MAPSOM: User Involvement in Ontology Matching

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8388)

Abstract

This paper presents a semi-automatic similarity aggregating system for ontology matching problem. The system consists of two main parts. The first part is aggregation of similarity measures with the help of self-organizing map. The second part incorporates user feedback for refining self-organizing map outcomes. The system calculates different similarity measures (e.g., string-based similarity measure, WordNet-based similarity measure...) to cover different causes of semantic heterogeneity. The next step is similarity aggregation by means of the self-organizing map and the ward clustering. The final step is the active learning phase for results tuning. We implemented this idea as MAPSOM framework. Our experimental results show that MAPSOM framework can be used for problems where the highest precision is needed.

References

  1. 1.
    Belkin, N.J., Croft, W.B.: Information filtering and information retrieval: two sides of the same coin? Commun. ACM 35(12), 29–38 (1992)CrossRefGoogle Scholar
  2. 2.
    Wache, H., Voegele, T., Visser, U., Stuckenschmidt, H., Schuster, G., Neumann, H., Hbner, S.: Ontology-based integration of information-a survey of existing approaches. In: Proceedings of IJCAI Workshop on Ontologies and Information Sharing, pp. 108–117 (2001)Google Scholar
  3. 3.
    Kashyap, V., Sheth, A.: Semantic and schematic similarities between database objects: a context-based approach. Int. J. Very Large Data Bases 5(4), 276–304 (1996)CrossRefGoogle Scholar
  4. 4.
    Kim, W., Seo, J.: Classifying schematic and data heterogeneity in multidatabase systems. Computer 24(12), 12–18 (1991)CrossRefGoogle Scholar
  5. 5.
    Goh, C.H.: Representing and reasoning about semantic conflicts in heterogeneous information systems. Ph.D. thesis (1996)Google Scholar
  6. 6.
    Gruber, T.R.: Toward principles for the design of ontologies used for knowledge sharing? Int. J. Hum.-Comput. Stud. 43(5), 907–928 (1995)CrossRefGoogle Scholar
  7. 7.
    Euzenat, J., Shvaiko, P.: Ontology Matching. Springer, Heidelberg (2007)MATHGoogle Scholar
  8. 8.
    Ichise, R.: Machine learning approach for ontology mapping using multiple concept similarity measures. In: Proceedings of the 7th IEEE/ACIS International Conference on Computer and Information Science, pp. 340–346 (2008)Google Scholar
  9. 9.
    Jirkovský, V., Obitko, M.: Ontology mapping approach for fault classification in multi-agent systems. In: Proceedings of the IFAC Conference on Manufacturing Modelling, Management, and Control, pp. 951–956 (2013)Google Scholar
  10. 10.
    Miller, G.A.: Wordnet: a lexical database for english. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  11. 11.
    Valtchev, P., Euzenat, J.: Dissimilarity measure for collections of objects and values. In: Liu, X., Cohen, P., Berthold, M. (eds.) IDA 1997. LNCS, vol. 1280, pp. 259–272. Springer, Heidelberg (1997) CrossRefGoogle Scholar
  12. 12.
    Melnik, S., Rahm, E., Bernstein, P.A.: Rondo: a programming platform for generic model management. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 193–204. ACM (2003)Google Scholar
  13. 13.
    Aumueller, D., Do, H.H., Massmann, S., Rahm, E.: Schema and ontology matching with coma. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 906–908. ACM (2005)Google Scholar
  14. 14.
    Jian, N., Hu, W., Cheng, G., Qu, Y.: Falcon-ao: Aligning ontologies with falcon. In: Proceedings of K-CAP Workshop on Integrating Ontologies, pp. 85–91 (2005)Google Scholar
  15. 15.
    Bernstein, P.A., Melnik, S., Churchill, J.E.: Incremental schema matching. In: Proceedings of the 32nd International Conference on Very Large Data Bases, pp. 1167–1170 (2006)Google Scholar
  16. 16.
    Giunchiglia, F., Yatskevich, M., Avesani, P., Shvaiko, P.: A large dataset for the evaluation of ontology matching. Knowl. Eng. Rev. 24(2), 137–157 (2009)CrossRefGoogle Scholar
  17. 17.
    Shvaiko, P., Euzenat, J.: Ten challenges for ontology matching. In: Meersman, R., Tari, Z. (eds.) OTM 2008, Part II. LNCS, vol. 5332, pp. 1164–1182. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  18. 18.
    Do, H.H., Rahm, E.: Matching large schemas: approaches and evaluation. Inf. Syst. 32(6), 857–885 (2007)CrossRefGoogle Scholar
  19. 19.
    Falconer, S.M., Storey, M.-A.D.: A cognitive support framework for ontology mapping. In: Aberer, K., et al. (eds.) ISWC/ASWC 2007. LNCS, vol. 4825, pp. 114–127. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  20. 20.
    Mocan, A., Cimpian, E.: An ontology-based data mediation framework for semantic environments. Int. J. Seman. Web Inf. Syst. 3(2), 69–98 (2007)CrossRefGoogle Scholar
  21. 21.
    Robertson, G.G., Czerwinski, M.P., Churchill, J.E.: Visualization of mappings between schemas. In: Proceedings of the SIGCHI Conference on Human Factors in Computing System, pp. 431–439. ACM (2005)Google Scholar
  22. 22.
    Zhao, L., Ichise, R.: Aggregation of similarity measures in ontology matching. In: Proceedings of the 5th International Workshop on Ontology Matching, pp. 232–233 (2010)Google Scholar
  23. 23.
    Curino, C., Orsi, G., Tanca, L.: X-som: A flexible ontology mapper. In: Proceedings of the 18th International Workshop on Database and Expert Systems Applications, pp. 424–428. IEEE (2007)Google Scholar
  24. 24.
    Tran, Q.V., Ichise, R., Ho, B.Q.: Clusterbased similarity aggregation for ontology matching. In: Proceedings of the 6th International Workshop on Ontology Matching, pp. 142–147 (2011)Google Scholar
  25. 25.
    Kohonen, T.: The self-organizing map. Proc. IEEE 78(9), 1464–1480 (1990)CrossRefGoogle Scholar
  26. 26.
    Kaski, S., Kohonen, T.: Exploratory data analysis by the self-organizing map: Structures of welfare and poverty in the world. In: Proceedings of the 3rd International Conference on Neural Networks in the Capital Markets (1996)Google Scholar
  27. 27.
    Settles, B.: Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison (2009)Google Scholar
  28. 28.
    Lewis, D.D., Gale, W.A.: A sequential algorithm for training text classifiers. In: Croft, B.W., van Rijsbergen, C.J. (eds.) Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 3–12. Springer, New York (1994)Google Scholar
  29. 29.
    Lewis, D.D., Catlett, J.: Heterogenous uncertainty sampling for supervised learning. In: Proceedings of the 11th International Conference on Machine Learning, pp. 148–156 (1994)Google Scholar
  30. 30.
    Needleman, S.B., Wunsch, C.D.: A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48(3), 443–453 (1970)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Czech Technical University in PraguePragueCzech Republic
  2. 2.Rockwell Automation Research and Development CenterPragueCzech Republic
  3. 3.National Institute of InformaticsTokyoJapan

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