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SVM-based ontology matching approach

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Abstract

There are a lot of heterogeneous ontologies in semantic web, and the task of ontology mapping is to find their semantic relationship. There are integrated methods that only simply combine the similarity values which are used in current multi-strategy ontology mapping. The semantic information is not included in them and a lot of manual intervention is also needed, so it leads to that some factual mapping relations are missed. Addressing this issue, the work presented in this paper puts forward an ontology matching approach, which uses multi-strategy mapping technique to carry on similarity iterative computation and explores both linguistic and structural similarity. Our approach takes different similarities into one whole, as a similarity cube. By cutting operation, similarity vectors are obtained, which form the similarity space, and by this way, mapping discovery can be converted into binary classification. Support vector machine (SVM) has good generalization ability and can obtain best compromise between complexity of model and learning capability when solving small samples and the nonlinear problem. Because of the said reason, we employ SVM in our approach. For making full use of the information of ontology, our implementation and experimental results used a common dataset to demonstrate the effectiveness of the mapping approach. It ensures the recall ration while improving the quality of mapping results.

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References

  1. T. Berners-Lee, J. Hendler, O. Lassila. The semantic web. Scientific American, vol. 284, no. 5, pp. 34–43, 2001.

    Article  Google Scholar 

  2. Amino Acid Ontologyv1.3, [Online], Available: http://www.co-ode.org/ontologies/aminoacid/2009/02/16/, March 4, 2012.

  3. Customer Complaint Ontology, [Online], Available: http://www.jarrar.info/CContology/, March 4, 2012.

  4. Search or Browse the Plant Ontology Database, [Online], Available: http://www.plantontology.org/, March 4, 2012.

  5. Gellish, [Online], Available: http://sourceforge.net/apps/trac/gellish/, March 4, 2012.

  6. The NeOn project and the NeOn Foundation, [Online], Available: http://aims.fao.org/website/NeON/sub2, March 4, 2012.

  7. B. T. Le, R. Dieng-Kuntz, F. Gandon. On ontology matching problems for building a corporate semantic web in a multi-communities organization. In Proceedings of the 6th International Conference on Enterprise Information Systems, PubZone, Porto, Portugal, pp. 236–243, 2004.

  8. J. Euzenat, P. Valtchev. Similarity-based ontology alignment for OWL-Lite. In Proceedings of European Conference on Artificial Intelligence, pp. 333–337, 2004. [Online], Available: http://disi.unitn.it/accord/RelatedWork/Matching/align-ECAI04-FSub.pdf, March 4, 2012.

  9. S. Castano, A. Ferrara, S. Montanelli. Matching ontologies in open networked systems: Techniques and applications. Journal on Data Semantics, vol. 3870, pp. 25–63, 2006.

    Google Scholar 

  10. H. Do, E. Rahm. Coma: A system for flexible combination of schema matching approaches. In Proceedings of the 28th International Conference on Very Large Data Bases, PubZone, Hong Kong, PRC, pp. 610–621, 2002.

    Chapter  Google Scholar 

  11. A. H. Doan, P. Domingos, A. Y. Halevy. Reconciling schemas of disparate data sources: A machine-learning approach. In Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data, ACM, New York, USA, pp. 509–520, 2001.

    Chapter  Google Scholar 

  12. T. R. Gruber. A translation approach to portable ontology specifications. Knowledge Acquisition, vol. 5, no. 2, pp. 199–220, 1993.

    Article  Google Scholar 

  13. R. Studer, V. R. Benjamins, D. Fensel. Knowledge engineering: Principles and methods. Data and Knowledge Engineering, vol. 25, no. 1–2, pp. 161–197, 1998.

    Article  MATH  Google Scholar 

  14. A. G. Perez, V. R. Benjamins. Overview of knowledge sharing and reuse components: Ontologies and problem solving methods. In Proceedings of the IJCAI299 Workshop on Ontologies and Problem-solving Methods, pp. 1-1–1-15, 1999.

  15. E. Rahm, P. A. Bernstein. A survey of approaches to automatic schema matching. The VLDB Journal, vol. 10, no. 4, pp. 334–350, 2001.

    Article  MATH  Google Scholar 

  16. Z. J. Wang, Y. L. Wang, S. S. Zhang. Effective large scale ontology mapping. In Proceedings of the 1st International Conference on Knowledge Science, Engineering and Management, Springer, Guilin, PRC, pp. 454–465, 2006.

    Google Scholar 

  17. D. Thau, S. Bowers, B. Ludascher. Merging sets of taxonomically organized data using concept mappings under uncertainty. In Proceedings of the Confederated International Conferences, CoopIS, DOA, IS, and ODBASE 2009 on the Move to Meaningful Internet Systems: Part II, ACM, Berlin, Germany, pp. 1103–1120, 2009.

    Google Scholar 

  18. P. Bouquet, J. Euzenat, E. Franconi, L. Serafini, G. Stamou, S. Tessaris. Specification of a Common Framework for Characterizing Alignment, Technical Report, University of Karlsruhe, Germany, 2004.

    Google Scholar 

  19. A. Locoro, V. Mascardi. A correspondence repair algorithm based onword sense disambiguation and upper ontologies. In Proceedings of KEOD, 2009. [Online], Available: http://www.disi.unige.it/person/LocoroA/download/LocoroMascardiKeod2009.pdf, March 4, 2012.

  20. WorldNet, [Online], Available: http://wordnet.princeton.edu/, March 4, 2012.

  21. A. E. Monge, C. P. Elkan. The field matching problem: Algorithms and applications. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, AAAI, pp. 267–270, 1996.

  22. P. Pantel, D. Lin. Discovering word senses from text. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Edmonton, Canada, pp. 613–619, 2002.

    Chapter  Google Scholar 

  23. G. Alexander, D. David, M. David. Large-scale Bayesian logistic regression for text categorization. Technometrics, vol. 49, no. 3, pp. 291–304, 2007.

    Article  MathSciNet  Google Scholar 

  24. S. Ashok, S. Mehran. Text Mining: Classification, Clustering, and Applications, Boca Raton, USA: CRC Press, 2009.

    MATH  Google Scholar 

  25. H. Kim, P. Howland, H. Park. Dimension reduction in text classification with support vector machine. Journal of Machine Learning Research, vol. 6, pp. 37–53, 2005.

    MathSciNet  MATH  Google Scholar 

  26. F. Sebastiani. Machine learning in automated text categorization. ACM Computing Surveys, vol. 34, no. 1, pp. 1–47, 2002.

    Article  Google Scholar 

  27. N. Miklos, V. Maria, M. Enrico. DSSim — Managing uncertainty on the semantic web. In Proceedings of the 2nd International Workshop on Ontology Matching, 2007. [Online], Available: http://wenku.baidu.com/view/3916881052d380eb62946d7a.html, March 4, 2012.

  28. A. Doan, J. Madhavan, P. Domingos, A. Halevy. Learning to map between ontologies on the semantic web. In Proceedings of the 11th World Wide Web Conference, pp. 662–673, 2002. [Online], Available: http://wenku.baidu.com/view/23e2f1000740be1e650e9ae3.html, March 4, 2012.

  29. N. F. Noy, M. A. Musen. Algorithm and tool for automated ontology merging and alignment. In Proceedings of the 2000 National Conference on Artificial Intelligence, AAAI, Austin, USA, pp. 450–455, 2000.

    Google Scholar 

  30. V. N. Vapnik. The Nature of Statistical Learning Theory, 2nd ed., New York, USA: Springer Verlag, 1999.

    Google Scholar 

  31. E. Osuna, R. Freund, F. Girosi. Training support vector machines: An application to face detection. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, San Juan, Puerto Rico, pp. 130–136, 1997.

    Chapter  Google Scholar 

  32. C. Y. Lu, P. F. Yan, C. S. Zhang, J. Zhou. Face recognition using support vector machine. In Proceedings of ICNNB’98, Beijing, PRC, pp. 652–655, 1998.

  33. V. Blanz, B. Schoblkopf, H. Bulthoff, C. Burges, V. Vapnik, T. Vetter. Comparison of view-based object recognition algorithms using realistic 3D models. [Online], Available: http://www.kyb.mpg.de/fileadmin/user_upload/files/publications/pdfs/pdf445.pdf, March 4, 2012.

  34. A. Alias, B. Subramanian, S. Pramala, B. Rajalakshmi, R. Rajaram. Improving decision tree performance by exception handling. International Journal of Automation and Computing, vol. 7, no. 3, pp. 372–380, 2010.

    Article  Google Scholar 

  35. X. H. Huang, X. J. Zeng, M. Wang. SVM-based identification and un-calibrated visual servoing for micromanipulation. International Journal of Automation and Computing, vol. 7, no. 1, pp. 47–54, 2010.

    Article  Google Scholar 

  36. Ontology Alignment Evaluation Initiative, [Online], Available: http://oaei.ontologymatching.org/2009/, March 4, 2012.

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, PRC

    Lei Liu, Feng Yang, Peng Zhang, Jing-Yi Wu & Liang Hu

  2. Department of Information, Jilin Teachers’ Institute of Engineering and Technology, Changchun, 130052, PRC

    Feng Yang

Authors
  1. Lei Liu
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  2. Feng Yang
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  3. Peng Zhang
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  5. Liang Hu
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Corresponding author

Correspondence to Liang Hu.

Additional information

This work was supported by National Natural Science Foundation of China (No. 60873044), Science and Technology Research of the Department of Jilin Education (Nos. 2009498, 2011394), and Opening Fund of Top Key Discipline of Computer Software and Theory in Zhejiang Provincial Colleges at Zhejiang Normal University of China (No. ZSDZZZZXK11).

Lei Liu received his B. Sc. and M. Sc. degrees in computer software and theory from the Jilin University, PRC in 1982 and 1985, respectively. Currently, he is a professor and doctoral supervisor in the Department of Computer Science at Jilin University, PRC. He received research award from Science Foundation, and the Specialized Research Foundation for the Doctoral Program of Higher Education of China in 2006 and 2009, respectively.

His research interests include computer software theory, semantic web, formal methods and compiler theory.

Feng Yang received her B. Sc. and M. Sc. degrees in computer software and theory from the Northeast Normal University, PRC in 1998 and 2006, respectively. Currently, she is a Ph. D. candidate in computer software and theory in Jilin University, PRC. Since 1998, she has been a faculty member at Department of Information, Jilin Teachers’ Institute of Engineering and Technology.

Her research interests include ontology engineering, data mining, and control theory.

Peng Zhang graduated from College of Computer Science and Technology (CCST) in Jilin University (JLU), PRC in 2009. Currently, he is a doctoral candidate of CCST in JLU.

His research interests include semantic web and ontology engineering.

Jing-Yi Wu graduated from College of Computer Science and Technology (CCST) in Jilin University (JLU), PRC in 2009. Currently, she continues her master degree at JLU.

Her research interests include ontology mapping and software engineering.

Liang Hu received his B. Sc. degree from Harbin Institute of Technology in 1990, M. Sc. and Ph.D. degrees in computer software and theory from the Jilin University, PRC in 1993 and 1999, respectively. Currently, he is a professor and doctoral supervisor in the Department of Computer Science at Jilin University, PRC.

His research interests include computer network and information security.

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Liu, L., Yang, F., Zhang, P. et al. SVM-based ontology matching approach. Int. J. Autom. Comput. 9, 306–314 (2012). https://doi.org/10.1007/s11633-012-0649-x

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  • DOI: https://doi.org/10.1007/s11633-012-0649-x

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