Advertisement

On the Missing Link Between Frequent Pattern Discovery and Concept Formation

  • Francesca A. Lisi
  • Floriana Esposito
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4455)

Abstract

Concept Formation is a unsupervised learning task usually decomposed into the two subtasks of clustering and characterization. This paper presents a novel approach to Concept Formation in First Order Logic (FOL) which adopts a pattern-based approach to clustering and a bias-based approach to characterization. The resulting method extends therefore the levelwise search method for Frequent Pattern Discovery. The FOL fragment chosen is \(\mathcal{AL}\)-log, a hybrid language that merges the description logic \(\mathcal{ALC}\) and the clausal logic Datalog and turns out to be suitable for applications in the context of Ontology Refinement. Indeed the method returns a taxonomy rooted into the concept that occurs in an existing taxonomic ontology and needs to be refined in the light of new knowledge coming from an external data source. Experimental results have been obtained on an \(\mathcal{ALC}\) ontology enriched with Datalog data extracted from the on-line CIA World Fact Book.

Keywords

Directed Acyclic Graph Frequent Pattern Concept Formation First Order Logic Formal Concept Analysis 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bisson, G., Nedellec, C., Cañamero, D.: Designing clustering methods for ontology building - the Mo’K workbench. In: Staab, S., Maedche, A., Nedellec, C., Wiemer-Hastings, P. (eds.) ECAI Workshop on Ontology Learning, vol. 31, CEUR Workshop Proceedings. CEUR-WS.org (2000)Google Scholar
  2. 2.
    Ceri, S., Gottlob, G., Tanca, L.: Logic Programming and Databases. Springer, Heidelberg (1990)Google Scholar
  3. 3.
    Donini, F.M., Lenzerini, M., Nardi, D., Schaerf, A.: \(\mathcal{AL}\)-log: Integrating Datalog and Description Logics. Journal of Intelligent Information Systems 10(3), 227–252 (1998)CrossRefGoogle Scholar
  4. 4.
    Fisher, D.H.: Knowledge acquisition via incremental conceptual clustering. Machine Learning 2(2), 139–172 (1987)Google Scholar
  5. 5.
    Ganter, B., Stumme, G., Wille, R. (eds.): Formal Concept Analysis. LNCS (LNAI), vol. 3626. Springer, Heidelberg (2005)Google Scholar
  6. 6.
    Gennari, J.H., Langley, P., Fisher, D.: Models of incremental concept formation. Artificial Intelligence 40(1-3), 11–61 (1989)CrossRefGoogle Scholar
  7. 7.
    Gómez-Pérez, A., Fernández-López, M., Corcho, O.: Ontological Engineering. Springer, Heidelberg (2004)Google Scholar
  8. 8.
    Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On clustering validation techniques. Journal of Intelligent Information Systems 17(2-3), 107–145 (2001)zbMATHCrossRefGoogle Scholar
  9. 9.
    Han, J., Fu, Y.: Mining multiple-level association rules in large databases. IEEE Transactions on Knowledge and Data Engineering 11(5) (1999)Google Scholar
  10. 10.
    Hartigan, J.A.: Statistical clustering. In: Smelser, N.J., Baltes, P.B. (eds.) International Encyclopedia of the Social and Behavioral Sciences, pp. 15014–15019. Oxford Press, Oxford (2001)Google Scholar
  11. 11.
    Langley, P.: Machine learning and concept formation. Machine Learning 2(2), 99–102 (1987)MathSciNetGoogle Scholar
  12. 12.
    Lisi, F.A.: A Pattern-Based Approach to Conceptual Clustering in FOL. In: Schärfe, H., Hitzler, P., Øhrstrøm, P. (eds.) ICCS 2006. LNCS (LNAI), vol. 4068, pp. 346–359. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Lisi, F.A., Esposito, F.: ILP Meets Knowledge Engineering: A Case Study. In: Kramer, S., Pfahringer, B. (eds.) ILP 2005. LNCS (LNAI), vol. 3625, pp. 209–226. Springer, Heidelberg (2005)Google Scholar
  14. 14.
    Lisi, F.A., Esposito, F.: Two Orthogonal Biases for Choosing the Intensions of Emerging Concepts in Ontology Refinement. In: Brewka, G., Coradeschi, S., Perini, A., Traverso, P. (eds.) ECAI 2006. Proceedings of the 17th European Conference on Artificial Intelligence, pp. 765–766. IOS Press, Amsterdam (2006)Google Scholar
  15. 15.
    Lisi, F.A., Malerba, D.: Inducing Multi-Level Association Rules from Multiple Relations. Machine Learning 55, 175–210 (2004)zbMATHCrossRefGoogle Scholar
  16. 16.
    Maedche, A., Staab, S.: Discovering Conceptual Relations from Text. In: Horn, W. (ed.) Proceedings of the 14th European Conference on Artificial Intelligence, pp. 321–325. IOS Press, Amsterdam (2000)Google Scholar
  17. 17.
    Maedche, A., Staab, S.: Measuring similarity between ontologies. In: Gómez-Pérez, A., Benjamins, V.R. (eds.) EKAW 2002. LNCS (LNAI), vol. 2473, pp. 251–263. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  18. 18.
    Maedche, A., Staab, S.: Ontology Learning. In: Staab, S., Studer, R. (eds.) Handbook on Ontologies, Springer, Heidelberg (2004)Google Scholar
  19. 19.
    Maedche, A., Zacharias, V.: Clustering Ontology-Based Metadata in the Semantic Web. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, pp. 348–360. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  20. 20.
    Mannila, H., Toivonen, H.: Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery 1(3), 241–258 (1997)CrossRefGoogle Scholar
  21. 21.
    Medin, D., Smith, E.: Concepts and concept formation. Annual Review of Psychology 35, 113–138 (1984)CrossRefGoogle Scholar
  22. 22.
    Michalski, R.S., Stepp, R.E.: Learning from observation: Conceptual clustering. In: Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (eds.) Machine Learning: an artificial intelligence approach, Morgan Kaufmann, San Francisco (1983)Google Scholar
  23. 23.
    Nienhuys-Cheng, S.-H., de Wolf, R. (eds.): Foundations of Inductive Logic Programming. LNCS, vol. 1228. Springer, Heidelberg (1997)Google Scholar
  24. 24.
    Rey, G.: Concepts and stereotypes. Cognition 15, 237–262 (1983)CrossRefGoogle Scholar
  25. 25.
    Schmidt-Schauss, M., Smolka, G.: Attributive concept descriptions with complements. Artificial Intelligence 48(1), 1–26 (1991)zbMATHCrossRefMathSciNetGoogle Scholar
  26. 26.
    Semeraro, G., Esposito, F., Malerba, D., Fanizzi, N., Ferilli, S.: A logic framework for the incremental inductive synthesis of Datalog theories. In: Fuchs, N.E. (ed.) LOPSTR 1997. LNCS, vol. 1463, pp. 300–321. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  27. 27.
    Stumme, G.: Iceberg query lattices for Datalog. In: Wolff, K.E., Pfeiffer, H.D., Delugach, H.S. (eds.) ICCS 2004. LNCS (LNAI), vol. 3127, pp. 109–125. Springer, Heidelberg (2004)Google Scholar
  28. 28.
    Vrain, C.: Hierarchical conceptual clustering in a first order representation. In: Michalewicz, M., Raś, Z.W. (eds.) ISMIS 1996. LNCS, vol. 1079, pp. 643–652. Springer, Heidelberg (1996)Google Scholar
  29. 29.
    Xiong, H., Steinbach, M., Ruslim, A., Kumar, V.: Characterizing pattern based clustering. Technical Report TR 05-015, Dept. of Computer Science and Engineering, University of Minnesota, Minneapolis, USA (2005)Google Scholar
  30. 30.
    Zimmermann, A., De Raedt, L.: Cluster-grouping: From subgroup discovery to clustering. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 575–577. Springer, Heidelberg (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Francesca A. Lisi
    • 1
  • Floriana Esposito
    • 1
  1. 1.Dipartimento di Informatica, Università degli Studi di Bari, Via Orabona 4, 70125 BariItaly

Personalised recommendations