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COHeRE: Cross-Ontology Hierarchical Relation Examination for Ontology Quality Assurance.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Extending import detection algorithms for concept import from two to three biomedical terminologies.BMC medical informatics and decision making · 2020Article
- A transformation-based method for auditing the IS-A hierarchy of biomedical terminologies in the Unified Medical Language System.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- A review of auditing techniques for the Unified Medical Language System.Journal of the American Medical Informatics Association : JAMIA · 2020Review
- Assessing the practice of biomedical ontology evaluation: Gaps and opportunities.Journal of biomedical informatics · 2018Review
- FEDRR: fast, exhaustive detection of redundant hierarchical relations for quality improvement of large biomedical ontologies.BioData mining · 2016Article
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1 author.
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Abstract
Biomedical ontologies play a vital role in healthcare information management, data integration, and decision support. Ontology quality assurance (OQA) is an indispensable part of the ontology engineering cycle. Most existing OQA methods are based on the knowledge provided within the targeted ontology. This paper proposes a novel cross-ontology analysis method, Cross-Ontology Hierarchical Relation Examination (COHeRE), to detect inconsistencies and possible errors in hierarchical relations across multiple ontologies. COHeRE leverages the Unified Medical Language System (UMLS) knowledge source and the MapReduce cloud computing technique for systematic, large-scale ontology quality assurance work. COHeRE consists of three main steps with the UMLS concepts and relations as the input. First, the relations claimed in source vocabularies are filtered and aggregated for each pair of concepts. Second, inconsistent relations are detected if a concept pair is related by different types of relations in different source vocabularies. Finally, the uncovered inconsistent relations are voted according to their number of occurrences across different source vocabularies. The voting result together with the inconsistent relations serve as the output of COHeRE for possible ontological change. The highest votes provide initial suggestion on how such inconsistencies might be fixed. In UMLS, 138,987 concept pairs were found to have inconsistent relationships across multiple source vocabularies. 40 inconsistent concept pairs involving hierarchical relationships were randomly selected and manually reviewed by a human expert. 95.8% of the inconsistent relations involved in these concept pairs indeed exist in their source vocabularies rather than being introduced by mistake in the UMLS integration process. 73.7% of the concept pairs with suggested relationship were agreed by the human expert. The effectiveness of COHeRE indicates that UMLS provides a promising environment to enhance qualities of biomedical ontologies by performing cross-ontology examination.
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26958178PMC4765676What OpenQuestion holds
Registered trials
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