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Inductive Logic Programming in Databases: From Datalog to $\mathcal{DL}+log}^{\neg\vee}$

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

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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 $\mathcal{DL}+log}^{\neg\vee}$. We illustrate the application scenarios by means of examples.

Keywords

Inductive Logic Programmingrelational databasesontologiesdescription logicshybrid Knowledge Representation and reasoning systems

Information

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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