Efficient Probabilistic Logic Reasoning with Graph Neural Networks

Yuyu Zhang, Xinshi Chen, Yuan Yang, Arun Ramamurthy, Bo Li, Yuan Qi, Le Song

TL;DR: We employ graph neural networks in the variational EM framework for efficient inference and learning of Markov Logic Networks.

Abstract: Markov Logic Networks (MLNs), which elegantly combine logic rules and probabilistic graphical models, can be used to address many knowledge graph problems. However, inference in MLN is computationally intensive, making the industrial-scale application of MLN very difficult. In recent years, graph neural networks (GNNs) have emerged as efficient and effective tools for large-scale graph problems. Nevertheless, GNNs do not explicitly incorporate prior logic rules into the models, and may require many labeled examples for a target task. In this paper, we explore the combination of MLNs and GNNs, and use graph neural networks for variational inference in MLN. We propose a GNN variant, named ExpressGNN, which strikes a nice balance between the representation power and the simplicity of the model. Our extensive experiments on several benchmark datasets demonstrate that ExpressGNN leads to effective and efficient probabilistic logic reasoning.

Code: https://github.com/expressGNN/ExpressGNN

Keywords: probabilistic logic reasoning, Markov Logic Networks, graph neural networks

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2001.11850/code)