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| Ontology Mapping Method Based on Ontology Partition |
| LI Zhi-Ming1, LI Shan-Ping1, YANG Chao-Hui1, LIN Xin2 |
College of Computer Science and Technology, Zhejiang University, Hangzhou 310027
Department of Computer Science and Technology, East China Normal University, Shanghai 200241 |
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Abstract The mapping efficiency is key to the performance of dynamic ontology mapping in semantic web service discovery, context-awareness in smart spaces and so on. The existing methods simplify the current methods of similarity computation to promotes the efficiency, nevertheless they fail in the case that the number of candidate mapping entity pairs increases when ontology gets larger. An efficient ontology mapping method based on ontology partition is proposed, which divides an ontology into a set of blocks through bottom-up clustering. Then, the blocks are mapped and candidate mapping entity pairs are selected from the block mapping result. The experimental results show that the proposed method promotes the efficiency of mapping significantly with 6 times faster than it of Falcon-AO.
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Received: 17 August 2009
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[1] Shvaiko P, Euzenat J. A Survey of Schema-Based Matching Approaches. Journal on Data Semantics, 2005, 4: 146-171
[2] Ehrig M, Staab S. QOM-Quick Ontology Mapping // Proc of the 3rd International Semantic Web Conference. Hiroshima, Japan, 2004: 683-696
[3] Pradeep W C, Cohen W W, Ravikumar P, et al. A Comparison of String Metrics for Matching Names and Records // Proc of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington, USA, 2003: 73-78
[4] Mork P, Bernstein P A. Adapting a Generic Match Algorithm to Align Ontologies of Human Anatomy // Proc of the 20th International Conference on Data Engineering. Boston, USA, 2004: 787-790
[5] Stuckenschmidt H, Klein M. Structure-Based Partitioning of Large Class Hierarchies // Proc of the 3rd International Semantic Web Conference. Hiroshima, Japan, 2004: 289-303
[6] Grau B C, Horrocks I, Kazakov Y, et al. Just the Right Amount: Extracting Modules from Ontologies // Proc of the 16th International Conference on World Wide Web. Banff, Canada, 2007: 717-726
[7] MacCartney B, McIlraith S, Amir E, et al. Practical Partition-Based Theorem Proving for Large Knowledge Bases // Proc of the 18th International Conference on Artificial Intelligence. Acapulco, Mexico, 2003: 89-96
[8] Do Honghai, Rahm E. Matching Large Schemas: Approaches and Evaluation. Information Systems, 2007, 32(6): 857-885
[9] Hu Wei, Qu Yuzhong. Matching Large Ontologies: A Divide-and-Conquer Approach. Data Knowledge Engineering, 2008, 67(1): 140-160
[10] Tu Kewei, Xiong Miao, Zhang Lei, et al. Towards Imaging Large-Scale Ontologies for Quick Understanding and Analysis // Proc of the 4th International Semantic Web Conference. Galway, Ireland, 2005: 702-715
[11] Wu Zhibiao, Palmer M. Verbs Semantics and Lexical Selection // Proc of the 32nd Annual Meating on Association for Computational Linguistics. Las Cruces, USA, 1994: 133-138
[12] Rodriguez M A, Egenhofer M J, Rugg R D. Assessing Semantic Similarities among Geospatial Feature Class Definitions // Proc of the International Conference on Interoperating Geographic Information Systems. Zurich, Switzerland, 1999, Ⅱ: 189-202
[13] Sokal S. Numerical Taxonomy. San Francisco, USA: W.H. Freeman and Company, 1973
[14] Wagner R A, Fischer M J. The String-to-String Correction Problem. Journal of the ACM, 1974, 21(1): 168-173
[15] Ehrig M, Sure Y. FOAM: Framework for Ontology Alignment and Mapping, Results of the Ontology Alignment Initiative // Proc of the Workshop on Integrating Ontologies. Banff, Canada, 2005: 72-76
[16] Hu Wei, Qu Yuzhong. Falcon-AO: A Practical Ontology Matching System. Journal of Web Semantics, 2008, 6(3): 237-239 |
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2011, Vol. 24 
