OntoMas: a Tutoring System dedicated to Ontology Matching

  • M. Huza
  • M. Harzallah
  • F. Trichet
Conference paper

Abstract

Ontology matching is now a core question in most of the applications that require semantic interoperability. To deal with this problem, a lot of methods, classified according to different criteria, are currently developed. However, choosing the most relevant method in a particular context is not an easy task since it requires to know all the methods and their intrinsic properties. The objective of the OntoMas1 tutoring system (Ontology Matching Assistant) is (1) to propose an architecture and to develop an effective knowledge-based system dedicated to a fine-grained description and a classification of the current matching methods and (2) to provide functionalities dedicated to the definition of advices and explanations (for the end-user), in order to facilitate both the choice of the most suitable method for a particular matching problem and the learning of this new domain: ontology matching.

This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

6 References

  1. [1]
    B. Ashpole, M. Ehrig, J. Euzenat, and H. Stuckenschmidt. In Proceedings of the KCAP 2005 Workshop on Integrating Ontologies, http://sunsite.informatik.rwthaachen.de/Publications/CEUR-WS/Vol-156/, 2005. CEUR Proceedings-Volume 156. 2005.Google Scholar
  2. [2]
    D. Bianchini, S. Castano, F. D’Antonio, V. De Antonellis, M. Harzallah, M. Missikoff, S. Montanelli, Digital Resource Discovery: Semantic Annotation and Matchmaking Techniques. Proceedings of the Interoperability for Enterprise Software and Applications Conference (I-ESA 2006). 2006.Google Scholar
  3. [3]
    P. Bouquet, L. Serafini, and S. Zanobini. Semantic coordination: A new approach and an application. In Proceedings of the International Semantic Web Conference (ISWC), pages 130–145, 2003.Google Scholar
  4. [4]
    A. Doan and A. Halevy. Semantic integration research in the database community: A brief survey. 2005.Google Scholar
  5. [5]
    A. Doan, J. Madhavan, P. Domingos, and A. Halevy. Ontology Matching: A Machine Learning Approach. In Handbook on Ontologies in Information Systems, pages 397–416, 2004.Google Scholar
  6. [6]
    J. Euzenat, J. Barrasa, P. Bouquet, R. Dieng, M. Ehrig, M. Hauswirth, M. Jarrar, R. Lara, D. Maynard, A. Napoli, G. Stamou, H. Stuckenschmidt, P. Shvaiko, S. Tessaris, S. van Acker, I. Zaihrayeu, and T. L. Bach. D2.2.3: State of the art on ontology alignment. Technical report, NoE Knowledge Web project deliverable, 2004. http://knowledgeweb.semanticweb.org/Google Scholar
  7. [7]
    J. Euzenat and P. Valtchev. Similarity-based ontology alignment in OWL-Lite. In R. Lopez de Mantaras and L. Saitta, editors, European Conference on Artificial Intelligence (ECAI’2004), pages 333–337. IOS Press, 2004.Google Scholar
  8. [8]
    F. Giunchiglia, P. Shvaiko, and M. Yatskevich. S-Match: an Algorithm and an Implementation of Semantic Matching. In Proceedings of the First European Semantic Web Symposium, pages 61–65. Springer-Verlag. LNCS 3053, 2004.Google Scholar
  9. [9]
    F. Giunchiglia and P. Shvaiko. Semantic matching. The Knowledge Engineering Review Journal (KER), 18(3):265–280, 2003.CrossRefGoogle Scholar
  10. [10]
    Y. Kalfoglou and M. Schorlemmer. Ontology mapping: the state of the art. The Knowledge Engineering Review, 18(1):1–31, 2003.CrossRefGoogle Scholar
  11. [11]
    N. F. Noy and M. Musen. The PROMPT suite: Interactive tools for ontology merging and mapping. International Journal of Human-Computer Studies, 59(6):983–1024, 2003.CrossRefGoogle Scholar
  12. [12]
    N. F. Noy. Semantic integration: A survey of ontology-based approaches. SIGMOD Record, 33(4):65–70, 2004.CrossRefGoogle Scholar
  13. [13]
    Interop partners, D8.1: State of the art and state of the practice including. initial possible research orientations. Technical report, NoE Interop Deliverable, 2004. http://www.interop-noe.orgGoogle Scholar
  14. [14]
    P. Shvaiko and J. Euzenat. A survey of schema-based matching approaches. 3730:146–171, 2005.Google Scholar
  15. [15]
    G. Stumme and A. Maedche. FCA-MERGE: Bottom-up merging of ontologies. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI’2001), pages 225–234, 2001.Google Scholar

Copyright information

© Springer-Verlag London Limited 2007

Authors and Affiliations

  • M. Huza
    • 1
  • M. Harzallah
    • 1
  • F. Trichet
    • 1
  1. 1.LINA - Laboratoire d’Informatique Nantes Atlantique (FRE CNRS 2729) - Team « Knowledge and Decision »University of NantesNantesFrance

Personalised recommendations