On Ontologies as Prior Conceptual Knowledge in Inductive Logic Programming

  • Francesca A. Lisi
  • Floriana Esposito
Part of the Studies in Computational Intelligence book series (SCI, volume 220)

Abstract

In this paper we consider the problem of having ontologies as prior conceptual knowledge in Inductive Logic Programming (ILP). In particular, we take a critical look at three ILP proposals based on knowledge representation frameworks that integrate Description Logics and Horn Clausal Logic. From the comparative analysis of the three, we draw general conclusions that can be considered as guidelines for an upcoming Onto-Relational Learning aimed at extending Relational Learning to account for ontologies.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P. (eds.): The Description Logic Handbook: Theory, Implementation and Applications. Cambridge University Press, Cambridge (2003)MATHGoogle Scholar
  2. 2.
    Berners-Lee, T., Hendler, J., Lassila, O.: The Semantic Web. Scientific American (May 2001)Google Scholar
  3. 3.
    Borgida, A.: On the relative expressiveness of description logics and predicate logics. Artificial Intelligence 82(1–2), 353–367 (1996)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Buntine, W.: Generalized subsumption and its application to induction and redundancy. Artificial Intelligence 36(2), 149–176 (1988)MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Calvanese, D., Lenzerini, M., Rosati, R., Vetere, G.: Dl-lite: Practical reasoning for rich dls. In: Haarslev, V., Möller, R. (eds.) Proceedings of the 2004 International Workshop on Description Logics (DL 2004), CEUR Workshop Proceedings, vol. 104. CEUR-WS.org (2004)Google Scholar
  6. 6.
    Ceri, S., Gottlob, G., Tanca, L.: Logic Programming and Databases. Springer, Heidelberg (1990)Google Scholar
  7. 7.
    Chandrasekaran, B., Josephson, J., Benjamins, V.: What are ontologies, and why do we need them? IEEE Intelligent Systems 14(1), 20–26 (1999)CrossRefGoogle Scholar
  8. 8.
    De Raedt, L.: Logical Settings for Concept-Learning. Artificial Intelligence 95(1), 187–201 (1997)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    De Raedt, L., Dehaspe, L.: Clausal Discovery. Machine Learning 26(2–3), 99–146 (1997)MATHCrossRefGoogle Scholar
  10. 10.
    Donini, F., 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
  11. 11.
    Eiter, T., Gottlob, G., Mannila, H.: Disjunctive Datalog. ACM Transactions on Database Systems 22(3), 364–418 (1997)CrossRefGoogle Scholar
  12. 12.
    Frisch, A.: The substitutional framework for sorted deduction: Fundamental results on hybrid reasoning. Artificial Intelligence 49, 161–198 (1991)MATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Frisch, A.: Sorted downward refinement: Building background knowledge into a refinement operator for inductive logic programming. In: Džeroski, S., Flach, P.A. (eds.) ILP 1999. LNCS (LNAI), vol. 1634, pp. 104–115. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  14. 14.
    Frisch, A., Cohn, A.: Thoughts and afterthoughts on the 1988 workshop on principles of hybrid reasoning. AI Magazine 11(5), 84–87 (1991)Google Scholar
  15. 15.
    Gelfond, M., Lifschitz, V.: Classical negation in logic programs and disjunctive databases. New Generation Computing 9(3/4), 365–386 (1991)CrossRefGoogle Scholar
  16. 16.
    Glimm, B., Horrocks, I., Lutz, C., Sattler, U.: Conjunctive query answering for the description logic \(\mathcal{SHIQ}\). Journal of Artificial Intelligence Research 31, 151–198 (2008)MathSciNetGoogle Scholar
  17. 17.
    Gómez-Pérez, A., Fernández-López, M., Corcho, O.: Ontological Engineering. Springer, Heidelberg (2004)Google Scholar
  18. 18.
    Gruber, T.: A translation approach to portable ontology specifications. Knowledge Acquisition 5, 199–220 (1993)CrossRefGoogle Scholar
  19. 19.
    Horrocks, I., Patel-Schneider, P., van Harmelen, F.: From \(\mathcal{SHIQ}\) and RDF to OWL: The making of a web ontology language. Journal of Web Semantics 1(1), 7–26 (2003)Google Scholar
  20. 20.
    Horrocks, I., Sattler, U., Tobies, S.: Practical reasoning for very expressive description logics. Logic Journal of the IGPL 8(3), 239–263 (2000)MATHCrossRefMathSciNetGoogle Scholar
  21. 21.
    Kietz, J.: 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
  22. 22.
    van der Laag, P.: An analysis of refinement operators in inductive logic programming. Ph.D. Thesis, Erasmus University, Rotterdam, The Netherlands (1995)Google Scholar
  23. 23.
    Levy, A., Rousset, M.C.: Combining Horn rules and description logics in CARIN. Artificial Intelligence 104, 165–209 (1998)MATHCrossRefMathSciNetGoogle Scholar
  24. 24.
    Lisi, F.: Building Rules on Top of Ontologies for the Semantic Web with Inductive Logic Programming. Theory and Practice of Logic Programming 8(03), 271–300 (2008)MATHCrossRefMathSciNetGoogle Scholar
  25. 25.
    Lisi, F., Esposito, F.: Efficient Evaluation of Candidate Hypotheses in \(\mathcal{AL}\)-log. In: Camacho, R., King, R., Srinivasan, A. (eds.) ILP 2004. LNCS (LNAI), vol. 3194, pp. 216–233. Springer, Heidelberg (2004)Google Scholar
  26. 26.
    Lisi, F., Esposito, F.: Foundations of Onto-Relational Learning. In: Železný, F., Lavrač, N. (eds.) ILP 2008. LNCS (LNAI), vol. 5194, pp. 158–175. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  27. 27.
    Lisi, F., Esposito, F.: Learning \(\mathcal{SHIQ}\)+log Rules for Ontology Evolution. In: Gangemi, A., Keizer, J., Presutti, V., Stoermer, H. (eds.) Semantic Web Applications and Perspectives (SWAP 2008), CEUR Workshop Proceedings, vol. 426 (2008)Google Scholar
  28. 28.
    Lisi, F., Malerba, D.: Bridging the Gap between Horn Clausal Logic and Description Logics in Inductive Learning. In: Cappelli, A., Turini, F. (eds.) AI*IA 2003. LNCS (LNAI), vol. 2829, pp. 49–60. Springer, Heidelberg (2003)Google Scholar
  29. 29.
    Lisi, F., Malerba, D.: Ideal Refinement of Descriptions in \(\mathcal{AL}\)-log. In: Horváth, T., Yamamoto, A. (eds.) ILP 2003. LNCS (LNAI), vol. 2835, pp. 215–232. Springer, Heidelberg (2003)Google Scholar
  30. 30.
    Lisi, F., Malerba, D.: Inducing Multi-Level Association Rules from Multiple Relations. Machine Learning 55, 175–210 (2004)MATHCrossRefGoogle Scholar
  31. 31.
    Lloyd, J.: Foundations of Logic Programming, 2nd edn. Springer, Heidelberg (1987)MATHGoogle Scholar
  32. 32.
    Michalski, R.: A theory and methodology of inductive learning. In: Michalski, R., Carbonell, J., Mitchell, T. (eds.) Machine Learning: an artificial intelligence approach, vol. I. Morgan Kaufmann, San Mateo (1983)Google Scholar
  33. 33.
    Mitchell, T.: Generalization as search. Artificial Intelligence 18, 203–226 (1982)CrossRefMathSciNetGoogle Scholar
  34. 34.
    Motik, B., Sattler, U., Studer, R.: Query Answering for OWL-DL with Rules. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 549–563. Springer, Heidelberg (2004)Google Scholar
  35. 35.
    Nédellec, C., Rouveirol, C., Adé, H., Bergadano, F., Tausend, B.: Declarative bias in ILP. In: Raedt, L.D. (ed.) Advances in Inductive Logic Programming, pp. 82–103. IOS Press, Amsterdam (1996)Google Scholar
  36. 36.
    Nienhuys-Cheng, S., de Wolf, R.: Foundations of Inductive Logic Programming. In: Nienhuys-Cheng, S.-H., de Wolf, R. (eds.) Foundations of Inductive Logic Programming. LNCS (LNAI), vol. 1228. Springer, Heidelberg (1997)Google Scholar
  37. 37.
    Plotkin, G.: A note on inductive generalization. Machine Intelligence 5, 153–163 (1970)MathSciNetGoogle Scholar
  38. 38.
    Plotkin, G.: A further note on inductive generalization. Machine Intelligence 6, 101–121 (1971)MATHMathSciNetGoogle Scholar
  39. 39.
    Reiter, R.: Equality and domain closure in first order databases. Journal of ACM 27, 235–249 (1980)MATHCrossRefMathSciNetGoogle Scholar
  40. 40.
    Rosati, R.: On the decidability and complexity of integrating ontologies and rules. Journal of Web Semantics 3(1) (2005)Google Scholar
  41. 41.
    Rosati, R.: Semantic and computational advantages of the safe integration of ontologies and rules. In: Fages, F., Soliman, S. (eds.) PPSWR 2005. LNCS, vol. 3703, pp. 50–64. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  42. 42.
    Rosati, R.: \(\mathcal{DL}\)+log: Tight integration of description logics and disjunctive datalog. In: Doherty, P., Mylopoulos, J., Welty, C. (eds.) Proc. of Tenth International Conference on Principles of Knowledge Representation and Reasoning, pp. 68–78. AAAI Press, Menlo Park (2006)Google Scholar
  43. 43.
    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
  44. 44.
    Schmidt-Schauss, M., Smolka, G.: Attributive concept descriptions with complements. Artificial Intelligence 48(1), 1–26 (1991)MATHCrossRefMathSciNetGoogle Scholar
  45. 45.
    Shapiro, E.: Inductive inference of theories from facts. Technical Report 624, Dept. of Computer Science. Yale University (1981)Google Scholar
  46. 46.
    Utgoff, P., Mitchell, T.: Acquisition of appropriate bias for inductive concept learning. In: Proceedings of the 2nd National Conference on Artificial Intelligence, pp. 414–418. Morgan Kaufmann, Los Altos (1982)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Francesca A. Lisi
    • 1
  • Floriana Esposito
    • 1
  1. 1.Dipartimento di InformaticaUniversità degli Studi di BariBariItaly

Personalised recommendations