Ontology Matching using multiple similarity measures

This paper presents an automatic ontology matching approach (called LSSOM - Lexical Structural Semantic-based Ontology Matching method) which brings a final alignment by combining three kinds of different similarity measures: lexical-based, structure-based, and semantic-based techniques as well as using information in ontologies including names, labels, comments, relations and positions of concepts in the hierarchy and integrating WordNet dictionary. Firstly, two ontologies are matched sequentially by using the lexical-based and structure-based similarity measures to find structural correspondences among the concepts. Secondly, the semantic similarity based on WordNet dictionary is applied to these concepts in given ontologies. After the semantic and structural similarities are obtained, they are combined in the parallel phase by using weighted sum method to yield the final similarities. Our system is implemented and evaluated based on the OAEI 2008 benchmark dataset. The experimental results show that our approach obtains good F-measure values and outperforms other automatic ontology matching systems which do not use instances information.