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International Conference on Knowledge Engineering and Knowledge Management

EKAW 2020: Knowledge Engineering and Knowledge Management pp 255–270Cite as

Coming to Terms with FAIR Ontologies

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12387))

Abstract

Ontologies are widely used nowadays for many different purposes and in many different contexts, like industry and research, and in domains ranging from geosciences, biology, chemistry or medicine. When used for research, ontologies should be treated as other research artefacts, such as data, software, methods, etc.; following the same principles used to make them findable, accessible, interoperable and reusable (FAIR) to others. However, in comparison to the number of guides, indicators and recommendations available for making research data FAIR, not much attention has been paid so far on how to publish ontologies following the FAIR principles. This position paper reviews the technical and social needs required to define a roadmap for generating and publishing FAIR ontologies on the Web. We analyze four initiatives for ontology publication, aligning them in a common framework for comparison. The paper concludes by opening a discussion about existing, ongoing and required initiatives and instruments to facilitate FAIR ontology sharing on the Web.

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Notes

  1. 1.

    https://www.eosc-portal.eu/.

  2. 2.

    https://www.rd-alliance.org/.

  3. 3.

    We point in parentheses to the principles numeration used in the original FAIR paper [35].

  4. 4.

    https://fairsfair.eu/.

  5. 5.

    https://www.go-fair.org/implementation-networks/overview/go-inter/.

  6. 6.

    https://www.rd-alliance.org/groups/vocabulary-services-interest-group.html.

  7. 7.

    https://www.w3.org/DesignIssues/LinkedData.html.

  8. 8.

    http://www.obofoundry.org/principles/fp-000-summary.html.

  9. 9.

    https://www.industrialontologies.org/?page_id=87.

  10. 10.

    https://bvatant.blogspot.com/2012/02/is-your-linked-data-vocabulary-5-star_9588.html.

  11. 11.

    Note that the “open license” is added to the first star as a comment by the author as a reaction to the feedback, but not shown in the original list.

  12. 12.

    To ease the reading of the rest of the paper we recommend to have the FAIR principles list (Annex A) and Sect. 3 at hand.

  13. 13.

    https://www.w3.org/Submission/SWRL/.

  14. 14.

    http://shex.io/shex-primer/.

  15. 15.

    https://w3id.org/widoco/bestPractices.

  16. 16.

    European Data Portal https://www.europeandataportal.eu/en.

  17. 17.

    https://www.w3.org/TR/vocab-dcat-2.

  18. 18.

    Acronym adapted from the PODs defined in [33] as Personal Online Data.

  19. 19.

    https://creativecommons.org/ns.

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Acknowledgments

This work has been supported by a Predoctoral grant from the I+D+i program of the Universidad Politécnica de Madrid and the Spanish project DATOS 4.0: RETOS Y SOLUCIONES (TIN2016-78011-C4-4-R). Authors would like to thank Yann Le Franc for his clarifications and explanations about the FAIRsFAIR recommendations content and their development process and OEG, especially Victor Rodríguez Doncel, for all the valuable comments.

Author information

Authors and Affiliations

  1. Ontology Engineering Group, Universidad Politécnica de Madrid, Madrid, Spain

    María Poveda-Villalón, Paola Espinoza-Arias & Oscar Corcho

  2. Information Sciences Institute, University of Southern California, Los Angeles, USA

    Daniel Garijo

Authors
  1. María Poveda-Villalón
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  2. Paola Espinoza-Arias
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  3. Daniel Garijo
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  4. Oscar Corcho
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Corresponding authors

Correspondence to María Poveda-Villalón or Paola Espinoza-Arias .

Editor information

Editors and Affiliations

  1. University of Cape Town, Cape Town, South Africa

    C. Maria Keet

  2. Institute of Data Science, Maastricht University, Maastricht, The Netherlands

    Michel Dumontier

Annex: FAIR Principles

Annex: FAIR Principles

The list of FAIR guiding principles defined in [35] is:

  • To be Findable

    • F1. (meta)data are assigned a globally unique and persistent identifier

    • F2. data are described with rich metadata (defined by R1 below)

    • F3. metadata clearly and explicitly include the identifier of the data it describes

    • F4. (meta)data are registered or indexed in a searchable resource

  • To be Accesible

    • A1. (meta)data are retrievable by their identifier using a standardized communications protocol

    • A1.1 the protocol is open, free, and universally implementable

    • A1.2 the protocol allows for an authentication and authorization procedure, where necessary

    • A2. metadata are accessible, even when the data are no longer available

  • To be Interoperable

    • I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.

    • I2. (meta)data use vocabularies that follow FAIR principles

    • I3. (meta)data include qualified references to other (meta)data

  • To be Reusable

    • R1. meta(data) are richly described with a plurality of accurate and relevant attributes

    • R1.1. (meta)data are released with a clear and accessible data usage license

    • R1.2. (meta)data are associated with detailed provenance

    • R1.3. (meta)data meet domain-relevant community standards

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Poveda-Villalón, M., Espinoza-Arias, P., Garijo, D., Corcho, O. (2020). Coming to Terms with FAIR Ontologies. In: Keet, C.M., Dumontier, M. (eds) Knowledge Engineering and Knowledge Management. EKAW 2020. Lecture Notes in Computer Science(), vol 12387. Springer, Cham. https://doi.org/10.1007/978-3-030-61244-3_18

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  • DOI: https://doi.org/10.1007/978-3-030-61244-3_18

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