Consistent Ontologies Evolution Using Graph Grammars

  • Mariem Mahfoudh
  • Germain Forestier
  • Laurent Thiry
  • Michel Hassenforder
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8041)

Abstract

Ontologies are often used for the meta-modelling of dynamic domains, therefore it is essential to represent and manage their changes and to adapt them to new requirements. Due to changes, an ontology may become invalid and non-interpretable. This paper proposes the use of the graph grammars to formalize and manage ontologies evolution. The objective is to present an a priori approach of inconsistencies resolutions to adapt the ontologies and preserve their consistency. A framework composed of different graph rewriting rules is proposed and presented using the AGG (Algebraic Graph Grammar) tool. As an application, the article considers the EventCCAlps ontology developed within the CCAlps European project.

Keywords

ontologies graph grammars evolution rewriting ontology changes category theory AGG 

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References

  1. 1.
    Gruber, T.R., et al.: A translation approach to portable ontology specifications. Knowledge Acquisition 5(2), 199–220 (1993)CrossRefGoogle Scholar
  2. 2.
    Stojanovic, L.: Methods and Tools for Ontology Evolution. PhD thesis, University of Karlsruhe, Germany (2004)Google Scholar
  3. 3.
    Qin, L., Atluri, V.: Semdiff: An approach to detecting semantic changes to ontologies. International Journal on Semantic Web and Information Systems (IJSWIS) 2(4), 1–32 (2006)CrossRefGoogle Scholar
  4. 4.
    Klein, M.: Change Management for Distributed Ontologies. PhD thesis, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands (2004)Google Scholar
  5. 5.
    Rozenberg, G.: Handbook of graph grammars and computing by graph transformation, vol. 1. World Scientific (1999)Google Scholar
  6. 6.
    Ehrig, H., Pfender, M., Schneider, H.J.: Graph-grammars: An algebraic approach. In: IEEE Conference Record of 14th Annual Symposium on Switching and Automata Theory, SWAT 2008, pp. 167–180. IEEE (1973)Google Scholar
  7. 7.
    Löwe, M.: Algebraic approach to single-pushout graph transformation. Theoretical Computer Science 109(1), 181–224 (1993)MathSciNetMATHCrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Ermel., C., Rudolf., M., Taentzer, G.: The agg approach: Language and environment. In: Handbook of Graph Grammars and Computing by Graph Transformation, pp. 551–603. World Scientific Publishing Co., Inc. (1999)Google Scholar
  10. 10.
    Nickel, U., Niere, J., Zündorf, A.: The fujaba environment. In: Proceedings of the 22nd International Conference on Software Engineering, pp. 742–745. ACM (2000)Google Scholar
  11. 11.
    Varró, D., Pataricza, A.: Generic and meta-transformations for model transformation engineering. In: Baar, T., Strohmeier, A., Moreira, A., Mellor, S.J. (eds.) UML 2004. LNCS, vol. 3273, pp. 290–304. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Object Management Group: Ontology definition metamodel (omg) version 1.0. Technical report, Object Management Group (2009)Google Scholar
  13. 13.
    Raimond, Y., Abdallah, S.: The event ontology. Technical report, Technical report, 2007 (2007), http://motools.sourceforge.net/event
  14. 14.
    Shaw, R., Troncy, R., Hardman, L.: Lode: Linking open descriptions of events. The Semantic Web, 153–167 (2009)Google Scholar
  15. 15.
    Noy, N.F., Klein, M.: Ontology evolution: Not the same as schema evolution. Knowledge and information systems 6(4), 428–440 (2004)CrossRefGoogle Scholar
  16. 16.
    Rahm, E., Bernstein, P.A.: An online bibliography on schema evolution. ACM SIGMOD Record 35(4), 30–31 (2006)CrossRefGoogle Scholar
  17. 17.
    Stojanovic, N., Stojanovic, L., Handschuh, S.: Evolution in the ontology-based knowledge management system. In: Proceedings of the European Conference on Information Systems-ECIS (2002)Google Scholar
  18. 18.
    Rogozan, D., Paquette, G.: Managing ontology changes on the semantic web. In: Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 430–433. IEEE (2005)Google Scholar
  19. 19.
    Hartung, M., Groß, A., Rahm, E.: Conto-diff: Generation of complex evolution mappings for life science ontologies. J. Biomed. Inform. (1), 15–32 (2013)Google Scholar
  20. 20.
    Khattak, A.M., Latif, K., Lee, S.: Change management in evolving web ontologies. Knowledge-Based Systems 37(0), 1–18 (2013)CrossRefGoogle Scholar
  21. 21.
    Luong, P.H., Dieng-Kuntz, R.: A rule-based approach for semantic annotation evolution. Computational Intelligence 23(3), 320–338 (2007)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Dragoni, M., Ghidini, C.: Evaluating the impact of ontology evolution patterns on the effectiveness of resources retrieval. In: 2nd Joint Workshop on Knowledge Evolution and Ontology Dynamics EvoDyn 2012 (2012)Google Scholar
  23. 23.
    Wang, M., Jin, L., Liu, L.: A description method of ontology change management using pi-calculus. In: Knowledge Science, Engineering and Management, pp. 477–489 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

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

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