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（KG）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（QA）场景中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开发，这可能无法针对时序知识图谱问答任务学习实体的时序特定表示。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（TMA）。受人类逐步推理行为的启发，TMA首先从知识图谱中提取与给定问题相关的概念，然后将其输入多路自适应模块，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相结合，以产生最终预测。实证结果表明，所提模型在基准数据集上取得了优于现有最先进模型的性能。值得注意的是，在CronQuestions数据集的复杂问题上，TMA的Hits@1和Hits@10结果相较于最佳基线分别绝对提升了24%和10%。此外，我们还展示了采用自适应融合机制的TMA可通过分析问题表示中的信息比例提供可解释性。