Adaptive Similarity Aggregation Method for Ontology Matching

Ontology matching finds correspondences between similar entities of different ontologies. Two ontologies may be similar in some aspects such as structure, semantic etc. Most ontology matching systems integrate multiple matchers to extract all the similarities that two ontologies may have. Thus, we face a major problem to aggregate different similarities. Some matching systems use experimental weights for aggregation of similarities among different matchers while others use machine learning approaches and optimization algorithms to find optimal weights to assign to different matchers. However, both approaches have their own deficiencies. In this paper, we will point out the problems and shortcomings of current similarity aggregation strategies and propose a new strategy, which enables us to utilize the structural information of ontologies to get weights of matchers for the similarity aggregation task. We have tested our similarity aggregation strategy on the OAEI 2009 data set. Experimental results show a significant accuracy in several cases, especially for matching the classes of ontologies.