Linked Data Semantic Distance with Global Normalization for evaluating Semantic Similarity in a Taxonomy

Abstract

In this work, the problem of evaluating semantic similarity in a taxonomy by relying on the notion of information content is investigated. In particular, a measure that takes into account not only the generic sense of a concept but also its intended sense in a given context is considered. Such a measure needs a semantic relatedness approach in order to evaluate the relatedness between the generic sense and the intended sense of a concept. In this work, we show that relying on the Linked Data Semantic Distance with Global Normalization leads to higher Spearman's correlation values with human judgment with respect to the original proposal and previous experiments of the authors.