Measuring Semantic Similarity Between Biomedical Concepts Within Multiple Ontologies

Most of the intelligent knowledge-based applications contain components for measuring semantic similarity between terms. Many of the existing semantic similarity measures that use ontology structure as their primary source cannot measure semantic similarity between terms and concepts using multiple ontologies. This research explores a new way to measure semantic similarity between biomedical concepts using multiple ontologies. We propose a new ontology-structure-based technique for measuring semantic similarity in single ontology and across multiple ontologies in the biomedical domain within the framework of unified medical language system (UMLS). The proposed measure is based on three features: 1) cross-modified path length between two concepts; 2) a new feature of common specificity of concepts in the ontology; and 3) local granularity of ontology clusters. The proposed technique was evaluated relative to human similarity scores and compared with other existing measures using two terminologies within UMLS framework: medical subject headings and systemized nomenclature of medicine clinical term. The experimental results validate the efficiency of the proposed technique in single and multiple ontologies, and demonstrate that our proposed measure achieves the best results of correlation with human scores in all experiments.