. AS2 enables interoperability between social networking platforms, federated social web applications, and activity tracking systems by providing a common semantic framework. The ontology supports extensibility, allowing developers to define custom activity types and properties for domain-specific use cases. AS2 is widely used in decentralized social networks, content syndication, and social analytics, enabling rich representation and exchange of activity data. By standardizing the description of social actions, AS2 facilitates data integration, aggregation, and analysis across diverse social platforms. +The Activity Streams 2.0 Ontology (AS2) is a W3C standard vocabulary and syntax for describing social activities, actions, interactions, and content on the Web [#as2-core]_ [#as2-github]_. It provides a set of classes and properties for modeling activities such as posting, liking, sharing, following, creating, updating, and deleting, as well as the actors, objects, targets, collections, and links involved in those activities [#as2-core]_. AS2 enables interoperability between social networking platforms, federated social web applications, content syndication systems, and activity tracking systems by providing a common semantic framework for representing activity data [#as2-core]_ [#as2-github]_. The vocabulary supports extensibility, allowing developers to use additional vocabularies or define specialized activity types and properties for domain-specific use cases [#as2-core]_. AS2 is used in decentralized social web systems and social data exchange, enabling rich representation, publication, aggregation, and analysis of activity streams across platforms [#as2-core]_ [#as2-github]_. By standardizing the description of social actions and their related entities, AS2 facilitates data integration, interoperability, and machine-readable exchange of social activity information [#as2-core]_. **Example Usage**: -Annotate a social networking application with AS2 terms to describe user activities such as posting a status update, liking a photo, or following another user, enabling interoperability with other platforms and activity streams consumers. +Annotate a social networking application with AS2 terms to describe user activities such as posting a status update, liking a photo, following another user, or sharing content. This enables interoperability with other platforms, federated social web applications, and activity stream consumers [#as2-core]_ [#as2-github]_. Metrics & Statistics -------------------------- @@ -135,3 +135,18 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#as2-core] W3C. 2017. + "Activity Streams 2.0." + W3C Recommendation. + Available at: + `https://www.w3.org/TR/activitystreams-core/ `_ + +.. [#as2-github] W3C. n.d. + "Activity Streams 2.0." + GitHub Repository. + Available at: + `https://github.com/w3c/activitystreams `_ diff --git a/docs/source/benchmarking/social_sciences/bio.rst b/docs/source/benchmarking/social_sciences/bio.rst index 539b07c..2d4c8c4 100644 --- a/docs/source/benchmarking/social_sciences/bio.rst +++ b/docs/source/benchmarking/social_sciences/bio.rst @@ -23,10 +23,10 @@ BIO: A vocabulary for biographical information (BIO) ======================================================================================================== -The BIO vocabulary is a domain ontology for describing biographical information about people, their backgrounds, and genealogical relationships. It models a person's life as a series of interconnected key events (birth, marriage, employment, death, etc.), providing a framework for weaving together biographical and genealogical data. BIO defines an event-centric approach, supplying a set of core event types and properties that are person-centric, making it suitable for representing life histories, family trees, and career trajectories. The ontology is designed for extensibility, allowing other vocabularies to build upon its event framework for specialized use cases. BIO is widely used in digital humanities, genealogy platforms, and social history research to enable semantic annotation, data integration, and advanced queries on biographical datasets. By providing a standardized vocabulary, BIO facilitates interoperability and knowledge sharing across biographical and genealogical information systems. +The BIO vocabulary is a domain vocabulary for describing biographical information about people, their backgrounds, and genealogical relationships [#bio-vocab]_ [#bio-github]_. It models a person's life as a series of interconnected key events, such as birth, marriage, employment, and death, around which additional biographical and genealogical information can be organized [#bio-vocab]_. BIO defines an event-centric approach, supplying a set of core event types and properties that are person-centric, making it suitable for representing life histories, family trees, career trajectories, and historical biographies [#bio-vocab]_ [#bio-github]_. The vocabulary is designed for extensibility, allowing other vocabularies to build upon its event framework for specialized use cases [#bio-vocab]_. BIO can be used in digital humanities, genealogy platforms, library metadata, and social history research to support semantic annotation, data integration, and structured queries over biographical datasets [#bio-vocab]_. By providing a standardized vocabulary for biographical events, BIO facilitates interoperability and knowledge sharing across biographical and genealogical information systems [#bio-github]_. **Example Usage**: -Annotate a genealogy database with BIO terms to describe a person's birth, marriage, and employment events, linking them to dates, places, and related individuals for semantic search and family history visualization. +Annotate a genealogy database with BIO terms to describe a person's birth, marriage, employment, and death events, linking them to dates, places, and related individuals for semantic search, family history visualization, and biographical data integration [#bio-vocab]_ [#bio-github]_. Metrics & Statistics -------------------------- @@ -135,3 +135,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#bio-vocab] Davis, Ian, and David Galbraith. n.d. + "BIO: A vocabulary for biographical information." + Available at: + `https://vocab.org/bio/ `_ + +.. [#bio-github] Davis, Ian. n.d. + "BIO Vocabulary." + GitHub Repository. + Available at: + `https://github.com/iand/vocab-bio `_ diff --git a/docs/source/benchmarking/social_sciences/contact.rst b/docs/source/benchmarking/social_sciences/contact.rst index c8ae362..cbc7f02 100644 --- a/docs/source/benchmarking/social_sciences/contact.rst +++ b/docs/source/benchmarking/social_sciences/contact.rst @@ -25,9 +25,9 @@ Contact Ontology (Contact) ======================================================================================================== -The Contact Ontology is a specialized vocabulary for representing and managing contact information and related communication metadata such as addresses, phone numbers, email addresses, and physical locations. It reuses and extends the iContact Ontology developed by the Enterprise Integration Lab, incorporating specialized definitions while maintaining backward compatibility. The ontology extends the iContact model by introducing Hours of Operation as a specialized concept defined as both a subclass of iContact hours of operation and the Recurring Event class from the iCity Recurring Event ontology. Contact Ontology enriches address representation by associating address data with spatial location information, enabling geocoding and location-aware applications. The ontology supports business and organizational applications including customer relationship management (CRM), location-based services, and facility management systems. +The Contact Ontology is a specialized vocabulary for representing contact information, including addresses, phone numbers, email-related contact details, hours of operation, and associated location information [#contact-ontology]_. It reuses and extends contact-related modeling patterns to support city, business, organizational, and location-aware applications [#contact-ontology]_. The ontology introduces specialized concepts for representing operational availability, such as hours of operation, and connects contact information with spatial location data to support geographic lookup, mapping, and service discovery [#contact-ontology]_. By providing structured terms for addresses, contact channels, operating hours, and location associations, the Contact Ontology enables interoperable representation of business and organizational contact data [#contact-ontology]_. It supports applications such as customer relationship management, location-based services, facility management, city information systems, and service discovery platforms [#contact-ontology]_. -**Example Usage**: Represent a business location with Contact terms including street address, postal code, phone number, email, hours of operation (business hours), and associated geographic coordinates for location mapping and service discovery. +**Example Usage**: Represent a business location with Contact Ontology terms for street address, postal code, phone number, email contact, hours of operation, and associated geographic coordinates. This enables location mapping, contact discovery, service lookup, and integration with city or facility information systems [#contact-ontology]_. Metrics & Statistics -------------------------- @@ -136,3 +136,11 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#contact-ontology] Enterprise Integration Laboratory. 2018. + "Contact Ontology." + Available at: + `https://enterpriseintegrationlab.github.io/icity/Contact/Contact_1.0/doc/index-en.html `_ diff --git a/docs/source/benchmarking/social_sciences/foaf.rst b/docs/source/benchmarking/social_sciences/foaf.rst index b9e9c70..d5bda06 100644 --- a/docs/source/benchmarking/social_sciences/foaf.rst +++ b/docs/source/benchmarking/social_sciences/foaf.rst @@ -23,10 +23,10 @@ Friend of a Friend (FOAF) ======================================================================================================== -FOAF (Friend of a Friend) is a widely adopted ontology for describing people, their relationships, and the information that links them on the web. It provides a standardized vocabulary for representing personal profiles, social connections, organizations, projects, and online accounts. FOAF enables the creation of machine-readable social networks, supporting interoperability between social platforms, personal websites, and linked data applications. The ontology is designed for extensibility, allowing integration with other vocabularies and domain-specific ontologies. FOAF is used in digital identity management, social media analytics, and knowledge graph construction to enable semantic search, data integration, and discovery of social connections. By providing a common framework, FOAF facilitates the linking of people and information across the decentralized web. +FOAF (Friend of a Friend) is a widely adopted RDF vocabulary for describing people, their relationships, and the information that links them on the Web [#foaf-spec]_ [#foaf-bioportal]_. It provides a standardized vocabulary for representing personal profiles, social connections, organizations, projects, documents, online accounts, and other related entities [#foaf-spec]_. FOAF enables the creation of machine-readable social networks, supporting interoperability between personal websites, social platforms, linked data applications, and knowledge graphs [#foaf-spec]_. The vocabulary is designed for extensibility and can be used together with other RDF vocabularies and domain-specific ontologies to describe richer social and identity-related data [#foaf-spec]_. FOAF is used in digital identity management, social data publishing, semantic search, and knowledge graph construction to support data integration and discovery of social connections [#foaf-spec]_ [#foaf-bioportal]_. By providing a common framework for describing people, agents, and their relationships, FOAF facilitates linking people and information across the decentralized Web [#foaf-spec]_. **Example Usage**: -Annotate a personal website or social network profile with FOAF terms to describe a person's name, email, homepage, friends, and memberships in organizations, enabling semantic search and cross-platform social data integration. +Annotate a personal website or social network profile with FOAF terms to describe a person's name, email, homepage, friends, online accounts, and memberships in organizations, enabling semantic search and cross-platform social data integration [#foaf-spec]_ [#foaf-bioportal]_. Metrics & Statistics -------------------------- @@ -135,3 +135,16 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#foaf-spec] Brickley, Dan, and Libby Miller. 2014. + "FOAF Vocabulary Specification 0.99." + Available at: + `https://xmlns.com/foaf/spec/ `_ + +.. [#foaf-bioportal] NCBO BioPortal. 2019. + "Friend of a Friend Vocabulary." + Available at: + `https://bioportal.bioontology.org/ontologies/FOAF `_ diff --git a/docs/source/benchmarking/social_sciences/sioc.rst b/docs/source/benchmarking/social_sciences/sioc.rst index 8ef9a16..a9cfa10 100644 --- a/docs/source/benchmarking/social_sciences/sioc.rst +++ b/docs/source/benchmarking/social_sciences/sioc.rst @@ -23,10 +23,10 @@ Semantically-Interlinked Online Communities (SIOC) ======================================================================================================== -The SIOC (Semantically-Interlinked Online Communities) Ontology is a widely used ontology for describing the information and structure of online communities. It provides a standardized vocabulary for representing discussion forums, blogs, wikis, social networks, and other collaborative platforms. SIOC enables the modeling of users, posts, threads, topics, and relationships between community members and content. By providing a common framework, SIOC facilitates interoperability between social platforms, supports data integration, and enables advanced queries and analytics on social data. The ontology is used in social media mining, digital humanities, and knowledge graph construction to link and analyze user-generated content across platforms. SIOC is actively maintained and extended to support emerging social web technologies and applications. +The SIOC (Semantically-Interlinked Online Communities) Ontology is a widely used ontology for describing the information and structure of online communities on the Semantic Web [#sioc-spec]_ [#sioc-project]_. It provides a standardized vocabulary for representing discussion forums, blogs, wikis, mailing lists, social networks, and other collaborative platforms [#sioc-spec]_. SIOC enables the modeling of users, posts, threads, forums, sites, containers, topics, and relationships between community members and user-generated content [#sioc-spec]_. By providing a common semantic framework, SIOC facilitates interoperability between social platforms, supports data integration, and enables advanced queries and analytics over online community data [#sioc-project]_ [#sioc-spec]_. The ontology is used in social web applications, social media analysis, digital humanities, and knowledge graph construction to link and analyze user-generated content across platforms [#sioc-project]_. SIOC is designed to be extensible, allowing it to be used together with other vocabularies and modules for richer descriptions of online community data [#sioc-spec]_. **Example Usage**: -Annotate a forum or blog platform with SIOC terms to describe users, posts, threads, and relationships, enabling semantic search and cross-platform analysis of online community interactions. +Annotate a forum or blog platform with SIOC terms to describe users, posts, threads, forums, topics, and relationships between community members and content. This enables semantic search, cross-platform analysis, and interoperability of online community interactions [#sioc-spec]_ [#sioc-project]_. Metrics & Statistics -------------------------- @@ -135,3 +135,18 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#sioc-spec] Breslin, John G., Stefan Decker, Andreas Harth, + and Uldis Bojars. 2007. + "SIOC Core Ontology Specification." + W3C Member Submission. + Available at: + `https://www.w3.org/submissions/sioc-spec/ `_ + +.. [#sioc-project] SIOC Project. n.d. + "Semantically-Interlinked Online Communities." + Available at: + `https://sioc-project.org/ `_ diff --git a/docs/source/benchmarking/units_and_measurements/om.rst b/docs/source/benchmarking/units_and_measurements/om.rst index c2ba1db..dd5a55e 100644 --- a/docs/source/benchmarking/units_and_measurements/om.rst +++ b/docs/source/benchmarking/units_and_measurements/om.rst @@ -23,14 +23,14 @@ Ontology of Units of Measure (OM) ======================================================================================================== -The Ontology of units of Measure (OM) models concepts and relations important to scientific research. It has a strong focus on units, quantities, measurements, and dimensions. It includes, for instance, common units such as the SI units metre and kilogram, but also units from other systems of units such as the mile or nautical mile. For many application areas, it includes more specific units and quantities, such as the unit of the Hubble constant or the quantity vaselife. The following application areas are supported by OM: Geometry; Mechanics; Thermodynamics; Electromagnetism; Fluid mechanics; Chemical physics; Photometry; Radiometry and Radiobiology; Nuclear physics; Astronomy and Astrophysics; Cosmology; Earth science; Meteorology; Material science; Microbiology; Economics; Information technology and Typography. +The Ontology of units of Measure (OM) models concepts and relations important to scientific research, with a strong focus on units, quantities, measurements, measures, and dimensions [#om-github]_ [#om-paper]_. It includes common units such as the SI units ``metre`` and ``kilogram``, as well as units from other systems of units, including examples such as ``mile`` and ``nautical mile`` [#om-github]_. OM also includes more specialized units and quantities for many scientific and technical application areas, supporting fields such as geometry, mechanics, thermodynamics, electromagnetism, fluid mechanics, chemical physics, astronomy, Earth science, meteorology, material science, microbiology, economics, information technology, and typography [#om-github]_. The ontology provides classes, instances, and properties for defining and using measures and units, enabling quantitative research data to be represented more explicitly, integrated across sources, verified, and reproduced [#om-paper]_. OM supports interoperability by providing a standardized semantic framework for describing quantities, units, dimensions, scales, prefixes, systems of units, and measurement values [#om-github]_ [#om-paper]_. -The ontology employs a class-based modeling approach, defining classes for different types of units, quantities, and measurements, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. OM supports the integration of data from various sources, promoting interoperability and data-driven research in units and measurements. +The ontology employs a class-based modeling approach, defining classes for different types of units, quantities, and measurements, along with properties to describe their characteristics and relationships [#om-paper]_. Hierarchies are used to organize units, quantities, dimensions, and systems of units into structured categories, enabling efficient retrieval, comparison, conversion, and analysis [#om-github]_. OM supports the integration of quantitative data from diverse sources, promoting interoperability and data-driven research in units and measurements [#om-paper]_. -Typical applications of OM include the development of new measurement methods, the optimization of measurement practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, OM enhances collaboration and innovation in the field of units and measurements. +Typical applications of OM include semantic annotation of scientific datasets, explicit representation of measurement values, support for unit conversion, development of measurement-related tools and services, and integration of heterogeneous datasets for analytics and knowledge discovery [#om-github]_ [#om-paper]_. By providing a standardized vocabulary and framework, OM enhances collaboration, reproducibility, and innovation in domains that depend on precise units and measurements [#om-paper]_. **Example Usage**: -Annotate a scientific dataset with OM terms to specify units, quantities, and measurements, enabling semantic search and integration with measurement management platforms. +Annotate a scientific dataset with OM terms to specify quantities, units, dimensions, and measurement values, enabling semantic search, unit conversion, data integration, and interoperability with measurement management platforms [#om-github]_ [#om-paper]_. Metrics & Statistics -------------------------- @@ -139,3 +139,18 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#om-github] Rijgersberg, Hajo. n.d. + "Ontology of units of Measure (OM)." + GitHub Repository. + Available at: + `https://github.com/HajoRijgersberg/OM `_ + +.. [#om-paper] Rijgersberg, Hajo, Mark van Assem, and Jan L. Top. 2013. + "Ontology of Units of Measure and Related Concepts." + *Semantic Web* 4(1): 3--13. + DOI: + `10.3233/SW-2012-0069 `_ diff --git a/docs/source/benchmarking/units_and_measurements/owltime.rst b/docs/source/benchmarking/units_and_measurements/owltime.rst index 59e6e20..70ed2b1 100644 --- a/docs/source/benchmarking/units_and_measurements/owltime.rst +++ b/docs/source/benchmarking/units_and_measurements/owltime.rst @@ -23,10 +23,10 @@ Time Ontology in OWL (OWL-Time) ======================================================================================================== -The Time Ontology in OWL (OWL-Time) is a comprehensive ontology for representing temporal concepts, relationships, and properties in semantic web and linked data applications. It provides a standardized vocabulary for describing time instants, intervals, durations, temporal positions, and topological relations (e.g., before, after, during) among temporal entities. OWL-Time supports multiple temporal reference systems, including the Gregorian calendar, Unix time, geologic time, and custom calendars, enabling flexible modeling of temporal data. The ontology is widely used for annotating temporal aspects of resources in scientific datasets, event logs, web pages, and knowledge graphs. By providing a common framework, OWL-Time facilitates temporal reasoning, event sequencing, and integration of time-based data across domains. The ontology is maintained by the World Wide Web Consortium (W3C) and is continuously updated to support new temporal modeling requirements. +The Time Ontology in OWL (OWL-Time) is an OWL ontology for representing temporal concepts, relationships, and properties in Semantic Web and Linked Data applications [#owltime-w3c]_ [#owltime-ogc]_. It provides a standardized vocabulary for describing temporal entities, including instants and intervals, as well as durations, temporal positions, date-time descriptions, and ordering relations between temporal entities [#owltime-w3c]_. OWL-Time supports multiple temporal reference systems, allowing temporal positions and durations to be expressed using the Gregorian calendar and clock, Unix time, geologic time, or other temporal reference systems and calendars [#owltime-w3c]_. The ontology is used to annotate temporal aspects of resources in datasets, event logs, web pages, knowledge graphs, and other Web resources [#owltime-w3c]_ [#owltime-ogc]_. By providing a common semantic framework, OWL-Time facilitates temporal reasoning, event sequencing, temporal ordering, and integration of time-based data across domains [#owltime-w3c]_ [#owltime-ogc]_. OWL-Time is maintained as a W3C/OGC standard for describing temporal properties of resources on the Web [#owltime-w3c]_ [#owltime-ogc]_. **Example Usage**: -Annotate an event dataset with OWL-Time terms to specify event start and end times, durations, and temporal relationships (e.g., "event A before event B"), enabling temporal reasoning and timeline visualization in knowledge graphs. +Annotate an event dataset with OWL-Time terms to specify event start and end times, durations, temporal positions, and temporal relationships such as ``before``, ``after``, or ``during``. This enables temporal reasoning, event ordering, timeline visualization, and integration of temporal information in knowledge graphs [#owltime-w3c]_ [#owltime-ogc]_. Metrics & Statistics -------------------------- @@ -135,3 +135,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#owltime-w3c] W3C. 2022. + "Time Ontology in OWL." + W3C Candidate Recommendation Draft. + Available at: + `https://www.w3.org/TR/owl-time/ `_ + +.. [#owltime-ogc] Open Geospatial Consortium. n.d. + "Time Ontology in OWL." + Available at: + `https://www.ogc.org/standards/time-ontology-in-owl/ `_ diff --git a/docs/source/benchmarking/units_and_measurements/qudt.rst b/docs/source/benchmarking/units_and_measurements/qudt.rst index a8d5d66..3c61922 100644 --- a/docs/source/benchmarking/units_and_measurements/qudt.rst +++ b/docs/source/benchmarking/units_and_measurements/qudt.rst @@ -23,10 +23,10 @@ Quantities, Units, Dimensions and Data Types (QUDT) ======================================================================================================== -The Quantities, Units, Dimensions and Data Types (QUDT) ontology is a comprehensive framework for representing quantities, units, dimensions, and data types in scientific, engineering, and technical domains. QUDT provides a standardized vocabulary for describing measurement units, physical quantities, conversion factors, and dimensional analysis, supporting data expressed in RDF and JSON. The ontology is widely used for semantic annotation of scientific datasets, IoT data streams, and engineering models, enabling automated unit conversion, validation, and interoperability across diverse systems. QUDT is maintained by NASA Ames Research Center and is continuously updated to reflect new standards and measurement systems. By providing a common semantic foundation, QUDT facilitates data integration, analytics, and knowledge sharing in multidisciplinary projects. The ontology is extensible and can be aligned with other units and measurements ontologies for broader compatibility. +The Quantities, Units, Dimensions and Data Types (QUDT) ontology is a comprehensive framework for representing quantities, units, dimensions, and data types in scientific, engineering, and technical domains [#qudt-official]_ [#qudt-github]_. QUDT provides a standardized vocabulary for describing measurement units, quantity kinds, dimensional vectors, physical constants, conversion factors, and measurement values [#qudt-official]_. The ontology supports data expressed in RDF and related semantic web formats, enabling machine-readable representation of scientific and engineering measurements [#qudt-github]_. QUDT is widely used for semantic annotation of scientific datasets, IoT data streams, engineering models, and knowledge graphs, supporting automated unit conversion, validation, interoperability, and dimensional analysis [#qudt-official]_ [#qudt-github]_. The QUDT models are made publicly available through the QUDT project and public repository, supporting reuse, extension, and integration in semantic web and engineering applications [#qudt-official]_ [#qudt-github]_. By providing a common semantic foundation, QUDT facilitates data integration, analytics, and knowledge sharing across multidisciplinary projects [#qudt-official]_. **Example Usage**: -Annotate a scientific dataset with QUDT terms to specify the quantities measured (e.g., "temperature", "pressure"), their units (e.g., "degree Celsius", "pascal"), and conversion factors, enabling automated unit conversion and semantic search across datasets. +Annotate a scientific dataset with QUDT terms to specify the quantities measured, such as ``temperature`` or ``pressure``; their units, such as ``degree Celsius`` or ``pascal``; and conversion factors or dimensional information. This enables automated unit conversion, semantic search, validation, and integration across datasets [#qudt-official]_ [#qudt-github]_. Metrics & Statistics -------------------------- @@ -135,3 +135,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#qudt-official] QUDT. n.d. + "Quantities, Units, Dimensions and Data Types Ontologies." + Available at: + `https://www.qudt.org/ `_ + +.. [#qudt-github] QUDT. n.d. + "QUDT Public Repository." + GitHub Repository. + Available at: + `https://github.com/qudt/qudt-public-repo `_ diff --git a/docs/source/benchmarking/units_and_measurements/qudv.rst b/docs/source/benchmarking/units_and_measurements/qudv.rst index f7db8fa..c940af5 100644 --- a/docs/source/benchmarking/units_and_measurements/qudv.rst +++ b/docs/source/benchmarking/units_and_measurements/qudv.rst @@ -23,10 +23,10 @@ Quantities, Units, Dimensions and Values (QUDV) ======================================================================================================== -The Quantities, Units, Dimensions and Values (QUDV) ontology is a formal representation of the SysML QUDV modelLibrary, providing a standardized vocabulary for describing quantities, units, dimensions, and values in scientific and engineering domains. QUDV is specified in UML/SysML class/block diagrams and is designed to support interoperability and alignment with other standardization efforts concerning quantities and units. The ontology enables the semantic annotation of measurement data, supporting unit conversion, dimensional analysis, and data integration across diverse systems. QUDV is used in systems engineering, modeling and simulation, and scientific data management to ensure consistency and comparability of quantitative information. By providing a common framework, QUDV facilitates automated reasoning, validation, and knowledge sharing in multidisciplinary projects. The ontology is maintained by the SysML community and is aligned with other units and measurements ontologies for broader compatibility. +The Quantities, Units, Dimensions and Values (QUDV) ontology is a formal representation of the SysML QUDV model library, providing a standardized framework for describing quantities, units, dimensions, and values in scientific and engineering domains [#qudv-owl]_. The QUDV OWL representation was created to support ontology-based use of the SysML QUDV model and to enable alignment with other standardization efforts concerning quantities and units [#qudv-owl]_. QUDV supports the description of quantity kinds, measurement units, dimensions, values, and related measurement concepts, enabling quantitative information to be represented in a machine-readable form [#qudv-w3c]_. The ontology enables semantic annotation of measurement data, supporting unit representation, dimensional analysis, validation, and data integration across engineering and scientific systems [#qudv-owl]_ [#qudv-w3c]_. It is useful in systems engineering, modeling and simulation, sensor data annotation, and scientific data management where consistent representation of units, dimensions, and quantities is required [#qudv-w3c]_. By providing a common conceptual framework, QUDV facilitates interoperability, automated reasoning, validation, and knowledge sharing across multidisciplinary engineering and semantic web applications [#qudv-owl]_ [#qudv-w3c]_. **Example Usage**: -Annotate a systems engineering model with QUDV terms to specify the quantities measured (e.g., "length", "mass"), their units (e.g., "meter", "kilogram"), and dimensions, enabling automated unit conversion and validation across engineering tools. +Annotate a systems engineering model with QUDV terms to specify measured quantities such as ``length`` and ``mass``, their units such as ``meter`` and ``kilogram``, dimensions, and associated values. This enables unit-aware data integration, dimensional validation, and interoperability across engineering and semantic web tools [#qudv-owl]_ [#qudv-w3c]_. Metrics & Statistics -------------------------- @@ -135,3 +135,16 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#qudv-owl] OMG SysML Portal. 2009. + "QUDV OWL." + Available at: + `https://www.omgwiki.org/OMGSysML/doku.php?id=sysml-qudv:qudv_owl `_ + +.. [#qudv-w3c] W3C Semantic Sensor Network Incubator Group. n.d. + "Library for Quantity Kinds and Units." + Available at: + `https://www.w3.org/2005/Incubator/ssn/ssnx/qu/qu `_ diff --git a/docs/source/benchmarking/units_and_measurements/uo.rst b/docs/source/benchmarking/units_and_measurements/uo.rst index b4e35e8..7a22d0b 100644 --- a/docs/source/benchmarking/units_and_measurements/uo.rst +++ b/docs/source/benchmarking/units_and_measurements/uo.rst @@ -23,10 +23,10 @@ Units of Measurement Ontology (UO) ======================================================================================================== -The Units of Measurement Ontology (UO) is a domain ontology designed to provide a standardized vocabulary for metrical units, particularly for use in conjunction with the Phenotype and Trait Ontology (PATO) and other biomedical ontologies. UO covers a wide range of units, including SI units, derived units, and commonly used non-SI units, supporting the annotation of quantitative data in biological, medical, and scientific research. The ontology enables semantic interoperability and data integration by providing unique identifiers and relationships for units, quantities, and measurement systems. UO is widely used in bioinformatics, clinical informatics, and data repositories to ensure consistency and comparability of measurement data. By providing a common framework, UO facilitates automated data analysis, unit conversion, and cross-study comparison. The ontology is actively maintained and extended to incorporate new units and measurement concepts as research needs evolve. +The Units of Measurement Ontology (UO) is a domain ontology designed to provide a standardized vocabulary for metrical units, particularly for use with the Phenotype and Trait Ontology (PATO) and other biomedical ontologies [#uo-obofoundry]_ [#uo-paper]_. UO covers a wide range of measurement units, including SI units, derived units, and commonly used non-SI units, supporting the annotation of quantitative data in biological, medical, and scientific research [#uo-obofoundry]_ [#uo-paper]_. The ontology enables semantic interoperability and data integration by providing unique identifiers and structured relationships for units and measurement concepts [#uo-paper]_. UO is widely used in bioinformatics, clinical informatics, and scientific data repositories to ensure consistency and comparability of measurement data [#uo-paper]_. By providing a common framework for units of measurement, UO facilitates automated data analysis, semantic search, unit-aware annotation, and cross-study comparison [#uo-obofoundry]_ [#uo-paper]_. The ontology is maintained as part of the OBO Foundry ontology ecosystem [#uo-obofoundry]_. **Example Usage**: -Annotate a biomedical dataset with UO terms to specify measurement units for phenotypic traits (e.g., "UO:0000016 (centimeter)", "UO:0000021 (gram)") and enable automated unit conversion and semantic search across datasets. +Annotate a biomedical dataset with UO terms to specify measurement units for phenotypic traits, such as ``UO:0000016`` for ``centimeter`` or ``UO:0000021`` for ``gram``. This enables semantic search, consistent quantitative annotation, and comparison of measurement data across biomedical datasets [#uo-obofoundry]_ [#uo-paper]_. .. tab:: Breadth @@ -80,3 +80,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#uo-obofoundry] OBO Foundry. n.d. + "Units of measurement ontology." + Available at: + `https://obofoundry.org/ontology/uo.html `_ + +.. [#uo-paper] Gkoutos, Georgios V., Paul N. Schofield, and Robert Hoehndorf. 2012. + "The Units Ontology: a tool for integrating units of measurement in science." + *Database* 2012: bas033. + DOI: + `10.1093/database/bas033 `_ diff --git a/docs/source/benchmarking/upper_ontology/bfo.rst b/docs/source/benchmarking/upper_ontology/bfo.rst index 54e44c8..98230fa 100644 --- a/docs/source/benchmarking/upper_ontology/bfo.rst +++ b/docs/source/benchmarking/upper_ontology/bfo.rst @@ -23,10 +23,10 @@ Basic Formal Ontology (BFO) ======================================================================================================== -The Basic Formal Ontology (BFO) is a small, upper-level ontology that describes the basic types of entities in the world and how they relate to each other. BFO provides a rigorous, domain-neutral framework for organizing and integrating domain ontologies across scientific and technical disciplines. It distinguishes between continuants (entities that persist through time, such as objects and qualities) and occurrents (entities that unfold over time, such as processes and events), supporting precise modeling of reality. BFO is widely adopted in the biomedical, engineering, and environmental sciences as the upper ontology for the Open Biomedical Ontologies (OBO) Foundry and other ontology initiatives. Its formal structure enables automated reasoning, semantic interoperability, and data integration across heterogeneous knowledge bases. BFO is actively maintained and extended by an international community of ontology experts and is recognized as an ISO standard (ISO/IEC 21838-2:2021). +The Basic Formal Ontology (BFO) is a small, upper-level ontology that describes the basic types of entities in the world and how they relate to each other [#bfo-github]_ [#bfo-obo]_. BFO provides a rigorous, domain-neutral framework for organizing and integrating domain ontologies across scientific and technical disciplines [#bfo-github]_. It distinguishes between continuants, which are entities that persist through time, such as objects and qualities, and occurrents, which are entities that unfold over time, such as processes and events, supporting precise modeling of reality [#bfo-github]_ [#bfo-obo]_. BFO is widely adopted in the biomedical, engineering, and environmental sciences and is used as the upper ontology for the Open Biomedical Ontologies (OBO) Foundry [#bfo-obo]_. Its formal structure supports automated reasoning, semantic interoperability, and data integration across heterogeneous knowledge bases [#bfo-github]_. BFO is actively maintained and extended by an international ontology community through the BFO project and the OBO Foundry ecosystem [#bfo-github]_ [#bfo-obo]_. **Example Usage**: -Use BFO as the upper ontology for a biomedical ontology, classifying entities such as "cell" (object, continuant), "cell division" (process, occurrent), and "cell membrane" (object part), enabling semantic integration with other OBO ontologies. +Use BFO as the upper ontology for a biomedical ontology, classifying entities such as ``cell`` as an object/continuant, ``cell division`` as a process/occurrent, and ``cell membrane`` as an object part. This enables semantic integration with other OBO ontologies that share the same upper-level structure [#bfo-obo]_. Metrics & Statistics -------------------------- @@ -135,3 +135,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#bfo-github] BFO Ontology. n.d. + "Basic Formal Ontology (BFO)." + GitHub Repository. + Available at: + `https://github.com/bfo-ontology/bfo `_ + +.. [#bfo-obo] OBO Foundry. n.d. + "Basic Formal Ontology." + Available at: + `https://obofoundry.org/ontology/bfo.html `_ diff --git a/docs/source/benchmarking/upper_ontology/dolce.rst b/docs/source/benchmarking/upper_ontology/dolce.rst index 1f1101d..53a61b7 100644 --- a/docs/source/benchmarking/upper_ontology/dolce.rst +++ b/docs/source/benchmarking/upper_ontology/dolce.rst @@ -23,10 +23,10 @@ Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) ======================================================================================================== -The Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) is a foundational ontology that provides a conceptual framework for the formalization of domain ontologies. DOLCE is designed to capture the ontological categories underlying natural language and human common sense, supporting the modeling of linguistic, cognitive, and social phenomena. It distinguishes between endurants (entities persisting through time), perdurants (events and processes), qualities, and abstract entities, enabling nuanced representation of reality. DOLCE is widely used in linguistics, cognitive science, artificial intelligence, and knowledge engineering to support semantic interoperability and reasoning. Its modular structure allows for extensions and customization for specific domains, making it a popular choice for building interoperable ontologies. DOLCE has influenced the development of many domain ontologies and is recognized for its rigorous formal foundations and alignment with human conceptualization. +The Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) is a foundational ontology that provides a conceptual framework for the formalization of domain ontologies [#dolce-loa]_ [#dolce-paper]_. DOLCE is designed to capture the ontological categories underlying natural language and human common sense, supporting the modeling of linguistic, cognitive, social, and commonsense phenomena [#dolce-loa]_ [#dolce-paper]_. It distinguishes between endurants, which persist through time; perdurants, such as events and processes; qualities; and abstract entities, enabling nuanced representation of reality [#dolce-paper]_. DOLCE is used in ontology engineering and knowledge representation to support semantic interoperability, conceptual analysis, and reasoning across domains [#dolce-loa]_ [#dolce-paper]_. Its formal foundations and modeling principles have influenced many ontology initiatives and have been applied in areas such as socio-technical systems, manufacturing, financial transactions, cultural heritage, and linguistic resources [#dolce-paper]_. DOLCE provides general categories and relations that help integrate domain knowledge and mediate across heterogeneous ontologies [#dolce-paper]_. **Example Usage**: -Use DOLCE as the upper ontology for a linguistic ontology, classifying entities such as "utterance" (perdurant), "speaker" (endurant), and "meaning" (abstract), enabling semantic integration with other cognitive and linguistic resources. +Use DOLCE as the upper ontology for a linguistic ontology, classifying entities such as ``utterance`` as a perdurant, ``speaker`` as an endurant, and ``meaning`` as an abstract entity. This enables semantic integration with other cognitive, linguistic, and commonsense knowledge resources [#dolce-loa]_ [#dolce-paper]_. Metrics & Statistics -------------------------- @@ -135,3 +135,18 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#dolce-loa] Laboratory for Applied Ontology, ISTC-CNR. n.d. + "Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE)." + Available at: + `https://www.loa.istc.cnr.it/index.php/dolce/ `_ + +.. [#dolce-paper] Borgo, Stefano, Roberta Ferrario, Aldo Gangemi, + Nicola Guarino, Claudio Masolo, Daniele Porello, + Emilio M. Sanfilippo, and Laure Vieu. 2023. + "DOLCE: A Descriptive Ontology for Linguistic and Cognitive Engineering." + Available at: + `https://arxiv.org/abs/2308.01597 `_ diff --git a/docs/source/benchmarking/upper_ontology/fair.rst b/docs/source/benchmarking/upper_ontology/fair.rst index 428b3ea..297bc21 100644 --- a/docs/source/benchmarking/upper_ontology/fair.rst +++ b/docs/source/benchmarking/upper_ontology/fair.rst @@ -24,10 +24,9 @@ FAIR Vocabulary (FAIR) ======================================================================================================== +The FAIR Vocabulary is a formal vocabulary that provides machine-readable definitions and semantic representation of the FAIR Data Principles: Findability, Accessibility, Interoperability, and Reusability [#fair-vocabulary]_. It formalizes key concepts underlying FAIR data management, including persistent identifiers, metadata, access protocols, interoperability standards, qualified references, provenance, licensing, and reusability conditions [#fair-vocabulary]_. The vocabulary represents FAIR principles and sub-principles as semantic entities, allowing them to be referenced, linked, and interpreted in machine-readable environments [#fair-vocabulary]_. FAIR vocabulary terms can be applied to dataset descriptions, data repositories, metadata records, and digital object properties to formally express FAIR characteristics and support FAIR assessment workflows [#fair-vocabulary]_. By providing standardized semantic definitions, the vocabulary facilitates communication of FAIR principles within research organizations, funding agencies, repositories, and data management communities [#fair-vocabulary]_. -The FAIR Vocabulary is a formal ontology that provides machine-readable definitions and semantic representation of the FAIR Data Principles (Findability, Accessibility, Interoperability, Reusability). It formalizes key concepts underlying FAIR data management including data discoverability mechanisms, access protocols, interoperability standards, and reusability conditions. The ontology enables automated assessment and verification of FAIR compliance, supporting the development of FAIR data management tools and services. FAIR vocabulary terms can be applied to dataset descriptions, data repositories, and digital object properties to formally declare their FAIR characteristics and compliance levels. The vocabulary facilitates communication of FAIR principles within research organizations, funding agencies, and data management communities by providing standardized semantic definitions. - -**Example Usage**: Annotate a dataset in a repository with FAIR terms to indicate its Findability (via persistent identifiers like DOIs), Accessibility (through standard protocols), Interoperability (using standard formats and ontologies), and Reusability (via clear licensing and metadata). +**Example Usage**: Annotate a dataset in a repository with FAIR terms to indicate its Findability through persistent identifiers, Accessibility through standard access protocols, Interoperability through shared formats and ontologies, and Reusability through clear licensing, provenance, and rich metadata [#fair-vocabulary]_. Metrics & Statistics -------------------------- @@ -136,3 +135,11 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#fair-vocabulary] FAIR Principles Vocabulary. n.d. + "FAIR Vocabulary." + Available at: + `https://peta-pico.github.io/FAIR-nanopubs/principles/index-en.html `_ diff --git a/docs/source/benchmarking/upper_ontology/gfo.rst b/docs/source/benchmarking/upper_ontology/gfo.rst index cf1084b..519b6c7 100644 --- a/docs/source/benchmarking/upper_ontology/gfo.rst +++ b/docs/source/benchmarking/upper_ontology/gfo.rst @@ -23,10 +23,10 @@ General Formal Ontology (GFO) ======================================================================================================== -The General Formal Ontology (GFO) is a top-level, foundational ontology developed by Onto-Med for conceptual modeling across scientific, technical, and philosophical domains. GFO provides a rigorous framework for representing fundamental categories such as objects, processes, time, space, properties, relations, roles, functions, facts, and situations. It is designed to support the integration of material, mental, and social entities by incorporating the notion of levels of reality, enabling nuanced modeling of complex systems. GFO is modular and extensible, allowing domain ontologies to build upon its core categories for specialized applications. The ontology is used in knowledge engineering, biomedical informatics, cognitive science, and information systems to ensure semantic interoperability and logical consistency. GFO is actively maintained and extended to address emerging requirements in conceptual modeling and ontology integration. +The General Formal Ontology (GFO) is a top-level foundational ontology developed for conceptual modeling across scientific, technical, and philosophical domains [#gfo-github]_ [#gfo-paper]_. GFO provides a rigorous framework for representing fundamental categories such as objects, processes, time, space, properties, relations, roles, functions, facts, and situations [#gfo-github]_. It is designed to support the integration of material, mental, and social entities by incorporating the notion of levels of reality, enabling nuanced modeling of complex systems [#gfo-paper]_. GFO is modular and extensible, allowing domain ontologies to build upon its core categories for specialized applications [#gfo-github]_ [#gfo-paper]_. The ontology is used in knowledge engineering, biomedical informatics, cognitive science, and information systems to support semantic interoperability and logical consistency [#gfo-paper]_. GFO is maintained as an ontology resource through the Onto-Med GitHub repository and continues to support conceptual modeling and ontology integration work [#gfo-github]_. **Example Usage**: -Use GFO as the upper ontology for a biomedical ontology, classifying entities such as "disease" (situation), "patient" (object), and "treatment process" (process), enabling semantic integration and reasoning across clinical and research data. +Use GFO as the upper ontology for a biomedical ontology, classifying entities such as ``disease`` as a situation, ``patient`` as an object, and ``treatment process`` as a process, enabling semantic integration and reasoning across clinical and research data [#gfo-github]_ [#gfo-paper]_. Metrics & Statistics -------------------------- @@ -135,3 +135,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- +.. [#gfo-github] Onto-Med. n.d. + "GFO: The General Formal Ontology." + GitHub Repository. + Available at: + `https://github.com/Onto-Med/GFO `_ + +.. [#gfo-paper] Loebe, Frank, Patryk Burek, and Heinrich Herre. 2022. + "GFO: The General Formal Ontology." + *Applied Ontology*. + DOI: + `10.3233/AO-220264 `_ diff --git a/docs/source/benchmarking/upper_ontology/sio.rst b/docs/source/benchmarking/upper_ontology/sio.rst index 49592fa..85eb4d2 100644 --- a/docs/source/benchmarking/upper_ontology/sio.rst +++ b/docs/source/benchmarking/upper_ontology/sio.rst @@ -25,9 +25,9 @@ Semanticscience Integrated Ontology (SIO) ======================================================================================================== -The Semanticscience Integrated Ontology (SIO) is a simple yet comprehensive upper-level ontology that provides foundational types and relations for consistent semantic knowledge representation across physical entities, processes, and information constructs. SIO defines core abstract concepts such as objects, processes, attributes, and information entities, enabling researchers to build domain-specific ontologies with consistent semantic foundations. The ontology supports interconnection of diverse knowledge domains through its generalized entity and relationship types, facilitating interoperability across heterogeneous biological and biomedical ontologies. SIO serves as the semantic backbone for major linked data projects including Bio2RDF (which integrates biological databases) and SADI (Semantic Automated Discovery and Integration framework). The ontology enables semantic reasoning and automated knowledge discovery by providing explicit typing and relationship hierarchies that computationally systems can interpret. +The Semanticscience Integrated Ontology (SIO) is a simple yet comprehensive upper-level ontology that provides foundational types and relations for consistent semantic knowledge representation across physical entities, processes, attributes, and information constructs [#sio-github]_ [#sio-paper]_. SIO defines core abstract concepts such as objects, processes, attributes, qualities, capabilities, functions, quantities, and information entities, enabling researchers to build domain-specific ontologies with consistent semantic foundations [#sio-paper]_. The ontology supports the interconnection of diverse knowledge domains through generalized entity and relationship types, facilitating interoperability across heterogeneous biological and biomedical ontologies [#sio-paper]_. SIO serves as a semantic foundation for linked data and semantic web projects including Bio2RDF and SADI, supporting biomedical data integration, semantic service discovery, and automated knowledge discovery [#sio-github]_ [#sio-paper]_. The ontology enables semantic reasoning and machine-actionable knowledge representation by providing explicit typing, relation hierarchies, and reusable design patterns that computational systems can interpret [#sio-paper]_. -**Example Usage**: Use SIO classes to type entities in a knowledge graph (e.g., SIO:Protein, SIO:Gene, SIO:Organism) and relate them via SIO properties (e.g., SIO:is-derived-from, SIO:encodes, SIO:has-function) for automated biomedical data integration and discovery. +**Example Usage**: Use SIO classes to type entities in a biomedical knowledge graph, such as ``protein``, ``gene``, ``organism``, ``dataset``, or ``data analysis``, and relate them using SIO properties to support automated biomedical data integration, semantic querying, and knowledge discovery [#sio-github]_ [#sio-paper]_. Metrics & Statistics -------------------------- @@ -136,3 +136,18 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#sio-github] MaastrichtU-IDS. n.d. + "Semanticscience Integrated Ontology (SIO)." + GitHub Repository. + Available at: + `https://github.com/MaastrichtU-IDS/semanticscience `_ + +.. [#sio-paper] Dumontier, Michel, et al. 2014. + "The Semanticscience Integrated Ontology (SIO) for biomedical research and knowledge discovery." + *Journal of Biomedical Semantics* 5:14. + DOI: + `10.1186/2041-1480-5-14 `_ diff --git a/docs/source/benchmarking/upper_ontology/sumo.rst b/docs/source/benchmarking/upper_ontology/sumo.rst index b7ebd7b..41de78c 100644 --- a/docs/source/benchmarking/upper_ontology/sumo.rst +++ b/docs/source/benchmarking/upper_ontology/sumo.rst @@ -23,10 +23,10 @@ Suggested Upper Merged Ontology (SUMO) ======================================================================================================== -The Suggested Upper Merged Ontology (SUMO) is one of the largest and most widely used formal upper ontologies, providing a comprehensive framework for representing general concepts and relationships across all domains of knowledge. SUMO and its domain ontologies are used for research and applications in search, linguistics, automated reasoning, and artificial intelligence. SUMO is unique in being formally mapped to the entire WordNet lexicon, enabling semantic integration between natural language and formal knowledge representation. The ontology covers abstract and concrete entities, processes, attributes, relations, and events, supporting logical inference and knowledge discovery. SUMO is open source and maintained by an active community, with ongoing extensions and domain-specific modules for specialized applications. By providing a rigorous semantic foundation, SUMO facilitates interoperability, data integration, and advanced reasoning in knowledge-based systems. +The Suggested Upper Merged Ontology (SUMO) is one of the largest and most widely used formal upper ontologies, providing a comprehensive framework for representing general concepts and relationships across many domains of knowledge [#sumo-github]_ [#sumo-paper]_. SUMO and its domain ontologies are used for research and applications in search, linguistics, automated reasoning, semantic interoperability, and artificial intelligence [#sumo-paper]_. The ontology covers abstract and concrete entities, processes, attributes, relations, and events, supporting logical inference and knowledge discovery [#sumo-github]_ [#sumo-paper]_. SUMO is open source and maintained through the Ontology Portal project, with ongoing extensions and domain-specific modules for specialized applications [#sumo-github]_. By providing a rigorous semantic foundation, SUMO facilitates interoperability, data integration, and advanced reasoning in knowledge-based systems [#sumo-paper]_. **Example Usage**: -Use SUMO as the upper ontology for a knowledge graph, mapping domain-specific concepts (e.g., "vehicle," "disease," "financial transaction") to SUMO classes and leveraging its logical axioms for automated reasoning and semantic search. +Use SUMO as an upper ontology for a domain knowledge graph, mapping domain entities such as ``Vehicle``, ``Process``, ``Agent``, or ``Communication`` to SUMO classes and relations. This enables logical reasoning, semantic search, knowledge discovery, and integration across heterogeneous knowledge-based systems [#sumo-github]_ [#sumo-paper]_. Metrics & Statistics -------------------------- @@ -135,3 +135,18 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#sumo-github] Ontology Portal. n.d. + "SUMO: Suggested Upper Merged Ontology." + GitHub Repository. + Available at: + `https://github.com/ontologyportal/sumo `_ + +.. [#sumo-paper] Pease, Adam, Ian Niles, and John Li. 2002. + "The Suggested Upper Merged Ontology: A Large Ontology for the Semantic Web and its Applications." + *AAAI Workshop on Ontologies and the Semantic Web*. + Available at: + `https://cdn.aaai.org/Workshops/2002/WS-02-11/WS02-11-011.pdf `_ diff --git a/docs/source/benchmarking/web_and_internet/hydra.rst b/docs/source/benchmarking/web_and_internet/hydra.rst index 1b8750f..4f4a2a5 100644 --- a/docs/source/benchmarking/web_and_internet/hydra.rst +++ b/docs/source/benchmarking/web_and_internet/hydra.rst @@ -25,9 +25,9 @@ Hydra Ontology (Hydra) ======================================================================================================== -Hydra is a lightweight vocabulary and ontology for creating hypermedia-driven REST APIs that are self-describing and machine-actionable through semantic web technologies. It enables developers to create generic API clients that can automatically discover and interact with APIs by interpreting hypermedia controls and semantic metadata embedded in API responses. Hydra defines core concepts commonly used in Web APIs such as operations, properties, classes, and relationships, providing a standardized way to describe API structure and functionality. The vocabulary enables APIs to be self-documenting and interoperable, allowing clients to dynamically adapt to API changes without hardcoded endpoint knowledge. Hydra supports linked data and semantic web principles, enabling APIs to contribute to the broader linked open data ecosystem. +Hydra is a lightweight vocabulary and ontology for creating hypermedia-driven Web APIs that are self-describing and machine-actionable through semantic web technologies [#hydra-core]_ [#hydra-paper]_. It enables developers to create generic API clients that can automatically discover and interact with APIs by interpreting hypermedia controls and semantic metadata embedded in API responses [#hydra-core]_. Hydra defines core concepts commonly used in Web APIs, such as API documentation, supported classes, supported properties, operations, links, collections, and entry points, providing a standardized way to describe API structure and functionality [#hydra-core]_. The vocabulary enables APIs to be more self-describing and interoperable, allowing clients to understand available state transitions and construct valid HTTP requests without relying only on hardcoded endpoint knowledge [#hydra-core]_ [#hydra-paper]_. Hydra supports Linked Data and REST principles, helping APIs contribute to broader linked data ecosystems while preserving loose coupling, maintainability, evolvability, and scalability [#hydra-paper]_. -**Example Usage**: Define a REST API endpoint for a resource collection using Hydra vocabularies to describe available operations (GET, POST, DELETE), supported classes, properties with their types, and hypermedia links to related resources, enabling automated client discovery and interaction. +**Example Usage**: Define a REST API endpoint for a resource collection using Hydra vocabulary terms to describe available operations such as ``GET``, ``POST``, and ``DELETE``, supported classes, properties and their types, entry points, collections, and hypermedia links to related resources. This enables automated client discovery, interaction, and adaptation to API structure [#hydra-core]_ [#hydra-paper]_. Metrics & Statistics -------------------------- @@ -136,3 +136,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#hydra-core] Hydra W3C Community Group. 2021. + "Hydra Core Vocabulary." + Available at: + `https://www.hydra-cg.com/spec/latest/core/ `_ + +.. [#hydra-paper] Lanthaler, Markus, and Christian Gütl. 2013. + "Hydra: A Vocabulary for Hypermedia-Driven Web APIs." + *Proceedings of the 6th Workshop on Linked Data on the Web (LDOW 2013)*. + Available at: + `https://ceur-ws.org/Vol-996/papers/ldow2013-paper-03.pdf `_ diff --git a/docs/source/benchmarking/web_and_internet/saref.rst b/docs/source/benchmarking/web_and_internet/saref.rst index 7566037..ae33e07 100644 --- a/docs/source/benchmarking/web_and_internet/saref.rst +++ b/docs/source/benchmarking/web_and_internet/saref.rst @@ -25,9 +25,9 @@ Smart Applications REFerence ontology (SAREF) ======================================================================================================== -SAREF is a comprehensive suite of interrelated ontologies that defines a shared model of consensus for enabling semantic interoperability across IoT solutions and smart applications from diverse providers and industry sectors. It provides standardized vocabulary for describing smart devices, their capabilities, interactions, and services in Internet of Things (IoT) and smart environments (smart homes, smart buildings, smart cities). SAREF captures essential IoT concepts including devices, sensors, actuators, services, properties, and the relationships between them, facilitating machine-to-machine communication and automation. The SAREF suite is published as open standards by ETSI Technical Committee SmartM2M (Smart Machine-to-Machine communications), ensuring broad industry adoption and compatibility. SAREF enables semantic data spaces by providing common terminology that allows data and services from different IoT platforms and manufacturers to be integrated and understood. +SAREF is a comprehensive suite of interrelated ontologies that defines a shared model of consensus for enabling semantic interoperability across IoT solutions and smart applications from diverse providers and industry sectors [#saref-portal]_. It provides a standardized vocabulary for describing smart devices, their capabilities, interactions, services, properties, states, measurements, and relationships in Internet of Things (IoT) and smart environments such as smart homes, smart buildings, smart cities, industry, agriculture, energy, and health [#saref-portal]_ [#saref-core]_. SAREF captures essential IoT concepts including devices, sensors, actuators, services, properties, and the relationships between them, facilitating machine-to-machine communication, automation, and semantic interoperability [#saref-core]_. The SAREF suite is published by ETSI and provides common terminology that allows data and services from different IoT platforms, applications, and manufacturers to be integrated and understood [#saref-portal]_. SAREF supports semantic data spaces by enabling interoperable descriptions of devices, measurements, properties, services, and their relationships across heterogeneous IoT ecosystems [#saref-core]_. -**Example Usage**: Annotate an IoT device deployment with SAREF terms describing the device type (smart thermostat, light bulb, motion sensor), its capabilities (temperature measurement, brightness control), services offered (remote adjustment, scheduling), and relationships to control systems and user preferences. +**Example Usage**: Annotate an IoT device deployment with SAREF terms describing the device type, such as ``smart thermostat``, ``light bulb``, or ``motion sensor``; its capabilities, such as temperature measurement or brightness control; the services it offers, such as remote adjustment or scheduling; and its relationships to control systems, spaces, and user preferences [#saref-core]_ [#saref-portal]_. Metrics & Statistics -------------------------- @@ -136,3 +136,16 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#saref-portal] ETSI. n.d. + "SAREF Ontology." + Available at: + `https://saref.etsi.org/ `_ + +.. [#saref-core] ETSI. 2025. + "SAREF Core Ontology." + Available at: + `https://saref.etsi.org/core/v4.1.1/ `_