Using pattern detection techniques and refactoring to improve the performance of ASMOV

Abstract

One of the most important challenges in semantic Web is ontology matching. Ontology matching is a technology that enables semantic interoperability between structurally and semantically heterogeneous resources on the Web. Despite serious research efforts on ontology matching, matchers still suffers from severe problems with respect to the quality of matching results. Furthermore, Most of them take a lot of time for finding the correspondences. The aim of this paper is improving ontology matching results by adding the preprocessing phase for analyzing the input ontologies. This phase is added in order to solve problems caused by ontology diversity. We select one of the best matchers of Ontology Alignment Evaluation Initiative (OAEI) which is Automated Semantic Matching of Ontologies with Verification, called ASMOV. In preprocessing phase, some new patterns of ontologies are detected and then refactoring operations are used for reaching assimilated ontologies. Afterward, we applied ASMOV for testing our approach on both the original ontologies and their refactored counterparts. Experimental results show that these refactored ontologies are more efficient than the original unrepaired ones with respect to the standard evaluation measures i.e. Precision, Recall, and F-Measure.