Knowledge Extraction Using a Conceptual Information System (ExCIS)

  • Laurent Brisson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4623)


It is a well known fact that the data mining process can generate thousands of patterns from data. Various measures exist for evaluating and ranking these discovered patterns but often they don’t consider user subjective interest. We propose an ontology-based data-mining methodology called ExCIS (Extraction using a Conceptual Information System) for integrating expert prior knowledge in a data-mining process. Its originality is to build a specific Conceptual Information System related to the application domain in order to improve datasets preparation and results interpretation. This paper focus on our ontological choices and an interestingness measure IMAK which evaluates patterns considering expert knowledge.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bard, J.B., Rhee, S.Y.: Ontologies in Biology: Design, Applications and Future Challenges. Nature Review Genetics 5(3), 213–222 (2004)CrossRefGoogle Scholar
  2. 2.
    Chapman, P., al.: CRISP-DM - Step by step data mining guide CRoss Industry Standard Process for Data Mining,
  3. 3.
    Dai, H., Mobasher, B.: Using Ontologies to Discover Domain-level Web Usage Profiles. In: Proceedings 2nd ECML/PKDD Semantic Web Mining workshop (August 2002)Google Scholar
  4. 4.
    Guarino, N.: Formal Ontology and Information Systems. In: Proceedings of FOIS 1998, pp. 3–15 (June 1998)Google Scholar
  5. 5.
    Hilderman, R.J., Hamilton, H.J.: Evaluation of Interestingness Measures for Ranking Discovered Knowledge. In: Cheung, D., Williams, G.J., Li, Q. (eds.) PAKDD 2001. LNCS (LNAI), vol. 2035, pp. 247–259. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  6. 6.
    Johannesson, P.: A Method for Transforming Relational Schemas into Conceptual Schemas. In: Rusinkiewicz, M. (ed.) Proceedings 10th ICDE conference, pp. 115–122. IEEE Press, New York (1994)Google Scholar
  7. 7.
    Kashyap, V.: Design and Creation of Ontologies for Environmental Information Retrieval. In: Proceedings 12th workshop on Knowledge Acquisition, Modelling and Management (October 1999)Google Scholar
  8. 8.
    Liu, B., Hsu, W., Chen, S.: Using General Impressions to Analyze Discovered Classification Rules. In: Proceedings 3rd KDD conference, pp. 31–36 (August 1997)Google Scholar
  9. 9.
    Liu, B., Hsu, W., Mun, L.-F., Lee, H.-Y.: Finding Interesting Patterns using User Expectations. Knowledge and Data Engineering 11(6), 817–832 (1999)CrossRefGoogle Scholar
  10. 10.
    Geng, L., Hamilton, H.J.: Interestingness measures for data mining: A survey. ACM Comput. Surv. 38(3) (2006)Google Scholar
  11. 11.
    Mcgarry, K.: A Survey of Interestingness Measures for Knowledge Discovery. The knowledge engineering review, 1–24 (2005)Google Scholar
  12. 12.
    Pasquier, N., Taouil, R., Bastide, Y., Stumme, G., Lakhal, L.: Generating a Condensed Representation for Association Rules. Journal of Intelligent Information Systems. In: Kerschberg, L., Ras, Z., Zemankova, M. (eds.) Kluwer Academic PublishersGoogle Scholar
  13. 13.
    Piatetsky-Shapiro, G., Matheus, C.: The Interestingness of Deviations. In: Proceedings of the AAAI-94 workshop on Knowledge Discovery in Databases (1994)Google Scholar
  14. 14.
    Silberschatz, A., Tuzhilin, A.: On Subjective Measures of Interestingness in Knowledge Discovery. In: Proceedings 1st KDD conference, pp. 275–281 (August 1995)Google Scholar
  15. 15.
    Silberschatz, A., Tuzhilin, A.: What Makes Patterns Interesting in Knowledge Discovery Systems. IEEE Transaction On Knowledge And Data Engineering 8(6), 970–974 (1996)CrossRefGoogle Scholar
  16. 16.
    Stevens, R., Goble, C.A., Bechhofer, S.: Ontology-based Knowledge Representation for Bioinformatics. Brief Bioinformatics 1(4), 398–414 (2000)CrossRefGoogle Scholar
  17. 17.
    Stojanovic, L., Stojanovic, N., Volz, R.: Migrating Data-intensive Web Sites into the Semantic Web. In: Proceedings 17th ACM Symposium on Applied Computing, pp. 1100–1107. ACM Press, New York (2002)Google Scholar
  18. 18.
    Stumme, G.: Conceptual On-Line Analytical Processing. In: Tanaka, K., Ghandeharizadeh, S., Kambayashi, Y. (eds.) Information Organization and Databases, vol. 14, pp. 191–203. Kluwer Academic Publishers, Dordrecht (2000)Google Scholar
  19. 19.
    Tiffin, N., Kelso, J.F., Powell, A.R., Pan, H., Bajic, V.B., Hide, W.A.: Integration of Text- and Data-Mining using Ontologies Successfully Selects Disease Gene Candidates. Nucleic Acids Research 33(5), 1544–1552 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Laurent Brisson
    • 1
  1. 1.Laboratoire I3S - Université de Nice, 06903 Sophia-AntipolisFrance

Personalised recommendations