Secured Ontology Matching Using Graph Matching

  • K. Manjula ShenoyEmail author
  • K. C. Shet
  • U. Dinesh Acharya
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 177)


Today’s market evolution and high volatility of business requirements put an increasing emphasis on the ability for systems to accommodate the changes required by new organizational needs while maintaining security objectives satisfiability. This is all the more true in case of collaboration and interoperability between different organizations and thus between their information systems. Ontology mapping has been used for interoperability and several mapping systems have evolved to support the same. Usual solutions do not take care of security. That is almost all systems do a mapping of ontologies which are unsecured. We have developed a system for mapping secured ontologies using graph similarity concept. Here we give no importance to the strings that describe ontology concepts, properties etc. Because these strings may be encrypted in the secured ontology. Instead we use the pure graphical structure to determine mapping between various concepts of given two secured ontologies. The paper also gives the measure of accuracy of experiment in a tabular form in terms of precision, recall and F-measure.


Adjacency Matrix Authority Score Product Graph Graph Match Ontology Mapping 
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.
    Clifton, C., Doan, A., Kantarcioglu, M., Schadow, G., Vaidya, J., Elma Garmid, A., Suciu, D.: Privacy preserving data integration and sharing. In: Proc. DMKD 2004 (2004)Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Privacy preserving data mining. In: Proc. SIGMOD 2000 (2000)Google Scholar
  3. 3.
    Mitra, P., Liu, P., Pan, C.C.: Privacy preserving ontology matching. In: Proc. AAAI Workshop (2005)Google Scholar
  4. 4.
    Li, J.: LOM:Lexicon based ontology mapping tool. In: Proc. PerMIS 2004 (2004)Google Scholar
  5. 5.
    Noy, N.F., Musen, M.A.: Anchor-Prompt: Using non local context for semantic matching. In: Proc. IJCAI 2001 (2001)Google Scholar
  6. 6.
    Noy, N.F., Musen, M.A.: The Prompt Suite: Interactive tools for ontology mapping and merging. International Journal of Human Computer Studies 59(6) (2003)Google Scholar
  7. 7.
    Melnik, S., Molina, H.G., Rahm, E.: Similarity flooding a versatile graph matching algorithm and its application to schema matching. In: Proc. ICDE 2002 (2002)Google Scholar
  8. 8.
    Doan, A., Madhavan, J., Domingos, P., Halevy, A.: Learning to map be tween ontologies on the semantic web. In: Proc. WWW 2002 (2002)Google Scholar
  9. 9.
    Melnik, S., Molina, H.G., Rahm, E.: Similarity flooding a versatile graph matching algorithm. In: Proc. ICDE 2007 (2007)Google Scholar
  10. 10.
    Choi, N., Song, I.Y., Han, H.: A survey on ontology mapping. In: SIGMOD RECORD 2006 (2006)Google Scholar
  11. 11.
    Shvaiko, P., Euzenat, J.: Ten Challenges for ontology matching. In: Proc. ICODAS 2008 (2008)Google Scholar
  12. 12.
    Kalfoglou, Y., Schorelmmer, M.: Ontology mapping:The State of the Art. The Knowledge Engineering Review 18(1) (2003)Google Scholar
  13. 13.
    Ehrig, M., Staab, S.: QOM: Quick Ontology mapping. GI Jahrestagung (1) (2004)Google Scholar
  14. 14.
    Rahm, E., Bernstein, P.: A survey of approaches to automatic schema matching. The VLDB Journal 10(4) (2001)Google Scholar
  15. 15.
    Shvaiko, P., Euzenat, J.: A Survey of Schema-Based Matching Approaches. In: Spaccapietra, S. (ed.) Journal on Data Semantics IV. LNCS, vol. 3730, pp. 146–171. Springer, Heidelberg (2005)Google Scholar
  16. 16.
    Kleinberg, J.M.: Authoritive sources in a hyper linked environment. Journal of ACM (1999)Google Scholar
  17. 17.
    Van Dooren, B., et al.: A measure of similarity between graph vertices: Application to synonym extraction and we searching. SIAM Review (2004)Google Scholar
  18. 18.
    Ninove, L., et al.: Graph similarity algorithms. Seminar Presented at Department of Mathematical Engineering. University of Catholique de Louvain (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • K. Manjula Shenoy
    • 1
    Email author
  • K. C. Shet
    • 2
  • U. Dinesh Acharya
    • 1
  1. 1.Department of Computer Science and EngineeringMIT, Manipal UniversityManipalIndia
  2. 2.Department of Computer EngineeringNITKSuratkalIndia

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