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From extensional to intensional knowledge: Inductive Logic Programming techniques and their application to deductive databases

Peter Flach, From extensional to intensional knowledge: Inductive Logic Programming techniques and their application to deductive databases. Chapter in Transactions and Change in Logic Databases. B. Freitag, H. Decker, M. Kifer, A. Voronkov, (eds.). ISBN 3-540-65305-8, pp. 356–387. December 1998. PDF, 452 Kbytes. External information

Abstract

This chapter aims at demonstrating that inductive logic programming (ILP), a recently established subfield of machine learning that induces first-order clausal theories from examples, combines very well with the area of deductive databases. In the context of deductive databases, induction can be defined as inference of intensional knowledge from extensional knowledge. Classification-oriented ILP approaches correspond to induction of view definitions (IDB rules), while description-oriented ILP approaches correspond to induction of integrity constraints. The applicability of ILP methods in deductive databases thus includes induction of IDB rules and learning of integrity constraints. Further possible applications are reverse engineering, query optimisation and intensional answers, and data mining. The chapter gives an accessible introduction to ILP with particular emphasis on applications in deductive databases.

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