DL-FOIL Concept Learning in Description Logics

  • Nicola Fanizzi
  • Claudia d’Amato
  • Floriana Esposito
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5194)


In this paper we focus on learning concept descriptions expressed in Description Logics. After stating the learning problem in this context, a FOIL-like algorithm is presented that can be applied to general DL languages, discussing related theoretical aspects of learning with the inherent incompleteness underlying the semantics of this representation. Subsequently we present an experimental evaluation of the implementation of this algorithm performed on some real ontologies in order to empirically assess its performance.


Description Logic Target Concept Gain Function Concept Description Real Ontology 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P. (eds.): The Description Logic Handbook. Cambridge University Press, Cambridge (2003)zbMATHGoogle Scholar
  2. 2.
    Baader, F., Ganter, B., Sertkaya, B., Sattler, U.: Completing description logic knowledge bases using formal concept analysis. In: Veloso, M. (ed.) Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, pp. 230–235 (2007)Google Scholar
  3. 3.
    Berners-Lee, T., Hendler, J., Lassila, O.: The Semantic Web. Scientific American 284(5), 34–43 (2001)CrossRefGoogle Scholar
  4. 4.
    Borgida, A.: On the relative expressiveness of description logics and predicate logics. Artificial Intelligence 82(1–2), 353–367 (1996)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Brandt, S., Küsters, R., Turhan, A.-Y.: Approximation and difference in description logics. In: Fensel, D., Giunchiglia, F., McGuinness, D., Williams, M.-A. (eds.) Proceedings of the International Conference on Knowledge Representation, pp. 203–214. Morgan Kaufmann, San Francisco (2002)Google Scholar
  6. 6.
    Cohen, W.W., Hirsh, H.: Learnability of description logics. In: Proceedings of the Fourth Annual Workshop on Computational Learning Theory, Pittsburgh, PA. ACM Press, New York (1992)Google Scholar
  7. 7.
    Cohen, W.W., Hirsh, H.: Learning the CLASSIC description logic. In: Torasso, P., Doyle, J., Sandewall, E. (eds.) Proceedings of the 4th International Conference on the Principles of Knowledge Representation and Reasoning, pp. 121–133. Morgan Kaufmann, San Francisco (1994)Google Scholar
  8. 8.
    d’Amato, C., Fanizzi, N., Esposito, F.: Query answering and ontology population: An inductive approach. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 288–302. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Dean, M., Schreiber, G.: Web Ontology Language Reference. W3C recommendation, W3C (2004),
  10. 10.
    Esposito, F., Fanizzi, N., Iannone, L., Palmisano, I., Semeraro, G.: Knowledge-intensive induction of terminologies from metadata. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 441–455. Springer, Heidelberg (2004)Google Scholar
  11. 11.
    Goldman, S.A., Kwek, S., Scott, S.D.: Learning from examples with unspecified attribute values. Information and Computation 180(2), 82–100 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Grosof, B.N., Horrocks, I., Volz, R., Decker, S.: Description logic programs: combining logic programs with description logic. In: Proceedings of the 12th international conference on World Wide Web, WWW 2003, pp. 48–57. ACM Press, New York (2003)CrossRefGoogle Scholar
  13. 13.
    Iannone, L., Palmisano, I., Fanizzi, N.: An algorithm based on counterfactuals for concept learning in the semantic web. Applied Intelligence 26(2), 139–159 (2007)CrossRefGoogle Scholar
  14. 14.
    Inuzuka, N., Kamo, M., Ishii, N., Seki, H., Itoh, H.: Tow-down induction of logic programs from incomplete samples. In: Muggleton, S. (ed.) ILP 1996. LNCS, vol. 1314, pp. 265–282. Springer, Heidelberg (1997)Google Scholar
  15. 15.
    Kietz, J.-U.: Learnability of description logic programs. In: Matwin, S., Sammut, C. (eds.) ILP 2002. LNCS (LNAI), vol. 2583, pp. 117–132. Springer, Heidelberg (2003)Google Scholar
  16. 16.
    Kietz, J.-U., Morik, K.: A polynomial approach to the constructive induction of structural knowledge. Machine Learning 14(2), 193–218 (1994)zbMATHCrossRefGoogle Scholar
  17. 17.
    Lehmann, J., Hitzler, P.: Foundations of refinement operators for description logics. In: Blockeel, H., Ramon, J., Shavlik, J., Tadepalli, P. (eds.) ILP 2007. LNCS (LNAI), vol. 4894, pp. 161–174. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  18. 18.
    Lehmann, J., Hitzler, P.: A refinement operator based learning algorithm for the \({\cal ALC}\) description logic. In: Blockeel, H., Ramon, J., Shavlik, J., Tadepalli, P. (eds.) ILP 2007. LNCS (LNAI), vol. 4894, pp. 147–160. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  19. 19.
    Lisi, F.A.: Principles of inductive reasoning on the Semantic Web: A framework for learning in \(\mathcal{AL}\)-Log. In: Fages, F., Soliman, S. (eds.) PPSWR 2005. LNCS, vol. 3703, pp. 118–132. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  20. 20.
    Quinlan, R.: Learning logical definitions from relations. Machine Learning 5, 239–266 (1990)Google Scholar
  21. 21.
    Rouveirol, C., Ventos, V.: Towards learning in CARIN-\(\mathcal{ALN}\). In: Cussens, J., Frisch, A.M. (eds.) ILP 2000. LNCS (LNAI), vol. 1866, pp. 191–208. Springer, Heidelberg (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Nicola Fanizzi
    • 1
  • Claudia d’Amato
    • 1
  • Floriana Esposito
    • 1
  1. 1.LACAM – Dipartimento di InformaticaUniversità degli studi di BariBariItaly

Personalised recommendations