论文标题
学习协作代理商，并通过规则指南的知识图推理
Learning Collaborative Agents with Rule Guidance for Knowledge Graph Reasoning
论文作者
论文摘要
基于步行的模型通过在提供可解释的决策的同时实现不错的表现，在知识图（KG）推理中表现出了优势。但是，KG在遍历过程中提供的稀疏奖励信号通常不足以指导基于步行的强化学习（RL）模型。另一种方法是使用传统的符号方法（例如，规则归纳），该方法具有良好的性能，但由于符号表示的限制，很难概括。在本文中，我们提出了Ruleguider，该规则指导者利用基于符号的方法生成的高质量规则来为步行者的代理提供奖励监督。基准数据集上的实验表明，规则指导者可以改善基于步行的模型的性能而不会失去解释性。
Walk-based models have shown their advantages in knowledge graph (KG) reasoning by achieving decent performance while providing interpretable decisions. However, the sparse reward signals offered by the KG during traversal are often insufficient to guide a sophisticated walk-based reinforcement learning (RL) model. An alternate approach is to use traditional symbolic methods (e.g., rule induction), which achieve good performance but can be hard to generalize due to the limitation of symbolic representation. In this paper, we propose RuleGuider, which leverages high-quality rules generated by symbolic-based methods to provide reward supervision for walk-based agents. Experiments on benchmark datasets show that RuleGuider improves the performance of walk-based models without losing interpretability.
