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From Extensional to Intensional Knowledge: Inductive Logic Programming Techniques and Their Application to Deductive Databases (1998)
| Venue: | Transactions and Change in Logic Databases, volume 1472 of Lecture |
| Citations: | 9 - 3 self |
BibTeX
@INPROCEEDINGS{Flach98fromextensional,
author = {Peter A. Flach},
title = {From Extensional to Intensional Knowledge: Inductive Logic Programming Techniques and Their Application to Deductive Databases},
booktitle = {Transactions and Change in Logic Databases, volume 1472 of Lecture},
year = {1998},
pages = {356--387},
publisher = {Springer-Verlag}
}
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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 descriptionoriented ILP approaches correspond to induction of integrity constraints.
