A Multi-Agent Improved Semantic Similarity Matching Algorithm Based on Ontology Tree

온톨로지 트리기반 멀티에이전트 세만틱 유사도매칭 알고리즘

  • Gao, Qian (School of information, Shandong Polytechnic University) ;
  • Cho, Young-Im (College of Information Technology, University of Suwon)
  • ;
  • 조영임 (수원대학교 컴퓨터학과)
  • Received : 2012.08.27
  • Accepted : 2012.09.25
  • Published : 2012.11.01


Semantic-based information retrieval techniques understand the meanings of the concepts that users specify in their queries, but the traditional semantic matching methods based on the ontology tree have three weaknesses which may lead to many false matches, causing the falling precision. In order to improve the matching precision and the recall of the information retrieval, this paper proposes a multi-agent improved semantic similarity matching algorithm based on the ontology tree, which can avoid the considerable computation redundancies and mismatching during the entire matching process. The results of the experiments performed on our algorithm show improvements in precision and recall compared with the information retrieval techniques based on the traditional semantic similarity matching methods.


  1. X. Li, F. Bian, H. Zhang, C. Diot, R. Govindan, and G. Iannaccone, "MIND: a distributed multi-dimensional indexing system for network monitoring," IEEE Infocom-06 Barcelona, Apr. 2006.
  2. A. Ntoulas, G. Chao, and J. Cho, "The infocious web search engine: improving web searching through linguistic analysis," International World Wide Web Conference Committee (IW3C2) ACM, Chiba Japan, May 2005.
  3. T. Berners-Lee, J. Hendler, and O. Lassila, "The semantic web," Scientific American, May 2001.
  4. L. Wu, J. P. Feng, and Y. Luo, "A personalized intelligent web retrieval system based on the knowledge-base concept and latent semantic indexing model," 2009 Seventh ACIS International Conference on Software Engineering Research, Management and Applications, pp. 45-50, Dec. 2009.
  5. W.-D. Fang, L. Zhang, Y.-X.Wang, and S.-B.Dong, "Towards a semantic search engine based on Ontologies," IEEE Preceedings of the Fourth International Conference on Machine Learning and Cybernetics Guangzhou China, pp. 1913-1918, Aug. 2005.
  6. G. Varelas, E. Voutsakis, and P. Raftopoulou, "Semantic similarity methods in WordNet and their application to information retrieval on the web," 7th ACM International Workshop on Web Information and Data Management, pp. 10- 16, Nov. 2005.
  7. K. Saruladha, Dr. G. Aghila, and S. Raj, "A new semantic similarity metric for solving sparse data problem in ontology based information retrieval system," IJCSI International Journal of Computer Science Issues, vol. 7, no. 11, pp. 40-48, May 2010.
  8. J. Mustafa, S. Khan, and K. Latif, "Ontology based semantic information retrieval," 2008 4th International IEEE Conference Intelligent Systems, vol. 3, pp. 2214-2219, Sep. 2008.
  9. Chuan li, "Research on an efficient ontology matching algorithm," Chongqing University, 2011.
  10. Q. Gao and Y. I. Cho, "A multi-agent information retrieval system based on Ontology," 12th International Conference on Intelligent Autonomous System, Jun. 2012.
  11. A. Maedche and S. Staab, "Measuring similarity between Ontologies," Proc of European Conference on Knowledge Acquisition and Management, London: Springer-Verlag, pp. 251-263, 2002.
  12. A. Budanitsky and H. G. Evaluating, "WordNet-based measures oflexical semantic relatedness," Computational Linguistics, vol. 32, no. 1, pp. 13-47, 2006.
  13. P. Pantel and L. Dekang, "Discovering word senses from text," Proc of ACM SIGKDD Conference on Knowledge Discovery and Data Mining, New York:ACM Press, pp. 613-619, 2002.
  14. P. Bouquet, J. Euzenat, E. Franconi, L. Serafini, G. Stamou, and S. Tessaris, "Specification of a common framework for characterizing alignment,"Knowledge Web Deliverable, 2004.
  15. Reuters corpus volume 1.
  16. TREC 2002 Collections.
  17. D. Bonino, F. Corno, L. Farinetti, and A. Bosca, "Ontology driven semantic search," WSEASTransaction on Information Science andApplication, vol. 1, pp. 1597-1605, Dec. 2004.