论文标题
通过加强学习的知识图中的异构关系推理
Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning
论文作者
论文摘要
由于各种下游应用程序，例如对话系统中的问答，事实预测和推荐系统，基于路径的关系推理已经越来越流行。近年来，强化学习（RL）提供了比其他深度学习模型更容易解释和解释的解决方案。但是，这些解决方案仍然面临几个挑战，包括用于RL代理的大型动作空间以及实体邻里结构的准确表示。我们通过引入一种类型增强的RL代理来解决这些问题，该代理使用本地邻域信息来有效地基于知识图的路径推理。我们的解决方案使用图形神经网络（GNN）来编码邻域信息，并利用实体类型修剪动作空间。现实世界数据集的实验表明，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颖的路径。
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.
