Abstract
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.
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Dentler, K., Cornet, R. (2013). Redundant Elements in SNOMED CT Concept Definitions. In: Peek, N., Marín Morales, R., Peleg, M. (eds) Artificial Intelligence in Medicine. AIME 2013. Lecture Notes in Computer Science(), vol 7885. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38326-7_29
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DOI: https://doi.org/10.1007/978-3-642-38326-7_29
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