Improving the Efficiency of Inductive Logic Programming Through the Use of Query Packs
Abstract
Inductive logic programming, or relational learning, is a powerful paradigm for machine learning or data mining. However, in order for ILP to become practically useful, the efficiency of ILP systems must improve substantially. To this end, the notion of a query pack is introduced: it structures sets of similar queries. Furthermore, a mechanism is described for executing such query packs. A complexity analysis shows that considerable efficiency improvements can be achieved through the use of this query pack execution mechanism. This claim is supported by empirical results obtained by incorporating support for query pack execution in two existing learning systems.
Cite
Text
Blockeel et al. "Improving the Efficiency of Inductive Logic Programming Through the Use of Query Packs." Journal of Artificial Intelligence Research, 2002. doi:10.1613/JAIR.924Markdown
[Blockeel et al. "Improving the Efficiency of Inductive Logic Programming Through the Use of Query Packs." Journal of Artificial Intelligence Research, 2002.](https://mlanthology.org/jair/2002/blockeel2002jair-improving/) doi:10.1613/JAIR.924BibTeX
@article{blockeel2002jair-improving,
title = {{Improving the Efficiency of Inductive Logic Programming Through the Use of Query Packs}},
author = {Blockeel, Hendrik and Dehaspe, Luc and Demoen, Bart and Janssens, Gerda and Ramon, Jan and Vandecasteele, Henk},
journal = {Journal of Artificial Intelligence Research},
year = {2002},
pages = {135-166},
doi = {10.1613/JAIR.924},
volume = {16},
url = {https://mlanthology.org/jair/2002/blockeel2002jair-improving/}
}