Can Graph Neural Networks Help Logic Reasoning?

Understand

Effectively combining logic reasoning and probabilistic inference has been a long-standing goal of machine learning: the former has the ability to generalize with small training data, while the latter provides a principled framework for dealing with noisy data.

• However, existing methods for combining the best of both worlds are typically computationally intensive.
• In this paper, we focus on Markov Logic Networks and explore the use of graph neural networks (GNNs) for representing probabilistic logic inference.
• It is revealed from our analysis that the representation power of GNN alone is not enough for such a task.