Inductive Logic Programming in Databases: from Datalog to DL+log

TL;DR

This paper explores how ontologies and the DL+log framework enhance inductive logic programming for database tasks like view and constraint definition, integrating semantic web concepts with database reasoning.

Contribution

It introduces a novel approach using DL+log to reformulate database problems as ILP tasks, leveraging ontologies for improved reasoning capabilities.

Findings

Demonstrates the application of DL+log in defining database views and constraints.

Shows the benefits of integrating ontologies with ILP for database reasoning.

Provides example scenarios illustrating the approach.

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 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 DL+log. We illustrate the application scenarios by means of examples. Keywords: Inductive Logic Programming, Relational Databases, Ontologies, Description Logics, Hybrid Knowledge Representation and Reasoning Systems. Note: To appear in Theory and Practice of Logic Programming (TPLP).