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 of the authors.

DOI: 10.61416/ceai.v25i2.8353