A Particle Swarm-Based Approach for Semantic Similarity Computation

Semantic similarity plays a vital role within a myriad of shared data applications, such as data and information integration. A first step towards building such applications is to determine concepts, which are semantically similar to each other. One way to compute this similarity of two concepts is to assess their word similarity by exploiting different knowledge sources, e.g., ontologies, thesauri, domain corpora, etc. Over the last few years, several approaches to similarity assessment based on quantifying information content of concepts have been proposed and have shown encouraging performance. For all these approaches, the Least Common Subsumer (LCS) of two concepts plays an important role in determining their similarity. In this paper, we investigate the influence the choice of this node (or a set of nodes) on the quality of the similarity assessment. In particular, we develop a particle swarm optimization approach that optimally discovers LCSs. An empirical evaluation, based on well-established biomedical benchmarks and ontologies, illustrates the accuracy of the proposed approach, and demonstrates that similarity estimations provided by our approach are significantly more correlated with human ratings of similarity than those obtained via related works.