Using neural networks for relational learning

Abstract

Relational learners need to be able to handle the information contained in a set of related tuples. Most current relational learners are biased either towards the use of aggregate functions that summarize that set, or towards checking the existence of specific kinds of elements in that set. Learning patterns that contain a combination of both is a challenging task. In this paper we introduce a neural networks based approach to relational learning, where the neural net that is learned can actually represent such a combination. This capacity is illustrated on toy problems, but several questions are open with respect to learnability of more complicated concepts. 1.