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FAIR data requires FAIR ontologies, how do we do?


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Presentation at RDA P11 IGAD pre-meeting – Berlin, March 2018

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FAIR data requires FAIR ontologies, how do we do?

  1. 1. FAIR data requires FAIR ontologies, how do we do? Clement Jonquet, PhD Assistant Professor – LIRMM, University of Montpellier Visiting Scholar at Stanford University RDA P11 IGAD pre-meeting – Berlin, March 2018
  2. 2. As any data, ontologies need to be FAIR • The FAIR principles have established the importance of using standards vocabularies or ontologies to describe FAIR data and to facilitate interoperability and reuse… • Explosion of the number of ontologies/vocabularies • Cumbersome to identify the ontologies we need and manage their overlap.
  3. 3. Review of ontology metadata practices: Methods • Conducted three different studies: 1. Analysis of the existing metadata vocabularies for describing ontologies & literature survey • More than 23 vocabularies, around 450 properties reviewed 2. Analysis of the uses of metadata vocabularies in describing the ontologies (by the ontology developers) • 805 ontologies analyzed 3. Analysis of the uses of metadata vocabularies in various ontology repositories • 12 libraries C. Jonquet – RDA P11 IGAD pre-meeting – Berlin, March 2018
  4. 4. Review of ontology metadata practices: Findings • Developers use a variety of metadata vocabularies (e.g., DC, DCT, PROV,VOID, DCAT, SCHEMA) • Interestingly: the only ontology specific metadata OMV (first published in 2005) is found to be hardly used by the community • No existing vocabularies really covers enough aspects of ontologies to be used solely and despite • Despite a few exceptions, metadata vocabularies do not rely on one another although there is a strong overlap observed • Multiple properties to capture similar information (e.g., dc:license, and cc:license) • Strong overlap in all the vocabularies (25 properties available for dates) • Reviewed libraries uses, to some extent, some metadata elements but do not always use standard metadata vocabularies • 16% of ontologies did not use any metadata properties, 43% use less than 10 properties • Properties facilitated by ontology editors are more frequent • General purpose elements (e.g., rdfs:comment, owl:versionOf and owl:imports) are found to be the most frequently used elements • Confusion of use: DC/DCT or SKOS documentation properties used to describe ontologies C. Jonquet – RDA P11 IGAD pre-meeting – Berlin, March 2018
  5. 5. Ontology repositories help to make ontologies FAIR
  6. 6. Linked Open Data cloud in 2017 ( NCBO BioPortal data as of 2013
  7. 7. Ontology repositories help to make ontologies FAIR InteroperableFindable Accessible Re-usable
  8. 8. Ontology libraries, registries, repositories • Ontology libraries defined as • “a library system that offers various functions for managing, adapting and standardizing groups of ontologies. It should fulfill the needs for re-use of ontologies. In this sense, an ontology library system should be easily accessible and offer efficient support for re-using existing relevant ontologies and standardizing them based on upper-level ontologies and ontology representation languages.” [Ding & Fensel, 2001] • Ontology repositories defined as • “a structured collection of ontologies (…) by using an Ontology MetadataVocabulary. References and relations between ontologies and their modules build the semantic model of an ontology repository.Access to resources is realized through semantically- enabled interfaces applicable for humans and machines.Therefore a repository provides a formal query language” [Hartmann, Palma, Gomez-Perez, 2009]
  9. 9. What are the ontology libraries out there? • Ontology repositories / portal • NCBO BioPortal • Ontobee • AberOWL • EBI Ontology Lookup Service • OKFN Linked OpenVocabularies • ONKI Ontology Library Service • MMI Ontology Registry and Repository • ESIPportal • AgroPortal • SIFR BioPortal • CISMEF HeTOP • OntoHub • Web indexes • Watson, Swoogle, Sindice, Falcons • Ontology libraries / listings (more or less updated) • OBO Foundry • WebProtégé • Romulus • DAML ontology library • Colore • FAOVEST Registry • BioSharing • DERIVocabularies , OntologyDesignPatterns,,W3C Good ontologies • ANDS • Platform technology • Mondeca ITM, LexEVS, SKOSMOS, SissVoc • Abandoned projects • Cubboard, Knoodl, Schemapedia, SchemaWeb, OntoSelect, OntoSearch,TONES
  10. 10. Focus on NCBO BioPortal : a “one stop shop” for biomedical ontologies • Web repository for biomedical ontologies • Make ontologies accessible and usable – abstraction on format, locations, structure, etc. • Users can publish, download, browse, search, comment, align ontologies and use them for annotations both online and via a web services API.
  11. 11. • Online support for ontology • Peer review & notes • Versioning • Mapping • Search • Resources • Annotation • Open source technology • Packaged in a “virtual appliance” • Set up your own “bioportal” in a few hours
  12. 12. Ontology Services • Search • Traverse • Comment • Download Widgets • Tree-view • Auto-complete • Graph-view Annotation Data Access Mapping Services • Create • Upload • Download Term recognition Search data annotated with a given term
  13. 13. Who has been reusing NCBO technology so far? • Recently • AgroPortal ( – agronomy, food, plant sciences, biodiveristy • SIFR/French BioPortal ( – French biomedical ontologies & terminologies • BiblioPortal ( – libraries and metadata standards • EcoPortal – ongoing discussion with the Lifewatch/LTER projects for a more focused portal on ecology & biodiversity • Historically • NCI term browser ( – BioPortal first, then LexEVS • Open Ontology Repository (OOR) Initiative ( – Now stopped. Looked also at OntoHub • Marine Metadata Interoperability Ontology Registry and Repository ( • ESIPPortal (Earth Science Information Partners - ) • And a few hospitals, research labs, with private data and specific needs (often in-house annotation)
  14. 14. AgroPortal: a vocabulary and ontology repository for agronomy
  15. 15. AgroPortal: a vocabulary and ontology repository for agronomy • Develop and support a reference ontology repository • Primary focus on the agronomy & close related domains (food, plant sciences and biodiversity) • Reusing the NCBO BioPortal technology • Avoid to re-implement what has been done, facilitate interoperability • Reusing the scientific outcomes, experience & methods of the biomedical domain • Enable straightforward use of agronomy ontologies • Respect the requirements & specificities of the agronomic community • Fully semantic web compliant infrastructure • Enable new science Jonquet, C., Toulet, A., Arnaud, E., Aubin, S., DzaléYeumo, E., Emonet, V., Graybeal, J., Laporte, M.-A., Musen, M.A., Pesce, V., Larmande, P., AgroPortal: A vocabulary and ontology repository for agronomy. Comput. Electron. Agric. 144, (Jan 2018).
  16. 16. AgroPortal an ontology repository for agronomy, food, plant sciences & biodiversity Publish, search, download Browse, visualize Peer review Versioning Annotation Recommendation Mapping Notes Projects 80 ontologies, 95 candidates 5 driving use cases ~90 registered users http://agroportal.
  17. 17. AgroPortal Annotator identifies ontology concepts within plain text for semantic indexing
  18. 18. Align ontologies one another concept by concept
  19. 19. AgroPortal Recommender get the most relevant ontologies for your data
  20. 20. Harnessing the power of metadata to facilitate the comprehension of the agronomical ontology landscape
  21. 21. A new metadata model to better support description of ontologies and their relations • Building a list of properties to describe ontologies • Pickup properties and relations from 23 existing vocabularies • Existing properties in ontology repositories (especially BioPortal) • Non specific properties that “belong to the ontology” 346 relevant properties that could be used to described ontologies 127 used to build a new metadata model inside AgroPortal Ontology repositories metadata Other Interesting vocabularies (e.g., IDOT, PAV, SD, DOAP, …) Standards & Relevant (e.g., DC, DCAT, SKOS, OWL, PROV, OMV,VOID, VOAF, MOD …)
  22. 22. Describe ontologies with semantic metadata • Display “per ontology” • Ontology specific properties => viewable and editable within the ontology specific page • Everything you need to know about an ontology • URIs used in the backend to store the information • e.g., CC-BY => • Get my metadata back button
  23. 23. Browse and select ontologies • Allows to search, order and select ontologies using a facetted search approach, based on the metadata • 4 additional ways to filter ontologies in the list • 2 new options to sort this list (name, released date).
  24. 24. AgroPortal Landscape page Display “per property” • Global presentation of the properties • Synthesis diagrams & listing • Metadata automatically extracted from the files and authored by us and the ontology developpers • Allows to explore the agronomical ontology landscape by automatically aggregating the metadata fields of each ontologies in explicit vizualizations (charts, term cloud and graphs). Jonquet, C., Toulet, A., Dutta, B., Emonet, V.: Harnessing the power of unified metadata in an ontology repository: the case of AgroPortal. Data Semant. UNDER REVIEW.
  25. 25. All of it accessible thru JSON-LD API
  26. 26. On going work within RDA groups
  27. 27. Involvement in several groups • Several IGAD interest groups interested by AgroPortal: • Agrisemantics, • Wheat/Rice Data Interoperability • Synchronization with FAIRsharing • Ontology-metadata Task Group inside Vocabulary and Semantic Services Interest Group (VSSIG)
  28. 28. • Metadata vocabulary for Ontology Description and publication (v.1.2) • 88 properties, only 13 new ones • Ontology • To be discussed within the RDA Vocabulary and Semantic Services Interest Group (VSSIG) Generalizing this with MOD Dutta, B., … Jonquet, C.: New Generation Metadata vocabulary for Ontolog yDescription and Publication. 11th Metadata and Semantics Research Conference, MTSR’17. , Tallinn, Estonia (2017).
  29. 29. Conclusion • Good ontologies are required for FAIR data • Metadata are important to FAIR ontologies • Continue our work on repository ontologies to ease the sharing of FAIR ontologies and vocabularies • Measure FAIRness level for ontologies with metrics
  30. 30. Credits • Anne Toulet,Vincent Emonet LIRMM – University of Montpellier, France • Biswanath Dutta Documentation Research andTraining Centre (DRTC) Indian Statistical Institute, Bangalore, India • RDA Vocabulary and Semantic Services Interest Group (VSSIG) Ontology-metadataTask Group • The same + • Barbara Magagna Ecosystem Research & Environmental Information Management Ökosystemforschung & Umweltinformationsmanagement
  31. 31. Thank you! @jonquet_lirmm