论文标题
图形神经网络有效的概率逻辑推理
Efficient Probabilistic Logic Reasoning with Graph Neural Networks
论文作者
论文摘要
马尔可夫逻辑网络（MLN）优雅地结合了逻辑规则和概率图形模型，可用于解决许多知识图问题。但是，MLN的推论是计算密集型的，这使得MLN的工业规模应用非常困难。近年来，图形神经网络（GNN）已成为大规模图问题的有效工具。但是，GNN并未明确地将先前的逻辑规则纳入模型，并且可能需要许多标记的示例来实现目标任务。在本文中，我们探讨了MLN和GNN的组合，并使用图神经网络在MLN中进行变异推断。我们提出了一个名为Expressgnn的GNN变体，该变体在表示功率和模型的简单性之间达到了很好的平衡。我们在几个基准数据集上进行的广泛实验表明，表达导致有效，有效的概率逻辑推理。
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.
