Semantic Similarity in a Taxonomy by Evaluating the Relatedness of Concept Senses with the Linked Data Semantic Distance

Abstract

This paper addresses the information-theoretic definition of semantic similarity based on the notion of information content, and presents an evolution of a novel approach for evaluating semantic similarity in a taxonomy. Such an approach takes into account not only the generic sense of a concept but also its intended sense in a given context. In particular, a method for computing the semantic relatedness of concepts in RDF knowledge graphs is used for evaluating the relevance of the intended sense of a concept with respect to its generic sense. The experiment of this work shows that the relatedness method based on triple patterns adopted in this paper leads to higher correlation values with human judgment with respect to the ones obtained according to the original proposal that is based on a triple weights relatedness measure.