A Macrocommittees Method of Combining Multistrategy Classifiers for Heterogeneous Ontology Matching

Abstract

To resolve the problem of ontology heterogeneity, we apply 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. When using multistrategy learning approach, a central problem is the combination of multiple match results. We find that the goal and the conditions of using multistrategy classifiers within ontology matching are different from the ones for general text classification. We propose a macrocommittees combination method that uses multistrategy in matching phase but not classification phase. In this paper we describe the combination rule called Best Outstanding Champion, which is suitable for heterogeneous ontology mapping. On the prediction results of individual methods, our method can well accumulate the correct matching of alone classifier.

Research described in this paper is supported by Major International Cooperation Program of NSFC Grant 60221120145 and by Science & Technology Committee of Shanghai Municipality Key Project Grant 02DJ14045.