Temporal knowledge graphs (TKGs) model the temporal evolution of events and have recently attracted increasing attention. Since TKGs are intrinsically incomplete, it is necessary to reason out missing elements. Although existing TKG reasoning methods have the ability to predict missing future events, they fail to generate explicit reasoning paths and lack explainability. As reinforcement learning (RL) for multi-hop reasoning on traditional knowledge graphs starts showing superior explainability and performance in recent advances, it has opened up opportunities for exploring RL techniques on TKG reasoning. However, the performance of RL-based TKG reasoning methods is limited due to: (1) lack of ability to capture temporal evolution and semantic dependence jointly; (2) excessive reliance on manually designed rewards. To overcome these challenges, we propose an adaptive reinforcement learning model based on attention mechanism (DREAM) to predict missing elements in the future. Specifically, the model contains two components: (1) a multi-faceted attention representation learning method that captures semantic dependence and temporal evolution jointly; (2) an adaptive RL framework that conducts multi-hop reasoning by adaptively learning the reward functions. Experimental results demonstrate DREAM outperforms state-of-the-art models on public dataset

翻译：时序知识图谱（TKG）对事件的时间演化进行建模，近年来受到越来越多的关注。由于TKG本质上是不完整的，因此需要推理出缺失元素。尽管现有的TKG推理方法能够预测未来缺失事件，但它们无法生成显式的推理路径，缺乏可解释性。随着强化学习（RL）在传统知识图谱多跳推理中展现出优越的可解释性和性能，这为探索RL技术在TKG推理中的应用提供了机遇。然而，基于RL的TKG推理方法的性能受到以下限制：（1）缺乏联合捕捉时间演化和语义依赖的能力；（2）过度依赖人工设计的奖励函数。为克服这些挑战，我们提出了一种基于注意力机制的自适应强化学习模型（DREAM），用于预测未来的缺失元素。具体而言，该模型包含两个组件：（1）一种多方面注意力表示学习方法，联合捕捉语义依赖和时间演化；（2）一种自适应RL框架，通过自适应学习奖励函数进行多跳推理。实验结果表明，DREAM在公开数据集上优于当前最先进的模型。