From Inductive Logic Programming to Relational Data Mining

Abstract

Situated at the intersection of machine learning and logic programming, inductive logic programming (ILP) has been concerned with finding patterns expressed as logic programs. While ILP initially focussed on automated program synthesis from examples, it has recently expanded its scope to cover a whole range of data analysis tasks (classification, regression, clustering, association analysis). ILP algorithms can this be used to find patterns in relational data, i.e., for relational data mining (RDM). This paper briefly introduces the basic concepts of ILP and RDM and discusses some recent research trends in these areas.