Knowledge graphs (KGs) have received increasing attention due to its wide applications on natural language processing. However, its use case on temporal question answering (QA) has not been well-explored. Most of existing methods are developed based on pre-trained language models, which might not be capable to learn \emph{temporal-specific} presentations of entities in terms of temporal KGQA task. To alleviate this problem, we propose a novel \textbf{T}ime-aware \textbf{M}ultiway \textbf{A}daptive (\textbf{TMA}) fusion network. Inspired by the step-by-step reasoning behavior of humans. For each given question, TMA first extracts the relevant concepts from the KG, and then feeds them into a multiway adaptive module to produce a \emph{temporal-specific} representation of the question. This representation can be incorporated with the pre-trained KG embedding to generate the final prediction. Empirical results verify that the proposed model achieves better performance than the state-of-the-art models in the benchmark dataset. Notably, the Hits@1 and Hits@10 results of TMA on the CronQuestions dataset's complex questions are absolutely improved by 24\% and 10\% compared to the best-performing baseline. Furthermore, we also show that TMA employing an adaptive fusion mechanism can provide interpretability by analyzing the proportion of information in question representations.

翻译：知识图谱因其在自然语言处理中的广泛应用而受到越来越多的关注。然而，其在时序问答领域的应用尚未得到充分探索。现有方法大多基于预训练语言模型，这些模型可能无法有效学习用于时序知识图谱问答任务的实体时序特定表示。为解决这一问题，我们提出了一种新颖的时间感知多路自适应融合网络。受人类逐步推理行为的启发，对于每个给定的问题，TMA首先从知识图谱中提取相关概念，随后将其输入多路自适应模块，以生成问题的时序特定表示。该表示可与预训练的知识图谱嵌入结合，产生最终预测。实验结果表明，所提模型在基准数据集上取得了优于当前先进模型的性能。值得注意的是，在CronQuestions数据集复杂问题上，相较于最优基线，TMA的Hits@1和Hits@10结果分别绝对提升了24%和10%。此外，我们还展示了采用自适应融合机制的TMA能够通过分析问题表示中的信息比例提供可解释性。