Title: Stacked Structure Learning for Lifted Relational Neural Networks,
Abstract: Lifted Relational Neural Networks (LRNNs) describe relational domains using
weighted first-order rules which act as templates for constructing feed-forward
neural networks. While previous work has shown that using LRNNs can lead to
state-of-the-art results in various ILP tasks, these results depended on
hand-crafted rules. In this paper, we extend the framework of LRNNs with
structure learning, thus enabling a fully automated learning process. Similarly
to many ILP methods, our structure learning algorithm proceeds in an iterative
fashion by top-down searching through the hypothesis space of all possible Horn
clauses, considering the predicates that occur in the training examples as well
as invented soft concepts entailed by the best weighted rules found so far. In
the experiments, we demonstrate the ability to automatically induce useful
hierarchical soft concepts leading to deep LRNNs with a competitive predictive
power.