Ontology Selection for Semantic Similarity Assessment

Abstract

The assessment of the semantic similarity between concepts is a key tool to improve the understanding of text. The structured knowledge that ontologies provide has been extensively used to estimate similarities with encouraging results. However, in many domains, several ontologies modelling the same concepts in different ways are available. In such scenarios, the most suitable ontology for similarity calculation should be selected. In this paper we tackle this task by proposing an unsupervised method to select the ontology that seems to enable the most accurate similarity assessments. By studying the ontology features that most influence the similarity accuracy, we propose a score that captures them in a mathematically coherent way. Then, the most suitable ontology can be selected as that with the highest score. We also report the results of the proposed method for several well-known ontologies and a widely-used semantic similarity benchmark.