Structure Learning via Parameter Learning.

A key challenge in information and knowledge management is to automatically discover the underlying structures and patterns from large collections of extracted information. This paper presents a novel structure-learning method for a new, scalable probabilistic logic called ProPPR. Our approach builds on the recent success of meta-interpretive learning methods in Inductive Logic Programming (ILP), and we further extends it to a framework that enables robust and efficient structure learning of logic programs on graphs: using an abductive second-order probabilistic logic, we show how first-order theories can be automatically generated via parameter learning. To learn better theories, we then propose an iterated structural gradient approach that incrementally refines the hypothesized space of learned first-order structures. In experiments, we show that the proposed method further improves the results, outperforming competitive baselines such as Markov Logic Networks (MLNs) and FOIL on multiple datasets with various settings; and that the proposed approach can learn structures in a large knowledge base in a tractable fashion.