Rules–the clearest, most explored and best understood form of knowledge representation–
are particularly important for data mining, as they offer the best tradeoff between human and …

Abstract Inductive Logic Programming (ILP) is a new discipline which investigates the
inductive construction of first-order clausal theories from examples and background …

Current inductive machine learning algorithms typically use greedy search with limited
lookahead. This prevents them to detect significant conditional dependencies between the …

The 18th International Conference on Inductive Logic Programming was held in Prague,
September 10–12, 2008. ILP returned to Prague after 11 years, and it is tempting to look at …

This paper is a survey of inductive rule learning algorithms that use a separate-and-conquer
strategy. This strategy can be traced back to the AQ learning system and still enjoys …

FOIL is a learning system that constructs Horn clause programs from examples. This paper
summarises the development of FOIL from 1989 up to early 1993 and evaluates its …

This paper outlines some problems that may occur with Reduced Error Pruning in relational
learning algorithms, most notably efficiency. Thereafter a new method, Incremental Reduced …

The World Wide Web is a vast source of information accessible to computers, but
understandable only to humans. The goal of the research described here is to automatically …

We we present some algorithms, based on rough set theory, that can be used for the
problem of new cases classification. Most of the algorithms were implemented and included …

Even the entire Web corpus does not explicitly answer all questions, yet inference can
uncover many implicit answers. But where do inference rules come from? This paper …