Results of NBJLM for OAEI 2010

This paper presents the results obtained by NBJLM (Nankai Baidu Joint Lab Matcher) for its first participation to OAEI 2010. The research of ontology-based similarity calculation among concepts has already been a hot issue. NBJLM is an hybrid ontology alignment method that considers both similarity of literal concept and semantic structure. Simultaneously, how to accelerate matching has been mentioned in this paper and the experimental results show the remarkable improvement of matching speed. In OAEI 2010, NBJLM submitted the result for one alignment task: anatomy. 1 Presentation of NBJLM In recent years, Ontology matching is mainly used in ontology integration, ontology merging, and ontology reusing. Many approaches to ontology matching have been proposed over the years, references[1][5][3] make full use of information, probability and statistics theory, however, they have limited ability to distinguish semantic differences, and the similarity calculation methods are not perfect. Besides, references[2][4][7][8] have considered various factors, but they do not take into account how to avoid unnecessary calculation to shorten computing time in mapping large-scale ontologies. NBJLM is a multiple strategy dynamic ontology matching system implemented in java. It considers both the literal concept and ontology structure that includes node depth, node density and semantic distance. 1.1 State, purpose, general statement Given two heterogeneous ontologies O1 and O2, a matching is made up of a set of correspondences between pairs of node IDs belonging to O1 and O2, respectively. NBJLM is designed to find out relations of equivalence and subsumption between entities, i.e. classes and properties, issued from two ontologies. Our approach makes use of the matching strategy that considers literal similarity measure and ontology structure similarity measure. The core contributions of NBJLM is described as followed: Firstly, it uses Hash mapping algorithm to improve efficiency of calculation. Secondly, it takes a full analysis of a number of issues to be considered in structure matching, which makes the algorithm works better, and the matching results are more accurate and efficient. As demonstrated by the experimental results, our method can greatly cut the running time, meanwhile, precise matching results can be obtained. WordNet Heterogeneous Ontologies Structure Similarity Match Results 1 1 2 2 3 3 1 1 2 3 2