Comparison of Semantic Similarity Measures for Application Specific Ontology Pruning


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.

Proceedings of the First IEEE Conference on Healthcare Informatics, Imaging, and Systems Biology