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From extensional to intensional knowledge: Inductive Logic Programming
techniques and their application to deductive databases
P.A. Flach.
In B. Freitag,
H. Decker,
M. Kifer,
and A. Voronkov, editors, Transactions
and Change in Logic Databases, volume 1472 of Lecture Notes in
Computer Science, pages 356--387. Springer-Verlag, 1998. More behind this link.
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.
BibTeX entry.
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P A Flach,
Peter.Flach@bristol.ac.uk. Last modified on Wednesday 9 April 2003 at 18:31. © 2003 ILPnet2