SIMON: A multi-strategy classification approach resolving ontology heterogeneity on the semantic Web

One key idea of semantic web is that the content of the web is usable to machines (i.e. software agents). On the semantic web, data interoperability and ontology heterogeneity between agents are becoming ever more important issues. This paper presents a multi-strategy learning approach to resolve these problems. In this paper we describe the SIMON (Semantic Interoperation by Matching between ONtologies) system, which applies multiple classification methods to learn the matching between ontologies. We use the general statistic classification method to discover category features in data instances and use the first-order learning algorithm FOIL to exploit the semantic relations among data instances. On the prediction results of individual methods, the system combines their outcomes using our matching committee rule called the Best Outstanding Champion. The experiments show that SIMON system achieves high accuracy on real-world domain.