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Redundant Elements in SNOMED CT Concept Definitions
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
Dept. of Computer Science, VU University Amsterdam, The Netherlands and Dept. of Medical Informatics, Academic Medical Center, University of Amsterdam, The Netherlands.
2013 (English)In: proceedings of AIME 2013, Lecture Notes in ComputerScience 2013, Vol. 7885, Springer , 2013, 186-195 p.Conference paper, Published paper (Refereed)
Abstract [en]

While redundant elements in SNOMED CT concept definitions are harmless from a logical point of view, they unnecessarily make concept definitions of typically large ontologies such as SNOMED CT hard to construct and to maintain. In this paper, we apply a fully automated method to detect intra-axiom redundancies in SNOMED CT. We systematically analyse the completeness and soundness of the results of our method by examining the identified redundant elements. In absence of a gold standard, we check whether our method identifies concepts that are likely to contain redundant elements because they become equivalent to their stated subsumer when they are replaced by a fully defined concept with the same definition. To evaluate soundness, we remove all identified redundancies, and test whether the logical closure is preserved by comparing the concept hierarchy to the one of the official SNOMED CT distribution. We found that 35,010 of the 296,433 SNOMED CT concepts (12%) contain redundant elements in their definitions, and that the results of our method are sound and complete with respect to our partial evaluation. We recommend to free the stated form from these redundancies. In future, knowledge modellers should be supported by being pointed to newly introduced redundancies.

Place, publisher, year, edition, pages
Springer , 2013. 186-195 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 7885
National Category
Other Medical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-100440DOI: 10.1007/978-3-642-38326-7_29OAI: oai:DiVA.org:liu-100440DiVA: diva2:662580
Conference
14th Conference on Artificial Intelligence in Medicine, AIME 2013, Murcia, Spain, May 29-June 1, 2013
Available from: 2013-11-07 Created: 2013-11-07 Last updated: 2014-05-13

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Cornet, Ronald

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
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  • en-US
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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