Efficiency-conscious propositionalization for relational learning

Systems aiming at discovering interesting knowledge in data, now commonly called data mining systems, are typically employed in nding patterns in a single relational table. Most of mainstream data mining tools are not applicable in the more challenging task of nding knowledge in structured data represented by a multi-relational database. Although a family of methods known as inductive logic programming have been developed to tackle that challenge by immediate means, the idea of adapting structured data into a simpler form digestible by the wealth of AVL systems has been always tempting to data miners. To this end, we present a method based on constructing rst-order logic features that conducts this kind of conversion, also known as propositionalization. It incorporates some basic principles suggested in previous research and provides signiicant enhancements that lead to remarkable improvements in eeciency of the feature-construction process. We begin by motivating the propositionalization task with an illustrative example, review some previous approaches to propositionalization, and formalize the concept of a rst-order feature elaborating mainly the points that innuence the eeciency of the designed feature-construction algorithm.