Towards semantic interoperability: finding and repairing hidden contradictions in biomedical ontologies
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RESEARCH
Towards semantic interoperability: finding and repairing hidden contradictions in biomedical ontologies Luke T. Slater1,2* , Georgios V. Gkoutos1,2,3,4,5,6 and Robert Hoehndorf7
Abstract Background: Ontologies are widely used throughout the biomedical domain. These ontologies formally represent the classes and relations assumed to exist within a domain. As scientific domains are deeply interlinked, so too are their representations. While individual ontologies can be tested for consistency and coherency using automated reasoning methods, systematically combining ontologies of multiple domains together may reveal previously hidden contradictions. Methods: We developed a method that tests for hidden unsatisfiabilities in an ontology that arise when combined with other ontologies. For this purpose, we combined sets of ontologies and use automated reasoning to determine whether unsatisfiable classes are present. In addition, we designed and implemented a novel algorithm that can determine justifications for contradictions across extremely large and complicated ontologies, and use these justifications to semi-automatically repair ontologies by identifying a small set of axioms that, when removed, result in a consistent and coherent set of ontologies. Results: We tested the mutual consistency of the OBO Foundry and the OBO ontologies and find that the combined OBO Foundry gives rise to at least 636 unsatisfiable classes, while the OBO ontologies give rise to more than 300,000 unsatisfiable classes. We also applied our semi-automatic repair algorithm to each combination of OBO ontologies that resulted in unsatisfiable classes, finding that only 117 axioms could be removed to account for all cases of unsatisfiability across all OBO ontologies. Conclusions: We identified a large set of hidden unsatisfiability across a broad range of biomedical ontologies, and we find that this large set of unsatisfiable classes is the result of a relatively small amount of axiomatic disagreements. Our results show that hidden unsatisfiability is a serious problem in ontology interoperability; however, our results also provide a way towards more consistent ontologies by addressing the issues we identified. Keywords: Ontology interoperability, Automated reasoning
*Correspondence: [email protected] 1 College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UK Full list of author information is available at the end of the article
Background Ontologies are used to describe and organise domain knowledge in the biomedical sciences. Ontologies use classes to characterise the kinds of things that exist within a domain as well as axioms that provide constraints for these classes and conditions that must be satisfied within the domain. Most ontologies in biology are domain-specific and focus on a single domain. Creating ontologies that reference and extend other biomedical ontologies is
© The Author(s) 2020. Open Access This article is licensed un
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