Ontology Mapping by Axioms (OMA)

  • Marc Ehrig
  • York Sure
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3782)


Creation and execution of semantic mappings between two (or more) ontologies is a core issue to enable interoperability across various applications in the Semantic Web. To handle the increasing number of individual ontologies, but also for being able to create mappings on the fly, it becomes necessary to develop automatic approaches. In this paper, we determine mappings based on the similarity of the features of individual ontological entities. We show that mappings can be derived automatically by encoding similarities into logical axioms. Processing these axioms by inference engines allows for detection, creation and processing of mappings on the fly without human intervention. The advantages of this approach are obvious. Firstly, the axioms can easily be reused for mappings of arbitrary ontologies, no additional modelling effort is required. Secondly, the inference engine is the only mandatory technological infrastructure which means that no additional implementation effort is needed. Finally, we evaluate our approach with very promising results.


Logic Program Description Logic Inference Engine Similarity Rule Semantic 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Stumme, G., Ehrig, M., Handschuh, S., Hotho, A., Maedche, A., Motik, B., Oberle, D., Schmitz, C., Staab, S., Stojanovic, L., Stojanovic, N., Studer, R., Sure, Y., Volz, R., Zacharias, V.: The Karlsruhe view on ontologies. Technical report, University of Karlsruhe, Institute AIFB (2003)Google Scholar
  2. 2.
    Brickley, D., Guha, R.V.: RDF Vocabulary Description Language 1.0: RDF Schema. W3C Recommendation 10 February 2004 (2004), available at
  3. 3.
    Smith, M.K., Welty, C., McGuinness, D.: OWL Web Ontology Language Guide (2004) W3C Recommendation 10 February (2004), available at
  4. 4.
    Klein, M.: Combining and relating ontologies: an analysis of problems and solutions. In: Gomez-Perez, A., et al (ed.) Workshop on Ontology and Information Sharing, IJCAI 2001, Seattle, USA (2001)Google Scholar
  5. 5.
    Bisson, G.: Why and how to define a similarity measure for object based representation systems. Towards Very Large Knowledge Bases, 236–246 (1995)Google Scholar
  6. 6.
    Ehrig, M., Haase, P., Stojanovic, N., Hefke, M.: Similarity for ontologies - a comprehensive framework. In: 13th European Conference on Information Systems (2005)Google Scholar
  7. 7.
    Kifer, M., Lausen, G., Wu, J.: Logical foundations of object-oriented and frame-based languages. J. of the ACM 42, 741–843 (1995)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Decker, S., Erdmann, M., Fensel, D., Studer, R.: [9], 351–369Google Scholar
  9. 9.
    Meersman, R., Tari, Z., Stevens, S. (eds.): Database Semantics: Semantic Issues in Multimedia Systems. Kluwer Academic Publisher, Dordrecht (1999)Google Scholar
  10. 10.
    Frohn, J., Himmeröder, R., Kandzia, P., Schlepphorst, C.: How to write F–Logic programs in FLORID. A tutorial for the database language F–Logic. Technical report, Institut für Informatik der Universität Freiburg, Version 1.0 (1996)Google Scholar
  11. 11.
    van Gelder, A., Ross, K.A., Schlipf, J.S.: The well-founded semantics for general logic programs. J. of the ACM 38, 620–650 (1991)zbMATHGoogle Scholar
  12. 12.
    Horrocks, I.: Using an expressive description logic: FaCT or fiction? In: Proc. of the Int. Conf. on Knowledge Representation (KR 1998), pp. 636–649. Morgan Kaufmann, San Francisco (1998)Google Scholar
  13. 13.
    van Gelder, A.: The alternating fixpoint of logic programs with negation. J. of Computer and System Sciences 47, 185–221 (1993)zbMATHCrossRefGoogle Scholar
  14. 14.
    Kifer, M., Lozinskii, E.: A framework for an efficient implementation of deductive databases. In: Proc. of the 6th Advanced Database Symposium, Tokyo, pp. 109–116 (1986)Google Scholar
  15. 15.
    Erdmann, M.: Ontologien zur konzeptuellen Modellierung der Semantik von XML. Books on Demand, PhD Thesis (2001)Google Scholar
  16. 16.
    Ontoprise: How to write F–Logic programs – a tutorial for the language F–Logic (2004)Google Scholar
  17. 17.
    Ehrig, M., Sure, Y.: Ontology mapping - an integrated approach, pp. 76–91 (2004)Google Scholar
  18. 18.
    Bussler, C., Davies, J., Fensel, D., Studer, R. (eds.): Proc. of the First Europ. Semantic Web Symp. (ESWS 2004). LNCS, vol. 3053. Springer, Heraklion (2004)Google Scholar
  19. 19.
    Levenshtein, I.V.: Binary codes capable of correcting deletions, insertions, and reversals. Cybernetics and Control Theory (1966)Google Scholar
  20. 20.
    Kifer, M., Lausen, G., Wu, J.: Logical foundations of object-oriented and frame-based languages. Journal of the ACM 42 (1995)Google Scholar
  21. 21.
    Maier, A., Schnurr, H.P., Sure, Y.: Ontology-based information integration in the automotive industry. [22], pp. 897–912.Google Scholar
  22. 22.
    Fensel, D., Sycara, K., Mylopoulos, J. (eds.): Proc. of the 2nd Int. Semantic Web Conf (ISWC 2003). Volume 2870 of LNCS., Sanibel Island, FL, USA, Springer (2003)Google Scholar
  23. 23.
    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
  24. 24.
    Do, H., Melnik, S., Rahm, E.: Comparison of schema matching evaluations. In: Proceedings of the second int. workshop on Web Databases (German Informatics Society) (2002)Google Scholar
  25. 25.
    Rodríguez, M.A., Egenhofer, M.J.: Determining semantic similarity among entity classes from different ontologies. IEEE Transactions on Knowledge and Data Engineering (2000)Google Scholar
  26. 26.
    Noy, N.F., Musen, M.A.: Anchor-prompt: Using non-local context for semantic matching. In: Workshop on Ontologies and Information Sharing at the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI 2001), Seattle, WA (2001)Google Scholar
  27. 27.
    Doan, A., Madhavan, J., Domingos, P., Halevy, A.: Learning to map between ontologies on the semantic web. In: Proceedings to the Eleventh International World Wide Web Conference, Honolulu, Hawaii, USA (2002)Google Scholar
  28. 28.
    Bouquet, P., Magnini, B., Serafini, L., Zanobini, S.: A SAT-based algorithm for context matching. In: Blackburn, P., Ghidini, C., Turner, R.M., Giunchiglia, F. (eds.) CONTEXT 2003. LNCS, vol. 2680, pp. 66–79. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  29. 29.
    McGuinness, D.L.: Conceptual modeling for distributed ontology environments. In: International Conference on Conceptual Structures, pp. 100–112 (2000)Google Scholar
  30. 30.
    Mitra, P., Wiederhold, G., Kersten, M.: A graph-oriented model for articulation of ontology interdependencies. In: Zaniolo, C., Grust, T., Scholl, M.H., Lockemann, P.C. (eds.) EDBT 2000. LNCS, vol. 1777, p. 86. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  31. 31.
    Do, H., Rahm, E.: COMA - a system for flexible combination of schema matching approaches. In: Proceedings of the 28th VLDB Conference, Hong Kong, China (2002)Google Scholar
  32. 32.
    Euzenat, J., Petko, V.: An integrative proximity measure for ontology alignment. In: Proc. ISWC 2003 workshop on semantic information integration, Sanibel Island, FL, US, pp. 33–38 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Marc Ehrig
    • 1
  • York Sure
    • 1
  1. 1.Institute AIFBUniversity of KarlsruheKarlsruheGermany

Personalised recommendations