Abstract
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.
The work presented in this paper has been partially supported by Universidade do Porto, project APRIL (Project POSI/SRI/40749/2001), funds granted to LIACC through the Programa de Financiamento Plurianual, Fundaçã o para a Ciência e Tecnologia and Programa POSI.
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Camacho, R. (2003). Improving the Efficiency of ILP Systems. In: Pires, F.M., Abreu, S. (eds) Progress in Artificial Intelligence. EPIA 2003. Lecture Notes in Computer Science(), vol 2902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24580-3_29
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DOI: https://doi.org/10.1007/978-3-540-24580-3_29
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