Inductive Logic Programming (ILP) is a Machine Learning research field that has been quite successful in knowledge discovery in relational domains. ILP systems use a set of pre-classified examples (positive and negative) and prior knowledge to learn a theory in which positive examples succeed and the negative examples fail. In this paper we present a novel ILP system called April, capable of ex...

This paper introduces a new type of application for ILP called Bootstrapped Learning (BL). BL brings several challenges to ILP, including the need to (a) automate the “ILP setup” problem, (b) exploit the fact that a well-meaning teacher is providing pedagogically chosen examples and may be offering hints, (c) deal with small numbers of training examples and sometimes no explicit negative exampl...

The use of background knowledge and the adoption of Horn clausal logic as a knowledge representation and reasoning framework are the distinguishing features of Inductive Logic Programming (ILP) with respect to other approaches to concept learning. We argue that ILP can not ignore the latest developments in Knowledge Engineering such as ontologies and formalisms based on Description Logics. In t...

HYPOTHESIS In patients with truly unresectable melanoma of the extremities, results after isolated limb perfusion (ILP) are absent in the literature. Complete response rates are probably lower than the reported 54% for locoregional recurrent melanoma. In these patients, ILP with melphalan and tumor necrosis factor alpha (TNF-alpha) could be superior to ILP with melphalan alone. DESIGN Retrosp...

Inductive Logic Programming (ILP) is a subfield of Machine Learning with foundations in logic programming. In ILP, logic programming, a subset of first-order logic, is used as a uniform representation language for the problem specification and induced theories. ILP has been successfully applied to many real-world problems, especially in the biological domain (e.g. drug design, protein structure...

This chapter introduces Inductive Logic Programming (ILP) and Learning Language in Logic (LLL). No previous knowledge of logic programming, ILP or LLL is assumed. Elementary topics are covered and more advanced topics are discussed. For example, in the ILP section we discuss subsumption, inverse resolution, least general generalisation, relative least general generalisation, inverse entailment,...