NDLI: Comparison of Semantic Similarity Measures for Application Specific Ontology Pruning

Content Provider	IEEE Xplore Digital Library
Author	Wei-Nchih Lee Bridewell, W. Das, A.K.
Copyright Year	2011
Abstract	Comparing the effects of one drug to another drug, based on their similarity, is important in clinical research. Ontology-derived measures of drug-drug similarity may help to automate such analyses on large data sets. However, general drug ontologies can contain hierarchical distinctions that are irrelevant to a particular clinical application and thus may lead to inaccurate semantic similarity measures. We propose that ontology pruning be used to remove unneeded concepts so that the resulting ontology better reflects the semantic distinctions of a particular domain. In this paper, we present a novel pruning strategy for drug ontologies. For three clinical domains, we derive previously developed semantic similarity measures for the automatically pruned ontology and the full drug ontology against those for the expert derived ontology. We show that the values of similarity measures based on our pruned approach are closer to those of the expert derived ontology than to those of the full ontology. Our pruning approach thus provides a standardized domain-specific measure of drug-drug similarity for clinical applications.
Starting Page	97
Ending Page	103
File Size	823425
Page Count	7
File Format	PDF
ISBN	9781457703256
DOI	10.1109/HISB.2011.17
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Drugs Hypertension Heart Ontology pruning Correlation Semantics Ontologies Clinical informatics Semantic similarity Biomedical measurements
Content Type	Text
Resource Type	Article

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