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
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)
Borgida, A.: On the relative expressiveness of description logics and predicate logics. Artificial Intelligence 82(1–2), 353–367 (1996)
Buntine, W.: Generalized subsumption and its application to induction and redundancy. Artificial Intelligence 36(2), 149–176 (1988)
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)
Ceri, S., Gottlob, G., Tanca, L.: Logic Programming and Databases. Springer, Heidelberg (1990)
De Raedt, L., Džeroski, S.: First order jk-clausal theories are PAC-learnable. Artificial Intelligence 70, 375–392 (1994)
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)
Eiter, T., Gottlob, G., Mannila, H.: Disjunctive Datalog. ACM Transactions on Database Systems 22(3), 364–418 (1997)
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)
Gelfond, M., Lifschitz, V.: Classical negation in logic programs and disjunctive databases. New Generation Computing 9(3/4), 365–386 (1991)
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)
Gómez-Pérez, A., Fernández-López, M., Corcho, O.: Ontological Engineering. Springer, Heidelberg (2004)
Gruber, T.: A translation approach to portable ontology specifications. Knowledge Acquisition 5, 199–220 (1993)
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)
Horrocks, I., Sattler, U., Tobies, S.: Practical reasoning for very expressive description logics. Logic Journal of the IGPL 8(3), 239–263 (2000)
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)
Levy, A.Y., Rousset, M.-C.: Combining Horn rules and description logics in CARIN. Artificial Intelligence 104, 165–209 (1998)
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)
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)
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)
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)
Lisi, F.A., Malerba, D.: Inducing Multi-Level Association Rules from Multiple Relations. Machine Learning 55, 175–210 (2004)
Motik, B., Sattler, U., Studer, R.: Query Answering for OWL-DL with Rules. Journal on Web Semantics 3(1), 41–60 (2005)
Reiter, R.: Equality and domain closure in first order databases. Journal of ACM 27, 235–249 (1980)
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)
Rosati, R.: On the decidability and complexity of integrating ontologies and rules. Journal of Web Semantics 3(1) (2005)
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)
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)
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)
Schmidt-Schauss, M., Smolka, G.: Attributive concept descriptions with complements. Artificial Intelligence 48(1), 1–26 (1991)
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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
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