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

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.

Semantic Modeling, Development and Evaluation for the Resistance Spot Welding Industry

“…FAIR principles are a set of guidelines [43] that facilitate to build a coherent and machine-friendly data environment. [17] developed O'FAIRe 7 methodology to encourage ontologies, vocabularies and semantic artifacts compliance with the FAIR guiding principles. It includes 15 foundational FAIR principles for ontologies and is harmonized with state-of-theart FAIRness assessment initiatives.…”

“…production and existing ontologies. The RSWO is assessed in compliance with the FAIR guiding principles using O'FAIRe methodology [17]. The RSWO structural and functional, and quality and richness dimensions are assessed using OOPS and OntoMetrics, respectively.…”