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EPIA'03 - 11th Portuguese Conference on Artificial Intelligence
EKDB -- International Workshop on Extraction of Knowledge from Data Bases
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Session: December 5, 17:15-18:45, Room B |
Title: |
Improving the efficiency of ILP systems |
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Rui Camacho |
Abstract: |
Inductive Logic Programming (ILP) is a promissing technology for knowledge
extraction applications. ILP has produced intelligible solutions for a wide
variety of domains where it has been applied. The ILP lack of efficiency is,
however, a major impediment for its scalability to applications requiring
large amounts of data.
In this paper we address important issues that must be solved to make ILP
scalable to applications of knowledge exatrction in large amounts of data. The
issues include: efficiency and storage requirements. We propose and evaluate a
set of techniques, globally called lazy evaluation of examples, to improve the
efficiency of ILP systems. Lazy evaluation is essentially a way to avoid or
postpone the evaluation of the generated hypotheses (coverage tests). To
reduce the storage amount a representation schema called interval trees is
proposed and evaluated.
All the techniques were evaluated using the IndLog ILP system and a set of
ILP datasets referenced in the literature.
The proposals lead to substantial efficiency improvements and memory savings
and are generally applicable to any ILP system. |
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