A counting-based heuristic for ILP-based concept discovery systems

Abstract

Concept discovery systems are concerned with learning definitions of a specific relation in terms of other relations provided as background knowledge. Although such systems have a history of more than 20 years and successful applications in various domains, they are still vulnerable to scalability and efficiency issues - mainly due to large search spaces they build. In this study we propose a heuristic to select a target instance that will lead to smaller search space without sacrificing the accuracy. The proposed heuristic is based on counting the occurrences of constants in the target relation. To evaluate the heuristic, it is implemented as an extension to the concept discovery system called C2 D. The experimental results show that the modified version of C2 D builds smaller search space and performs better in terms of running time without any decrease in coverage in comparison to the one without extension. © 2013 Springer-Verlag.