A Hybrid Approach for Measuring Semantic Similarity between Ontologies Based on WordNet

Ontology is a conceptual model, which is used on data exchange between heterogeneous data sources in semantic web, and liked by many more people. Because of the shortage of the uniform standards for constructing ontology, it brings in lots of problems of ontology heterogeneity. Ontology mapping aims at these problems, and semantic similarity between ontologies is the key part of ontology mapping. In this paper we propose a hybrid approach for measuring semantic similarity between ontologies based on WordNet, denoted by WNOntoSim. WordNet is used to calculate semantic similarity between ontologies in elemental level. We compute semantic similarity between ontologies in structural level by constructing contexts of node where the structure of ontology is encoded, and combine these scores to obtain a comprehensive semantic similarity between ontologies. Experimental results on test dataset of competition on ontology matching provided by 3rd ISWC show WNOntoSim gives a better performance and improves the Average F-Measure, comparing against some state of the art related methods. Especially, it displays more competitive in general ontology.