Applying Semantic Similarity Measures Based on Information Content in the Evaluation of a Domain Ontology

Semantic similarity is a metric used to know the similarity degree of two concepts in an ontology or a taxonomy. Semantic similarity has a wide variety applications on artificial intelligence, natural language processing, biomedical informatics, geoinformatics and semantic web and is usually applied on machine translation and word-sense disambiguation. In this research, semantic similarity measures are used to evaluate taxonomic relationships in a domain ontology. This evaluation was carried out by using a proposed algorithm and through the accuracy measure. The semantic similarity measures implemented are based on information content and were proposed by the following authors: Resnik, Lin, Jiang & Conrath and Mazandu & Mulder. Mainly, this research contributes to the automatic evaluation of ontologies in the task of evaluating the ontology taxonomy. The experimental results show that the measures have at least 88% accuracy. In addition, the system has an accuracy of 94% compared to validation responses from an expert.