Title: Compact representation of knowledge bases in inductive logic programming
Authors: Struyf, Jan ×
Ramon, Jan
Bruynooghe, Maurice
Verbaeten, Sofie
Blockeel, Hendrik #
Issue Date: Dec-2004
Publisher: Kluwer academic publ
Series Title: Machine learning vol:57 issue:3 pages:305-333
Abstract: In many applications of Inductive Logic Programming (ILP), learning occurs from a knowledge base that contains a large number of examples. Storing such a knowledge base may consume a lot of memory. Often, there is a substantial overlap of information between different examples. To reduce memory consumption, we propose a method to represent a knowledge base more compactly. We achieve this by introducing a meta-theory able to build new theories out of other (smaller) theories. In this way, the information associated with an example can be built from the information associated with one or more other examples and redundant storage of shared information is avoided. We also discuss algorithms to construct the information associated with example theories and report on a number of experiments evaluating our method in different problem domains.
ISSN: 0885-6125
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

Files in This Item:
File Status SizeFormat
CompRepKB-StruyfEtAl-ML04.pdf Published 305KbAdobe PDFView/Open


All items in Lirias are protected by copyright, with all rights reserved.

© Web of science