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Missing lateral relationships in top-level concepts of an ontology.
Zheng, Ling; Chen, Yan; Min, Hua; Hildebrand, P Lloyd; Liu, Hao; Halper, Michael; Geller, James; de Coronado, Sherri; Perl, Yehoshua.
Afiliação
  • Zheng L; Computer Science and Software Engineering Department, Monmouth University, West Long Branch, NJ, 07764, USA. lzheng@monmouth.edu.
  • Chen Y; CIS Department, Borough of Manhattan Community College, CUNY, New York, NY, 10007, USA.
  • Min H; Department of Health Administration and Policy, George Mason University, Fairfax, VA, 22030, USA.
  • Hildebrand PL; Union Square Eye Care, New York, NY, 10003, USA.
  • Liu H; Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
  • Halper M; Department of Informatics, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
  • Geller J; Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
  • de Coronado S; National Cancer Institute, Center for Biomedical Informatics and Information Technology, National Institutes of Health, Rockville, MD, 20850, USA.
  • Perl Y; Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
BMC Med Inform Decis Mak ; 20(Suppl 10): 305, 2020 12 15.
Article em En | MEDLINE | ID: mdl-33319709
BACKGROUND: Ontologies house various kinds of domain knowledge in formal structures, primarily in the form of concepts and the associative relationships between them. Ontologies have become integral components of many health information processing environments. Hence, quality assurance of the conceptual content of any ontology is critical. Relationships are foundational to the definition of concepts. Missing relationship errors (i.e., unintended omissions of important definitional relationships) can have a deleterious effect on the quality of an ontology. An abstraction network is a structure that overlays an ontology and provides an alternate, summarization view of its contents. One kind of abstraction network is called an area taxonomy, and a variation of it is called a subtaxonomy. A methodology based on these taxonomies for more readily finding missing relationship errors is explored. METHODS: The area taxonomy and the subtaxonomy are deployed to help reveal concepts that have a high likelihood of exhibiting missing relationship errors. A specific top-level grouping unit found within the area taxonomy and subtaxonomy, when deemed to be anomalous, is used as an indicator that missing relationship errors are likely to be found among certain concepts. Two hypotheses pertaining to the effectiveness of our Quality Assurance approach are studied. RESULTS: Our Quality Assurance methodology was applied to the Biological Process hierarchy of the National Cancer Institute thesaurus (NCIt) and SNOMED CT's Eye/vision finding subhierarchy within its Clinical finding hierarchy. Many missing relationship errors were discovered and confirmed in our analysis. For both test-bed hierarchies, our Quality Assurance methodology yielded a statistically significantly higher number of concepts with missing relationship errors in comparison to a control sample of concepts. Two hypotheses are confirmed by these findings. CONCLUSIONS: Quality assurance is a critical part of an ontology's lifecycle, and automated or semi-automated tools for supporting this process are invaluable. We introduced a Quality Assurance methodology targeted at missing relationship errors. Its successful application to the NCIt's Biological Process hierarchy and SNOMED CT's Eye/vision finding subhierarchy indicates that it can be a useful addition to the arsenal of tools available to ontology maintenance personnel.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Vocabulário Controlado / Systematized Nomenclature of Medicine Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: BMC Med Inform Decis Mak Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Vocabulário Controlado / Systematized Nomenclature of Medicine Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: BMC Med Inform Decis Mak Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Estados Unidos