Using Formal Concept Analysis to Extract a Greatest Common Model

Bastien Amar, Abdoulkader Osman Guédi, André Miralles, Marianne Huchard, Thérèse Libourel, Clémentine Nebut

Abstract

Data integration and knowledge capitalization combine data and information coming from different data sources designed by different experts having different purposes. In this paper, we propose to assist the underlying model merging activity. For close models made by experts of various specialities, we partially automate the identification of a Greatest Common Model (GCM) which is composed of the common concepts (coreconcepts) of the different models. Our methodology is based on Formal Concept Analysis which is a method of data analysis based on lattice theory. A decision tree allows to semi-automatically classify concepts from the concept lattices and assist the GCM extraction. We apply our approach on the EIS-Pesticide project, an environmental information system which aims at centralizing knowledge and information produced by different specialized teams.

References

  1. Altmanninger, K., Schwinger, W., and Kotsis, G. (2010). Semantics for accurate conflict detection in smover: Specification, detection and presentation by example. International Journal of Enterprise Information Systems, 6(1):68-84.
  2. Altmanninger, K., Seidl, M., and Wimmer, M. (2009). A survey on model versioning approaches. International Journal of Web Information Systems, 5(3):271-304.
  3. Arévalo, G., Falleri, J.-R., Huchard, M., and Nebut, C. (2006). Building abstractions in class models: Formal concept analysis in a model-driven approach. In Model Driven Engineering Languages and Systems (MoDELS), pages 513-527.
  4. Batini, C., Lenzerini, M., and Navathe, S. B. (1986). A comparative analysis of methodologies for database schema integration. ACM Computer Survey, 18:323- 364.
  5. Bendaoud, R., Napoli, A., and Toussaint, Y. (2008). Formal Concept Analysis: A unified framework for building and refining ontologies. In International Conference on Knowledge Engineering and Knowledge Management (EKAW), pages 156-171.
  6. Birkhoff, G. (1940). Lattice theory. American Mathematical Society.
  7. Cicchetti, A., Ruscio, D., and Pierantonio, A. (2008). Managing model conflicts in distributed development. In Model Driven Engineering Languages and Systems (MoDELS), pages 311-325.
  8. Cicchetti, A., Ruscio, D. D., and Pierantonio, A. (2007). A metamodel independent approach to difference representation. Journal of Object Technology, 6(9):165- 185.
  9. Dao, M., Huchard, M., Hacene, M. R., Roume, C., and Valtchev, P. (2006). Towards practical tools for mining abstractions in uml models. In International Conference on Enterprise Information Systems: Databases and Information Systems Integration (ICEIS 2006), pages 276-283.
  10. Doan, A. and Halevy, A. Y. (2005). Semantic integration research in the database community: A brief survey. AI Magazine, 26:83-94.
  11. Falleri, J.-R. (2009). Contributions à l'IDM : reconstruction et alignement de modèles de classes. PhD thesis, Université Montpellier 2.
  12. Formica, A. (2006). Ontology-based concept similarity in Formal Concept Analysis. Information Sciences, 176:2624-2641.
  13. Ganter, B. and Wille, R. (1999). Formal Concept Analysis: Mathematical Foundation. Springer-Verlag Berlin.
  14. Godin, R. and Mili, H. (1993). Building and maintaining analysis-level class hierarchies using galois lattices. In Eighth annual conference on Object-Oriented Programming Systems, Languages, and Applications (OOPSLA), pages 394-410.
  15. Kalfoglou, Y. and Schorlemmer, M. (2005). Ontology mapping: The state of the art. In Semantic Interoperability and Integration.
  16. Miralles, A., Gorretta, N., Miller, P. C., Walklate, P., Van Zuydam, R. P., Porskamp, H. A., Ganzelmeier, H., Rietz, S., Ade, G., Balsari, P., Vannucci, D., and Planas, S. (1994). Orchard sprayers : an european program to compare testing methods. In International symposium on fruit nut and vegetable production production engineering, Valencia Zaragoza, ESP, 22-26 mars 1993, pages 117-122.
  17. Miralles, A., Pinet, F., Carluer, N., Vernier, F., Bimonte, S., Lauvernet, C., and Gouy, V. (2011). EIS-Pesticide: an information system for data and knowledge capitalization and analysis. In Euraqua-PEER Scientific Conference, 26/10/2011 - 28/10/2011, page 1, Montpellier, FRA.
  18. Miralles, A. and Polvêche, V. (1998). Effects of the agrochemical products and adjuvants on spray quality and drift potential. In 5th International Symposium on Adjuvants for Agrochemicals - ISAA 7898, volume 1, pages 426-432, Memphis (USA).
  19. Ohst, D., Welle, M., and Kelter, U. (2003). Differences between versions of uml diagrams. SIGSOFT Software Engineering Notes, 28:227-236.
  20. Osman Guedi, A., Miralles, A., Huchard, M., and Nebut, C. (2011). Analyse de l'évolution d'un modèle : vers une méthode basée sur l'analyse formelle de concepts. In XXIXème Congrès INFORSID.
  21. Parent, C. and Spaccapietra, S. (1998). Issues and approaches of database integration. Communication of the ACM, 41:166-178.
  22. Pinet, F., Miralles, A., Bimonte, S., Vernier, F., Carluer, N., Gouy, V., and Bernard, S. (2010). The use of uml to design agricultural data warehouses. In International Conference on Agricultural Engineering (AgEng 2010), pages 1-10.
  23. Rahm, E. and Bernstein, P. A. (2001). A survey of approaches to automatic schema matching. The VLDB Journal, 10:334-350.
  24. Rouane, M. H., Dao, M., Huchard, M., and Valtchev, P. (2007). Aspects de la réinginierie des modèles uml par analyse de données relationnelles. Ingénierie des Systèmes d'information (RSTI série), 12:39-68.
  25. Shvaiko, P. and Euzenat, J. (2005). A Survey of SchemaBased Matching Approaches Journal on Data Semantics IV. In Spaccapietra, S. and Spaccapietra, S., editors, Journal on Data Semantics IV, volume 3730 of Lecture Notes in Computer Science, chapter 5, pages 146-171. Springer Berlin / Heidelberg, Berlin, Heidelberg.
  26. Stumme, G. and Maedche, A. (2001). Ontology merging for federated ontologies on the semantic web. In International Workshop for Foundations of Models for Information Integration (FMII-2001), pages 413-418.
  27. Tatsiopoulos, C. and Boutsinas, B. (2009). Ontology mapping based on association rule mining. In International Conference on Enterprise Information Systems: Databases and Information Systems Integration (ICEIS 2009), pages 33-40.
Download


Paper Citation


in Harvard Style

Amar B., Osman Guédi A., Miralles A., Huchard M., Libourel T. and Nebut C. (2012). Using Formal Concept Analysis to Extract a Greatest Common Model . In Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-8565-10-5, pages 27-37. DOI: 10.5220/0003996000270037


in Bibtex Style

@conference{iceis12,
author={Bastien Amar and Abdoulkader Osman Guédi and André Miralles and Marianne Huchard and Thérèse Libourel and Clémentine Nebut},
title={Using Formal Concept Analysis to Extract a Greatest Common Model},
booktitle={Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2012},
pages={27-37},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003996000270037},
isbn={978-989-8565-10-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Using Formal Concept Analysis to Extract a Greatest Common Model
SN - 978-989-8565-10-5
AU - Amar B.
AU - Osman Guédi A.
AU - Miralles A.
AU - Huchard M.
AU - Libourel T.
AU - Nebut C.
PY - 2012
SP - 27
EP - 37
DO - 10.5220/0003996000270037