Efficient Probabilistic Logic Reasoning with Graph Neural Networks
This work addresses the problem of scaling probabilistic logic reasoning for industrial applications, though it is incremental as it builds on existing MLN and GNN methods.
The paper tackles the computational intensity of inference in Markov Logic Networks (MLNs) for knowledge graph problems by combining MLNs with graph neural networks (GNNs) for variational inference, resulting in effective and efficient probabilistic logic reasoning as demonstrated on benchmark datasets.
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.