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From extensional to intensional knowledge: Inductive logic programming techniques and their application to deductive databases

  • Peter A. Flach
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1472)

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.

Keywords

Integrity Constraint Inductive Logic Inductive Logic Programming Horn Clause Deductive Database 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Peter A. Flach
    • 1
  1. 1.Department of Computer ScienceUniversity of BristolBristolUK

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