Abstract
In this paper we carry on the work on Onto-Relational Learning by investigating the impact of having disjunctive Datalog with default negation either in the language of hypotheses or in the language for the background theory. The inclusion of nonmonotonic features strengthens the ability of our ILP framework to deal with incomplete knowledge. One such ability can turn out to be useful in application domains, such as the Semantic Web. As a showcase we face the problem of inducing an integrity theory for a relational database whose instance is given and whose schema encompasses an ontology and a set of rules linking the database to the ontology.
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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, 2nd edn. Cambridge University Press, Cambridge (2007)
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)
De Raedt, L., Bruynooghe, M.: A theory of clausal discovery. In: IJCAI, pp. 1058–1063 (1993)
Eiter, T., Gottlob, G., Mannila, H.: Disjunctive Datalog. ACM Transactions on Database Systems 22(3), 364–418 (1997)
Gelfond, M., Lifschitz, V.: Classical negation in logic programs and disjunctive databases. New Generation Computing 9(3/4), 365–386 (1991)
Gruber, T.: A translation approach to portable ontology specifications. Knowledge Acquisition 5, 199–220 (1993)
Horrocks, I., Sattler, U., Tobies, S.: Practical reasoning for very expressive description logics. Logic Journal of the IGPL 8(3), 239–263 (2000)
Lisi, F.A., 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)
Plotkin, G.D.: A further note on inductive generalization. Machine Intelligence 6, 101–121 (1971)
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 10th Int. Conf. on Principles of Knowledge Representation and Reasoning, pp. 68–78. AAAI Press, Menlo Park (2006)
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Lisi, F.A., Esposito, F. (2010). Nonmonotonic Onto-Relational Learning. In: De Raedt, L. (eds) Inductive Logic Programming. ILP 2009. Lecture Notes in Computer Science(), vol 5989. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13840-9_9
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DOI: https://doi.org/10.1007/978-3-642-13840-9_9
Publisher Name: Springer, Berlin, Heidelberg
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