Improving the efficiency of ILP systems

• Type: Article in International Scientific Journal
• Date: 2003

Authors

Rui Camacho (Author) FEUP

Journal

The Journal is awaiting validation by the Administrative Services.

Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Vol. 2902

Pages: 224-228

ISSN: 03029743

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 1 Citation

Other information

Authenticus ID: P-000-KGW

DOI: 10.1007/978-3-540-24580-3_29

Abstract (EN): Inductive Logic Programming (ILP) is a promising 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 propose a set of techniques that improve ILP systems efficiency and make then more likely to scale up to applications of knowledge extraction from large datasets. We propose and evaluate the 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). The techniques were evaluated using the IndLog system on ILP datasets referenced in the literature. The proposals lead to substantial efficiency improvements and are generally applicable to any ILP system.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 5