{"title":"Identifying Missing Hierarchical Relations in SNOMED CT from Logical Definitions Based on the Lexical Features of Concept Names.","authors":"Olivier Bodenreider","doi":"","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>To identify missing hierarchical relations in SNOMED CT from logical definitions based on the lexical features of concept names.</p><p><strong>Methods: </strong>We first create logical definitions from the lexical features of concept names, which we represent in OWL EL. We infer hierarchical (<i>subClassOf</i>) relations among these concepts using the ELK reasoner. Finally, we compare the hierarchy obtained from lexical features to the original SNOMED CT hierarchy. We review the differences manually for evaluation purposes.</p><p><strong>Results: </strong>Applied to 15,833 disorder and procedure concepts, our approach identified 559 potentially missing hierarchical relations, of which 78% were deemed valid.</p><p><strong>Conclusions: </strong>This lexical approach to quality assurance is easy to implement, efficient and scalable.</p>","PeriodicalId":72554,"journal":{"name":"CEUR workshop proceedings","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9584353/pdf/nihms-1840462.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"CEUR workshop proceedings","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Objectives: To identify missing hierarchical relations in SNOMED CT from logical definitions based on the lexical features of concept names.
Methods: We first create logical definitions from the lexical features of concept names, which we represent in OWL EL. We infer hierarchical (subClassOf) relations among these concepts using the ELK reasoner. Finally, we compare the hierarchy obtained from lexical features to the original SNOMED CT hierarchy. We review the differences manually for evaluation purposes.
Results: Applied to 15,833 disorder and procedure concepts, our approach identified 559 potentially missing hierarchical relations, of which 78% were deemed valid.
Conclusions: This lexical approach to quality assurance is easy to implement, efficient and scalable.
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