标题： 基于强化学习的知识图谱异构关系推理 显示英文标题

标题： Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning

摘要： 基于路径的关系推理在知识图谱中由于各种下游应用而变得越来越流行，例如对话系统中的问答、事实预测和推荐系统。 近年来，强化学习（RL）提供了比其他深度学习模型更具可解释性和可说明性的解决方案。 然而，这些解决方案仍然面临一些挑战，包括RL代理的大动作空间和实体邻域结构的准确表示。 我们通过引入一种类型增强的RL代理来解决这些问题，该代理利用局部邻域信息以在知识图谱上进行高效的基于路径的推理。 我们的解决方案使用图神经网络（GNN）对邻域信息进行编码，并利用实体类型来剪枝动作空间。 在真实数据集上的实验表明，我们的方法优于最先进的RL方法，并在训练过程中发现了更多新的路径。 显示英文摘要

摘要： Path-based relational reasoning over knowledge graphs has become increasingly popular due to a variety of downstream applications such as question answering in dialogue systems, fact prediction, and recommender systems. In recent years, reinforcement learning (RL) has provided solutions that are more interpretable and explainable than other deep learning models. However, these solutions still face several challenges, including large action space for the RL agent and accurate representation of entity neighborhood structure. We address these problems by introducing a type-enhanced RL agent that uses the local neighborhood information for efficient path-based reasoning over knowledge graphs. Our solution uses graph neural network (GNN) for encoding the neighborhood information and utilizes entity types to prune the action space. Experiments on real-world dataset show that our method outperforms state-of-the-art RL methods and discovers more novel paths during the training procedure.