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:时态知识图谱(TKG)可用于模拟事件的时间演变,并已引起越来越多的关注。由于TKG本质上是不完整的,因此需要推理出缺失的元素。尽管现有的TKG推理方法能够预测缺失的未来事件,但它们无法生成明确的推理路径并缺乏可解释性。由于强化学习(RL)在传统知识图谱上的多跳推理方面显示出卓越的可解释性和性能,因此它已经为探索TKG推理中的RL技术开辟了机遇。然而,基于RL的TKG推理方法的性能受到限制,因为它们缺乏同时捕捉时间演变和语义依赖性的能力,并且过度依赖手动设计的奖励。为了克服这些挑战,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)一种多方位注意表示学习方法,可以同时捕捉语义依赖和时间演变;(2)一种自适应RL框架,通过自适应地学习奖励函数进行多跳推理。实验结果表明,DREAM在公共数据集上的性能优于最先进的模型。