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Foundations of Onto-Relational Learning

  • Conference paper
Inductive Logic Programming (ILP 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5194))

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Abstract

ILP is a major approach to Relational Learning that exploits previous results in concept learning and is characterized by the use of prior conceptual knowledge. An increasing amount of conceptual knowledge is being made available in the form of ontologies, mainly formalized with Description Logics (DLs). In this paper we consider the problem of learning rules from observations that combine relational data and ontologies, and identify the ingredients of an ILP solution to it. Our proposal relies on the expressive and deductive power of the KR framework \(\mathcal{DL}\)+log that allows for the tight integration of DLs and disjunctive Datalog with negation. More precisely we adopt an instantiation of this framework which integrates the DL \(\mathcal{SHIQ}\) and positive Datalog. We claim that this proposal lays the foundations of an extension of Relational Learning, called Onto-Relational Learning, to account for ontologies.

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References

  1. Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P.F. (eds.): The Description Logic Handbook: Theory, Implementation and Applications. Cambridge University Press, Cambridge (2003)

    MATH  Google Scholar 

  2. Borgida, A.: On the relative expressiveness of description logics and predicate logics. Artificial Intelligence 82(1–2), 353–367 (1996)

    Article  MathSciNet  Google Scholar 

  3. Buntine, W.: Generalized subsumption and its application to induction and redundancy. Artificial Intelligence 36(2), 149–176 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  4. Ceri, S., Gottlob, G., Tanca, L.: What you always wanted to know about datalog (and never dared to ask). IEEE Transactions on Knowledge and Data Engineering 1(1), 146–166 (1989)

    Article  Google Scholar 

  5. Ceri, S., Gottlob, G., Tanca, L.: Logic Programming and Databases. Springer, Heidelberg (1990)

    Google Scholar 

  6. De Raedt, L., Džeroski, S.: First order jk-clausal theories are PAC-learnable. Artificial Intelligence 70, 375–392 (1994)

    Article  MATH  MathSciNet  Google Scholar 

  7. 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)

    Article  Google Scholar 

  8. Eiter, T., Gottlob, G., Mannila, H.: Disjunctive Datalog. ACM Transactions on Database Systems 22(3), 364–418 (1997)

    Article  Google Scholar 

  9. Frisch, A.M., Cohn, A.G.: Thoughts and afterthoughts on the 1988 workshop on principles of hybrid reasoning. AI Magazine 11(5), 84–87 (1991)

    Google Scholar 

  10. Gelfond, M., Lifschitz, V.: Classical negation in logic programs and disjunctive databases. New Generation Computing 9(3/4), 365–386 (1991)

    Article  Google Scholar 

  11. 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)

    MathSciNet  Google Scholar 

  12. Gómez-Pérez, A., Fernández-López, M., Corcho, O.: Ontological Engineering. Springer, Heidelberg (2004)

    Google Scholar 

  13. Gruber, T.: A translation approach to portable ontology specifications. Knowledge Acquisition 5, 199–220 (1993)

    Article  Google Scholar 

  14. Horrocks, I., Patel-Schneider, P.F., 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 

  15. Horrocks, I., Sattler, U., Tobies, S.: Practical reasoning for very expressive description logics. Logic Journal of the IGPL 8(3), 239–263 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  16. 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 

  17. Levy, A.Y., Rousset, M.-C.: Combining Horn rules and description logics in CARIN. Artificial Intelligence 104, 165–209 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  18. Lisi, F.A.: 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)

    Article  MATH  MathSciNet  Google Scholar 

  19. Lisi, F.A., 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 

  20. Lisi, F.A., 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, vol. 2829, pp. 49–60. Springer, Heidelberg (2003)

    Google Scholar 

  21. Lisi, F.A., 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 

  22. Lisi, F.A., Malerba, D.: Inducing Multi-Level Association Rules from Multiple Relations. Machine Learning 55, 175–210 (2004)

    Article  MATH  Google Scholar 

  23. Motik, B., Sattler, U., Studer, R.: Query Answering for OWL-DL with Rules. Journal on Web Semantics 3(1), 41–60 (2005)

    Google Scholar 

  24. Reiter, R.: Equality and domain closure in first order databases. Journal of ACM 27, 235–249 (1980)

    Article  MATH  MathSciNet  Google Scholar 

  25. Rosati, R.: Towards expressive KR systems integrating Datalog and description logics: preliminary report. In: Lambrix, P., Borgida, A., Lenzerini, M., Möller, R., Patel-Schneider, P.F. (eds.) Proceedings of the 1999 International Workshop on Description Logics (DL 1999). CEUR Workshop Proceedings (1999)

    Google Scholar 

  26. Rosati, R.: On the decidability and complexity of integrating ontologies and rules. Journal of Web Semantics 3(1) (2005)

    Google Scholar 

  27. 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)

    Chapter  Google Scholar 

  28. Rosati, R.: \(\mathcal{DL}\)+log: Tight integration of description logics and disjunctive datalog. In: Doherty, P., Mylopoulos, J., Welty, C.A. (eds.) Proc. of Tenth International Conference on Principles of Knowledge Representation and Reasoning, pp. 68–78. AAAI Press, Menlo Park (2006)

    Google Scholar 

  29. 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)

    Chapter  Google Scholar 

  30. Schmidt-Schauss, M., Smolka, G.: Attributive concept descriptions with complements. Artificial Intelligence 48(1), 1–26 (1991)

    Article  MATH  MathSciNet  Google Scholar 

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Filip Železný Nada Lavrač

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Lisi, F.A., Esposito, F. (2008). Foundations of Onto-Relational Learning. In: Železný, F., Lavrač, N. (eds) Inductive Logic Programming. ILP 2008. Lecture Notes in Computer Science(), vol 5194. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85928-4_15

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  • DOI: https://doi.org/10.1007/978-3-540-85928-4_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85927-7

  • Online ISBN: 978-3-540-85928-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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