Effective Large Scale Ontology Mapping

  • Zongjiang Wang
  • Yinglin Wang
  • Shensheng Zhang
  • Ge Shen
  • Tao Du
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4092)


Ontology mapping is the key point to reach interoperability over ontologies. It can identify the elements corresponding to each other. With the rapid development of ontology applications, domain ontologies became very large in scale. Dealing with the large scale ontology mapping problems is beyond the reach of the existing algorithms. To improve this situation a modularization-oriented approach (called MOM) was proposed in this paper. This approach tries to decompose a large mapping problem into several smaller ones and use a method to reduce the complexity dramatically. Several large and complex ontologies have been chosen and tested to verify this approach. Experimental results indicate that the MOM method can significantly reduce the time cost while keeping the high mapping accuracy.


Bipartite Graph Graph Match Module Match Ontology Mapping Lexical Similarity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Jayant, M., Philip, A.B., Erhard, R.: Generic Schema Matching with Cupid. In: Proceedings of the 27th International Conference on Very Large Data Bases. Morgan Kaufmann Publishers Inc., San Francisco (2001)Google Scholar
  2. 2.
    Do, H.H., Rahm, E.: COMA - a system for flexible combination of schema matching approaches. In: Proceedings of VLDB 2001, pp. 610–621 (2001)Google Scholar
  3. 3.
    AnHai, D., Jayant, M., Robin, D., Pedro, D., Alon, H.: Learning to match ontologies on the Semantic Web. The VLDB Journal 12(4), 303–319 (2003)CrossRefGoogle Scholar
  4. 4.
    Sergey, M., Erhard, R., Philip, A.B.: Rondo: a programming platform for generic model management. In: Proceedings of the 2003 ACM SIGMOD international conference on Management of data. ACM Press, San Diego (2003)Google Scholar
  5. 5.
    Giunchiglia, F., Shvaiko, P., Yatskevich, M.: S-match: an algorithm and an implementation of semantic matching. In: Bussler, C.J., Davies, J., Fensel, D., Studer, R. (eds.) ESWS 2004. LNCS, vol. 3053, pp. 61–75. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Thomas, R.G.: Toward principles for the design of ontologies used for knowledge sharing. Int. J. Hum.-Comput. Stud. 43(5-6), 907–928 (1995)CrossRefGoogle Scholar
  7. 7.
    Ehrig, M., Staab, S.: QOM – quick ontology mapping. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 683–697. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Erhard, R., Philip, A.B.: A survey of approaches to automatic schema matching. The VLDB Journal 10(4), 334–350 (2001)zbMATHCrossRefGoogle Scholar
  9. 9.
    Xiaomeng, S.: A text categorization perspective for ontology mapping. Technical report (2002)Google Scholar
  10. 10.
    Erhard, R., Hong-Hai, D., Sabine, M., Mann: Matching large XML schemas. SIGMOD Rec. 33(4), 26–31 (2004)CrossRefGoogle Scholar
  11. 11.
    Grau, B.C., Parsia, B., Sirin, E., Kalyanpur, A.: Modularizing OWL Ontologies. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729. Springer, Heidelberg (2005)Google Scholar
  12. 12.
    Oliver, K., Carsten, L., Frank, W., Michael, Z.: E-connections of abstract description systems. Artif. Intell. 156(1), 1–73 (2004)zbMATHCrossRefGoogle Scholar
  13. 13.
    Hopcroft, J., Karp, R.: An n5/2 algorithm for maximum matchings in bipartite graphs. SIAM Journal on Computing 2(4), 225–231 (1973)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Maedche, A., Staab, S.: Measuring similarity between ontologies. In: Gómez-Pérez, A., Benjamins, V.R. (eds.) EKAW 2002. LNCS, vol. 2473, p. 251. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  15. 15.
  16. 16.
    Euzenat, J., Valtchev, P.: Similarity-based ontology alignment in OWL-lite. In: Proceedings of ECAI (2004)Google Scholar
  17. 17.
    Do, H.H., Melnik, S., Rahm, E.: Comparison of schema matching evaluations. In: Proceedings of workshop on Web and Databases (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zongjiang Wang
    • 1
  • Yinglin Wang
    • 1
  • Shensheng Zhang
    • 1
  • Ge Shen
    • 1
  • Tao Du
    • 1
  1. 1.Dept. of Computer ScienceShanghai Jiaotong UniversityChina

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