Article: O'FAIRe makes you an offer: metadata-based automatic FAIRness assessment for ontologies and semantic resources Journal: International Journal of Metadata, Semantics and Ontologies (IJMSO) 2022 Vol.16 No.1 pp.16 - 46 Abstract: We have not yet seen a clear methodology implemented and tooled to automatically assess the level of FAIRness of semantic resources. We propose a metadata-based automatic FAIRness assessment methodology for ontologies and semantic resources called Ontology FAIRness Evaluator (O'FAIRe). It is based on the projection of the 15 foundational FAIR principles for ontologies, and it is aligned and nourished with relevant state-of-the-art initiatives for FAIRness assessment. We propose 61 questions of which 80% are based on the resource metadata descriptions and we review the standard metadata properties (taken from the MOD 1.4 ontology metadata model) that could be used to implement these metadata. We also demonstrate the importance of relying on ontology libraries or repositories to harmonise and harness unified metadata and thus allow FAIRness assessment. Moreover, we have implemented O'FAIRe in the AgroPortal semantic resource repository and produced a preliminary FAIRness analysis over 149 semantic resources in the agri-food/environment domain. Inderscience Publishers - linking academia, business and industry through research

Title: O'FAIRe makes you an offer: metadata-based automatic FAIRness assessment for ontologies and semantic resources

Authors: Emna Amdouni; Syphax Bouazzouni; Clement Jonquet

Addresses: LIRMM, University of Montpellier & CNRS, Montpellier, France ' LIRMM, University of Montpellier & CNRS, Montpellier, France ' LIRMM, University of Montpellier & CNRS, Montpellier, France; MISTEA, University of Montpellier, INRAE & Institut Agro, Montpellier, France

Abstract: We have not yet seen a clear methodology implemented and tooled to automatically assess the level of FAIRness of semantic resources. We propose a metadata-based automatic FAIRness assessment methodology for ontologies and semantic resources called Ontology FAIRness Evaluator (O'FAIRe). It is based on the projection of the 15 foundational FAIR principles for ontologies, and it is aligned and nourished with relevant state-of-the-art initiatives for FAIRness assessment. We propose 61 questions of which 80% are based on the resource metadata descriptions and we review the standard metadata properties (taken from the MOD 1.4 ontology metadata model) that could be used to implement these metadata. We also demonstrate the importance of relying on ontology libraries or repositories to harmonise and harness unified metadata and thus allow FAIRness assessment. Moreover, we have implemented O'FAIRe in the AgroPortal semantic resource repository and produced a preliminary FAIRness analysis over 149 semantic resources in the agri-food/environment domain.

Keywords: automatic FAIR assessment; semantic web; ontologies; standardised metadata and open repositories.

DOI: 10.1504/IJMSO.2022.131133

International Journal of Metadata, Semantics and Ontologies, 2022 Vol.16 No.1, pp.16 - 46

Received: 28 Jun 2022
Accepted: 12 Sep 2022
Published online: 31 May 2023 *

Title: O'FAIRe makes you an offer: metadata-based automatic FAIRness assessment for ontologies and semantic resources

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