Information content based semantic similarity approaches for multiple biomedical ontologies

Abstract

Semantic similarity mechanism is mandatory in information retrieval, information integration, ontology mapping and psycholinguistics. The objective of this work is to develop a computational approach which assesses semantic similarity among concepts from different and independent ontologies without constructing apriori a shared ontology. The proposed approach is based on Tversky similarity model and is mapped to information theoretic domain. This paper also explores the possibility of adapting the existing single ontology information content based approaches and propose methods for assessing semantic similarity among concepts from different multiple ontologies. The proposed approaches are corpus independent and they correlate well with the human judgements. The proposed approaches have been experimented with two biomedical ontologies: SNOMED-CT (Systemized nomenclature of medical clinical terms) and Mesh (Medical subject headings) and the results are reported. The proposed four approaches outperform the path length based computational method as it achieves the highest correlation. © 2011 Springer-Verlag.