A systematic analysis of term reuse and term overlap across biomedical ontologies

Article type: Research Article

Authors: Kamdar, Maulik R.^{; *} | Tudorache, Tania | Musen, Mark A.

Affiliations: Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, USA. E-mails: tudorache@stanford.edu, musen@stanford.edu

Correspondence: [*] Corresponding author. E-mail: maulikrk@stanford.edu.

Abstract: Reusing ontologies and their terms is a principle and best practice that most ontology development methodologies strongly encourage. Reuse comes with the promise to support the semantic interoperability and to reduce engineering costs. In this paper, we present a descriptive study of the current extent of term reuse and overlap among biomedical ontologies. We use the corpus of biomedical ontologies stored in the BioPortal repository, and analyze different types of reuse and overlap constructs. While we find an approximate term overlap between 25–31%, the term reuse is only <9%, with most ontologies reusing fewer than 5% of their terms from a small set of popular ontologies. Clustering analysis shows that the terms reused by a common set of ontologies have >90% semantic similarity, hinting that ontology developers tend to reuse terms that are sibling or parent–child nodes. We validate this finding by analyzing the logs generated from a Protégé plugin that enables developers to reuse terms from BioPortal. We find most reuse constructs were 2-level subtrees on the higher levels of the class hierarchy. We developed a Web application that visualizes reuse dependencies and overlap among ontologies, and that proposes similar terms from BioPortal for a term of interest. We also identified a set of error patterns that indicate that ontology developers did intend to reuse terms from other ontologies, but that they were using different and sometimes incorrect representations. Our results stipulate the need for semi-automated tools that augment term reuse in the ontology engineering process through personalized recommendations.

Keywords: Descriptive study, ontologies, biomedical domain, term reuse, term overlap, composite mappings, visualization

DOI: 10.3233/SW-160238

Journal: Semantic Web, vol. 8, no. 6, pp. 853-871, 2017