ArticleBMC medical informatics and decision making2020
Towards semantic interoperability: finding and repairing hidden contradictions in biomedical ontologies.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Bayesian-knowledge driven ontologies: A framework for fusion of semantic knowledge under uncertainty and incompleteness.PloS one · 2024Article
- The Ontology of Biological Attributes (OBA)-computational traits for the life sciences.Mammalian genome : official journal of the International Mammalian Genome Society · 2023Article
- The Ontology of Biological Attributes (OBA) - Computational Traits for the Life Sciences.bioRxiv : the preprint server for biology · 2023Article
- Normal Table of Xenopus development: a new graphical resource.Development (Cambridge, England) · 2022Article
- Knowledge Representation and Management: Interest in New Solutions for Ontology Curation.Yearbook of medical informatics · 2021Review
- Challenges for FAIR-compliant description and comparison of crop phenotype data with standardized controlled vocabularies.Database : the journal of biological databases and curation · 2021Article
- OnTARi: an ontology for factors influencing therapy adherence to rehabilitation.BMC medical informatics and decision making · 2021Article
- Quality assurance and enrichment of biological and biomedical ontologies and terminologies.BMC medical informatics and decision making · 2020Article
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Authors and funding
3 authors.
Funding
Abstract
backgroundOntologies 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.
methodsWe 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.
resultsWe 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.
conclusionsWe 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.