Scalable Learning for Structure in Markov Logic Networks

Zhengya Sun, Zhuoyu Wei, Jue Wang, Hongwei Hao

Abstract: Markov Logic Networks (MLNs) provide a unifying framework that incorporates first-order logic and probability. However, learning the structure of MLNs is a computationally hard task due to the large search space and the intractable clause evaluation. In this paper, we propose a random walk-based approach to learn MLN structure in a scalable manner. It uses the interactions existing among the objects to constrain the search space of candidate clauses. Specifically, we obtain representative subset of simple paths by sampling from all sequences of distinct objects. We then transform each sampled path into possible ground atoms, and use them to form clauses. Based on the resulting ground network, we finally attach a set of weights to the clauses by optimizing `1-constrained conditional likelihood. The experimental results demonstrate that our approach performs favorably compared to previous approaches.