Algebraic Graph Transformations for Merging Ontologies

  • Mariem Mahfoudh
  • Laurent Thiry
  • Germain Forestier
  • Michel Hassenforder
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8748)

Abstract

The conception of an ontology is a complex task influenced by numerous factors like the point of view of the authors or the level of details. Consequently, several ontologies have been developed to model identical or related domains leading to partially overlapping representations. This divergence of conceptualization requires the study of ontologies merging in order to create a common repository of knowledge and integrate various sources of information. In this paper, we propose a formal approach for merging ontologies using typed graph grammars. This method relies on the algebraic approach to graph transformations, SPO (Simple PushOut) which allows a formal representation and ensures the consistence of the results. Furthermore, a new ontologies merging algorithm called GROM (Graph Rewriting for Ontology Merging) is presented.

Keywords

Ontologies Merging Typed Graph Grammars Algebraic Graph Transformations GROM 

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References

  1. 1.
    Staab, S., Studer, R.: Handbook on ontologies. Springer (2010)Google Scholar
  2. 2.
    Rosse, C., Mejino Jr., J.L.: A reference ontology for biomedical informatics: the foundational model of anatomy. Journal of Biomedical Informatics 36(6), 478–500 (2003)CrossRefGoogle Scholar
  3. 3.
    Klein, M.: Combining and relating ontologies: an analysis of problems and solutions. In: IJCAI-2001, Workshop on Ontologies and Information Sharing, pp. 53–62 (2001)Google Scholar
  4. 4.
    Ehrig, H., Montanari, U., Rozenberg, G., Schneider, H.J.: Graph Transformations in Computer Science. Geschäftsstelle Schloss Dagstuhl (1996)Google Scholar
  5. 5.
    Zimmermann, A., Krotzsch, M., Euzenat, J., Hitzler, P.: Formalizing ontology alignment and its operations with category theory. Frontiers in Artificial Intelligence and Applications 150, 277–288 (2006)Google Scholar
  6. 6.
    d’Aquin, M., Doran, P., Motta, E., Tamma, V.A.: Towards a parametric ontology modularization framework based on graph transformation. In: WoMO (2007)Google Scholar
  7. 7.
    Cafezeiro, I., Haeusler, E.H.: Semantic interoperability via category theory. In: 26th International Conference on Conceptual Modeling, pp. 197–202. Australian Computer Society, Inc. (2007)Google Scholar
  8. 8.
    De Leenheer, P., Mens, T.: Using graph transformation to support collaborative ontology evolution. In: Schürr, A., Nagl, M., Zündorf, A. (eds.) AGTIVE 2007. LNCS, vol. 5088, pp. 44–58. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Javed, M., Abgaz, Y.M., Pahl, C.: Ontology change management and identification of change patterns. Journal on Data Semantics 2(2-3), 119–143 (2013)CrossRefGoogle Scholar
  10. 10.
    Mahfoudh, M., Forestier, G., Thiry, L., Hassenforder, M.: Consistent ontologies evolution using graph grammars. In: Wang, M. (ed.) KSEM 2013. LNCS, vol. 8041, pp. 64–75. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  11. 11.
    Rozenberg, G.: Handbook of graph grammars and computing by graph transformation, vol. 1. World Scientific (1999)Google Scholar
  12. 12.
    Ehrig, H., Pfender, M., Schneider, H.J.: Graph-grammars: An algebraic approach. In: Switching and Automata Theory (SWAT), pp. 167–180. IEEE (1973)Google Scholar
  13. 13.
    Barr, M., Wells, C.: Category theory for computing science, vol. 10. Prentice Hall, New York (1990)MATHGoogle Scholar
  14. 14.
    Fokkinga, M.M.: A gentle introduction to category theory — the calculational approach. In: Lecture Notes of the STOP 1992 Summerschool on Constructive Algorithmics, pp. 1–72. University of Utrecht (1992)Google Scholar
  15. 15.
    Löwe, M.: Algebraic approach to single-pushout graph transformation. Theoretical Computer Science 109(1), 181–224 (1993)MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    Ehrig, H.: Introduction to the algebraic theory of graph grammars (a survey). In: Claus, V., Ehrig, H., Rozenberg, G. (eds.) Graph Grammars 1978. LNCS, vol. 73, pp. 1–69. Springer, Heidelberg (1979)CrossRefGoogle Scholar
  17. 17.
    Ivanov, P., Voigt, K.: Schema, ontology and metamodel matching - different, but indeed the same? In: Bellatreche, L., Mota Pinto, F. (eds.) MEDI 2011. LNCS, vol. 6918, pp. 18–30. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  18. 18.
    Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions and reversals. In: Soviet physics doklady, vol. 10, pp. 707–710 (1966)Google Scholar
  19. 19.
    Do, H.H., Rahm, E.: Coma: a system for flexible combination of schema matching approaches. In: 28th International Conference on Very Large Data Bases (VLDB), VLDB Endowment, pp. 610–621 (2002)Google Scholar
  20. 20.
    Miller, G.A.: Wordnet: A lexical database for english. Communications of the ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  21. 21.
    Shvaiko, P., Euzenat, J.: Ontology matching: state of the art and future challenges (2012)Google Scholar
  22. 22.
    Raunich, S., Rahm, E.: Towards a benchmark for ontology merging. In: Herrero, P., Panetto, H., Meersman, R., Dillon, T. (eds.) OTM-WS 2012. LNCS, vol. 7567, pp. 124–133. Springer, Heidelberg (2012)Google Scholar
  23. 23.
    Karsai, G., Agrawal, A., Shi, F., Sprinkle, J.: On the use of graph transformations in the formal specification of computer-based systems. In: IEEE TC-ECBS and IFIP10. 1 Joint Workshop on Formal Specifications of Computer-Based Systems, pp. 19–27 (2003)Google Scholar
  24. 24.
    Noy, N.F., Musen, M.A.: Algorithm and tool for automated ontology merging and alignment. In: 17th National Conference on Artificial Intelligence (AAAI), pp. 450–455. AAAI Press/The MIT Press (2000)Google Scholar
  25. 25.
    Nováček, V., Smrž, P.: Empirical merging of ontologies — A proposal of universal uncertainty representation framework. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS, vol. 4011, pp. 65–79. Springer, Heidelberg (2006)Google Scholar
  26. 26.
    Li, G., Luo, Z., Shao, J.: Multi-mapping based ontology merging system design. In: 2nd International Conference onAdvanced Computer Control (ICACC), vol. 2, pp. 5–11. IEEE (2010)Google Scholar
  27. 27.
    Raunich, S., Rahm, E.: Atom: Automatic target-driven ontology merging. In: 27th International Conference on Data Engineering (ICDE), pp. 1276–1279. IEEE (2011)Google Scholar
  28. 28.
    Fareh, M., Boussaid, O., Chalal, R., Mezzi, M., Nadji, K.: Merging ontology by semantic enrichment and combining similarity measures. International Journal of Metadata, Semantics and Ontologies 8(1), 65–74 (2013)CrossRefGoogle Scholar
  29. 29.
    Hitzler, P., Krötzsch, M., Ehrig, M., Sure, Y.: What is ontology merging? In: American Association for Artificial Intelligence (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mariem Mahfoudh
    • 1
  • Laurent Thiry
    • 1
  • Germain Forestier
    • 1
  • Michel Hassenforder
    • 1
  1. 1.MIPS EA 2332, Université de Haute AlsaceMulhouseFrance

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