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Inductive Logic Programming in Databases: From Datalog to

Published online by Cambridge University Press:  20 May 2010

FRANCESCA A. LISI
FRANCESCA A. LISI*
Affiliation:
Dipartimento di Informatica, Università degli Studi di Bari “Aldo Moro”, Italy (e-mail: lisi@di.uniba.it)
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Abstract

In this paper we address an issue that has been brought to the attention of the database community with the advent of the Semantic Web, i.e., the issue of how ontologies (and semantics conveyed by them) can help solving typical database problems, through a better understanding of Knowledge Representation (KR) aspects related to databases. In particular, we investigate this issue from the ILP perspective by considering two database problems, (i) the definition of views and (ii) the definition of constraints, for a database whose schema is represented also by means of an ontology. Both can be reformulated as ILP problems and can benefit from the expressive and deductive power of the KR framework . We illustrate the application scenarios by means of examples.

Keywords

Inductive Logic Programmingrelational databasesontologiesdescription logicshybrid Knowledge Representation and reasoning systems
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 10 , Special Issue 3: Logic Programming in Databases: from Datalog to Semantic-Web Rules , May 2010 , pp. 331 - 359
Copyright
Copyright © Cambridge University Press 2010

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References

Baader, F., Calvanese, D., McGuinness, D., Nardi, D. and Patel-Schneider, P., Eds. 2007. The Description Logic Handbook: Theory, Implementation and Applications, 2nd ed., Cambridge University Press.CrossRefGoogle Scholar
Berners-Lee, T., Hendler, J. and Lassila, O. 2001. The semantic web. Scientific American May.CrossRefGoogle Scholar
Blockeel, H., De Raedt, L., Jacobs, N. and Demoen, B. 1999. Scaling up inductive logic programming by learning from interpretations. Data Mining and Knowledge Discovery 3, 59–93.CrossRefGoogle Scholar
Borgida, A. 1996. On the relative expressiveness of description logics and predicate logics. Artificial Intelligence 82, 1–2, 353–367.CrossRefGoogle Scholar
Buntine, W. 1988. Generalized subsumption and its application to induction and redundancy. Artificial Intelligence 36, 2, 149–176.CrossRefGoogle Scholar
Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M. and Rosati, R. 2007. Tractable reasoning and efficient query answering in description logics: The dl-lite family. Journal of Automated Reasoning 39, 3, 385–429.CrossRefGoogle Scholar
Calvanese, D., De Giacomo, G. and Lenzerini, M. 2008. Conjunctive query containment and answering under description logics constraints. ACM Transactions on Computational Logic 9, 3.CrossRefGoogle Scholar
Ceri, S., Gottlob, G. and Tanca, L. 1989. What you always wanted to know about datalog (and never dared to ask). IEEE Transactions on Knowledge and Data Engineering 1, 1, 146–166.CrossRefGoogle Scholar
Ceri, S., Gottlob, G. and Tanca, L. 1990. Logic Programming and Databases. Springer.CrossRefGoogle Scholar
De Raedt, L. and Bruynooghe, M. 1993. A theory of clausal discovery. In IJCAI. 1058–1063.Google Scholar
De Raedt, L. and Dehaspe, L. 1997. Clausal discovery. Machine Learning 26, 2–3, 99–146.CrossRefGoogle Scholar
De Raedt, L. and Džeroski, S. 1994. First order jk-clausal theories are PAC-learnable. Artificial Intelligence 70, 375–392.CrossRefGoogle Scholar
Donini, F., Lenzerini, M., Nardi, D. and Schaerf, A. 1998. -log: Integrating datalog and description logics. Journal of Intelligent Information Systems 10, 3, 227–252.CrossRefGoogle Scholar
Džeroski, S. and Lavrač, N., Eds. 2001. Relational Data Mining. Springer.CrossRefGoogle Scholar
Eiter, T., Gottlob, G. and Mannila, H. 1997. Disjunctive Datalog. ACM Transactions on Database Systems 22, 3, 364–418.CrossRefGoogle Scholar
Flach, P. 1993. Predicate invention in inductive data engineering. In Machine Learning: ECML-93, Brazdil, P., Ed. Lecture Notes in Computer Science, vol. 667. Springer, 83–94.CrossRefGoogle Scholar
Flach, P. 1998. From extensional to intensional knowledge: Inductive logic programming techniques and their application to deductive databases. In Transactions and Change in Logic Databases, Freitag, B., Decker, H., Kifer, M. and Voronkov, A., Eds. Lecture Notes in Computer Science, vol. 1472. Springer, 356–387.CrossRefGoogle Scholar
Frazier, M. and Pitt, L. 1993. Learning from entailment: An application to propositional horn sentences. In Proc. of the Tenth International Conference on Machine Learning. 120–127.CrossRefGoogle Scholar
Frisch, A. and Cohn, A. 1991. Thoughts and afterthoughts on the 1988 workshop on principles of hybrid reasoning. AI Magazine 11, 5, 84–87.Google Scholar
Glimm, B., Horrocks, I., Lutz, C. and Sattler, U. 2008. Conjunctive query answering for the description logic . Journal of Artificial Intelligence Research 31, 151–198.CrossRefGoogle Scholar
Glimm, B., Horrocks, I. and Sattler, U. 2008. Unions of conjunctive queries in . In Principles of Knowledge Representation and Reasoning: Proceedings of the Eleventh International Conference, KR 2008, Sydney, Australia, September 16–19, 2008, Brewka, G. and Lang, J., Eds. AAAI Press, 252–262.Google Scholar
Gómez-Pérez, A., Fernández-López, M. and Corcho, O. 2004. Ontological Engineering. Springer.Google Scholar
Gruber, T. 1993. A translation approach to portable ontology specifications. Knowledge Acquisition 5, 199–220.CrossRefGoogle Scholar
Horrocks, I., Patel-Schneider, P. and van Harmelen, F. 2003. From and RDF to OWL: The making of a web ontology language. Journal of Web Semantics 1, 1, 7–26.CrossRefGoogle Scholar
Horrocks, I., Sattler, U. and Tobies, S. 2000. Practical reasoning for very expressive description logics. Logic Journal of the IGPL 8, 3, 239–263.CrossRefGoogle Scholar
Kietz, J. 2003. Learnability of description logic programs. In Inductive Logic Programming, Matwin, S. and Sammut, C., Eds. Lecture Notes in Artificial Intelligence, vol. 2583. Springer, 117–132.Google Scholar
Levy, A. and Rousset, M.-C. 1998. Combining Horn rules and description logics in CARIN. Artificial Intelligence 104, 165–209.CrossRefGoogle Scholar
Lisi, F. A. 2008. Building rules on top of ontologies for the semantic web with inductive Logic Programming. Theory and Practice of Logic Programming 8, 03, 271–300.CrossRefGoogle Scholar
Lisi, F. A. and Esposito, F. 2008. Foundations of onto-relational learning. In Inductive Logic Programming, Železný, F. and Lavrač, N., Eds. Lecture Notes in Artificial Intelligence, vol. 5194. Springer, 158–175.CrossRefGoogle Scholar
Lisi, F. A. and Malerba, D. 2004. Inducing multi-level association rules from multiple relations. Machine Learning 55, 175–210.CrossRefGoogle Scholar
Michalski, R. 1983. A theory and methodology of inductive learning. In Machine Learning: an artificial intelligence approach, Michalski, R., Carbonell, J. and Mitchell, T., Eds. Vol. I. Morgan Kaufmann, San Mateo, CA.CrossRefGoogle Scholar
Mitchell, T. 1982. Generalization as search. Artificial Intelligence 18, 203–226.CrossRefGoogle Scholar
Motik, B., Sattler, U. and Studer, R. 2005. Query Answering for OWL-DL with Rules. Journal on Web Semantics 3, 1, 41–60.CrossRefGoogle Scholar
Muggleton, S. 1990. Inductive logic programming. In Proceedings of the 1st Conference on Algorithmic Learning Theory. Ohmsma, Tokyo, Japan.Google Scholar
Nienhuys-Cheng, S. and de Wolf, R. 1997. Foundations of Inductive Logic Programming. Lecture Notes in Artificial Intelligence, vol. 1228. Springer.CrossRefGoogle Scholar
Plotkin, G. 1970. A note on inductive generalization. Machine Intelligence 5, 153–163.Google Scholar
Plotkin, G. 1971. A further note on inductive generalization. Machine Intelligence 6, 101–121.Google Scholar
Quinlan, J. 1990. Learning logical definitions from relations. Machine Learning 5, 239–266.CrossRefGoogle Scholar
Reiter, R. 1980. Equality and domain closure in first order databases. Journal of ACM 27, 235–249.CrossRefGoogle Scholar
Rosati, R. 2005a. On the decidability and complexity of integrating ontologies and rules. Journal of Web Semantics 3, 1, 61–73.CrossRefGoogle Scholar
Rosati, R. 2005b. Semantic and computational advantages of the safe integration of ontologies and rules. In Principles and Practice of Semantic Web Reasoning, Fages, F. and Soliman, S., Eds. Lecture Notes in Computer Science, vol. 3703. Springer, 50–64.CrossRefGoogle Scholar
Rosati, R. 2006. +log: Tight integration of description logics and disjunctive datalog. In Proc. of Tenth International Conference on Principles of Knowledge Representation and Reasoning, Doherty, P., Mylopoulos, J. and Welty, C., Eds. AAAI Press, 68–78.Google Scholar
Rouveirol, C. and Ventos, V. 2000. Towards learning in CARIN-. In Inductive Logic Programming, Cussens, J. and Frisch, A., Eds. Lecture Notes in Artificial Intelligence, vol. 1866. Springer, 191–208.CrossRefGoogle Scholar
Sakama, C. 2001. Nonmonotonic inductive logic programming. In Logic Programming and Nonmonotonic Reasoning, Eiter, T., Faber, W. and Truszczynski, M., Eds. Lecture Notes in Computer Science, vol. 2173. Springer, 62–80.Google Scholar
Savnik, I. and Flach, P. A. 2000. Discovery of multivalued dependencies from relations. Intelligent Data Analysis 4, 3-4, 195–211.CrossRefGoogle Scholar
Schmidt-Schauss, M. and Smolka, G. 1991. Attributive concept descriptions with complements. Artificial Intelligence 48, 1, 1–26.CrossRefGoogle Scholar
van der Laag, P. 1995. An analysis of refinement operators in inductive logic programming. PhD thesis, Erasmus University.Google Scholar