On Applying Tabling to Inductive Logic Programming
Ricardo Rocha, Nuno Fonseca and VĂtor Santos Costa
October 2005
Abstract
Inductive Logic Programming (ILP) is an established sub-field of
Machine Learning. Nevertheless, it is recognized that efficiency and
scalability is a major obstacle to an increased usage of ILP systems
in complex applications with large hypotheses spaces. In this work, we
focus on improving the efficiency and scalability of ILP systems by
exploring tabling mechanisms available in the underlying Logic
Programming systems. Tabling is an implementation technique that
improves the declarativeness and performance of Prolog systems by
reusing answers to subgoals. To validate our approach, we ran the
April ILP system in the YapTab Prolog tabling system using two
well-known datasets. The results obtained show quite impressive gains
without changing the accuracy and quality of the theories generated.
Bibtex
@InProceedings{rocha-ecml05,
author = {R. Rocha and N. Fonseca and V. Santos Costa},
title = {{On Applying Tabling to Inductive Logic Programming}},
booktitle = {Proceedings of the 16th European Conference on Machine Learning (ECML 2005)},
pages = {707--714},
number = {3720},
series = {LNAI},
publisher = {Springer},
editor = {J. Gama and R. Camacho and P. Brazdil and A. Jorge and L. Torgo},
month = {October},
year = {2005},
address = {Porto, Portugal},
}
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