Ontological Representation of FAIR Principles: A Blueprint for FAIRer Data Sources

Anna Bernasconi, Alberto García Simon*, Giancarlo Guizzardi, Luiz Olavo Bonino da Silva Santos, Veda C. Storey

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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Abstract

Guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of datasets, known as FAIR principles, were introduced in 2016 to enable machines to perform automatic actions on a variety of digital objects, including datasets. Since then, the principles have been widely adopted by data creators and users worldwide with the ‘FAIR’ acronym becoming a common part of the vocabulary of data scientists. However, there is still some controversy on how datasets should be interpreted since not all datasets that are claimed to be FAIR, necessarily follow the principles. In this research, we propose the OntoUML FAIR Principles Schema, as an ontological representation of FAIR principles for data practitioners. The work is based on OntoUML, an ontologically well-founded language for Ontology-driven Conceptual Modeling. OntoUML is a proxy for ontological analysis that has proven effective in supporting the explanation of complex domains. Our schema aims to disentangle the intricacies of the FAIR principles’ definition, by resolving aspects that are ambiguous, under-specified, recursively-specified, or implicit. The schema can be considered as a blueprint, or a template to follow when the FAIR classification strategy of a dataset must be designed. To demonstrate the usefulness of the schema, we present a practical example based on genomic data and discuss how the results provided by the OntoUML FAIR Principles Schema contribute to existing data guidelines.

Original languageEnglish
Title of host publicationAdvanced Information Systems Engineering
Subtitle of host publication35th International Conference, CAiSE 2023, Zaragoza, Spain, June 12–16, 2023, Proceedings
EditorsMarta Indulska, Iris Reinhartz-Berger, Carlos Cetina, Oscar Pastor
Place of PublicationCham
PublisherSpringer
Pages261-277
Number of pages17
ISBN (Electronic)978-3-031-34560-9
ISBN (Print)978-3-031-34559-3
DOIs
Publication statusPublished - 2023
Event35th International Conference on Advanced Information Systems Engineering, CAiSE 2023 - Zaragoza, Spain
Duration: 12 Jun 202316 Jun 2023
Conference number: 35

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume13901 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference35th International Conference on Advanced Information Systems Engineering, CAiSE 2023
Abbreviated titleCAiSE
Country/TerritorySpain
CityZaragoza
Period12/06/2316/06/23

Keywords

  • FAIR data
  • FAIRness guidance
  • Ontological modeling language
  • OntoUML
  • OntoUML FAIR Principles Schema
  • 2023 OA procedure

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  • Towards Semantics for Abstractions in Ontology-Driven Conceptual Modeling

    Romanenko, E., Kutz, O., Calvanese, D. & Guizzardi, G., 26 Oct 2023, Advances in Conceptual Modeling - ER 2023 Workshops, CMLS, CMOMM4FAIR, EmpER, JUSMOD, OntoCom, QUAMES, and SmartFood, Lisbon, Portugal, November 6-9, 2023, Proceedings. Sales, T. P., Araújo, J., Borbinha, J. & Guizzardi, G. (eds.). Switzerland: Springer, Vol. 14319. p. 199-209 11 p. (Lecture Notes in Computer Science; vol. 14319).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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