Many models that leverage knowledge graphs (KGs) have recently demonstrated remarkable success in question answering (QA) tasks. In the real world, many facts contained in KGs are time-constrained thus temporal KGQA has received increasing attention. Despite the fruitful efforts of previous models in temporal KGQA, they still have several limitations. (I) They adopt pre-trained language models (PLMs) to obtain question representations, while PLMs tend to focus on entity information and ignore entity transfer caused by temporal constraints, and finally fail to learn specific temporal representations of entities. (II) They neither emphasize the graph structure between entities nor explicitly model the multi-hop relationship in the graph, which will make it difficult to solve complex multi-hop question answering. To alleviate this problem, we propose a novel Question Calibration and Multi-Hop Modeling (QC-MHM) approach. Specifically, We first calibrate the question representation by fusing the question and the time-constrained concepts in KG. Then, we construct the GNN layer to complete multi-hop message passing. Finally, the question representation is combined with the embedding output by the GNN 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 QC-MHM on the CronQuestions dataset's complex questions are absolutely improved by 5.1% and 1.2% compared to the best-performing baseline. Moreover, QC-MHM can generate interpretable and trustworthy predictions.

翻译：近期，许多利用知识图谱（KG）的模型在问答（QA）任务中取得了显著成功。现实世界中，知识图谱包含的诸多事实受时间约束，因此时序知识图谱问答（temporal KGQA）逐渐受到关注。尽管先前模型在时序知识图谱问答中取得了丰硕成果，但仍存在若干局限：（I）现有模型采用预训练语言模型（PLM）获取问题表征，但PLM倾向于关注实体信息，忽视时间约束引起的实体转移，最终难以学习实体特定的时间表征；（II）现有模型既未强调实体间的图结构，也未显式建模图中的多跳关系，这将导致复杂多跳问答的求解困难。为缓解上述问题，我们提出了一种新型时间校准与多跳建模（QC-MHM）方法。具体而言，我们首先通过融合问题与知识图谱中的时间约束概念来校准问题表征；其次构建图神经网络（GNN）层以完成多跳消息传递；最终将问题表征与GNN输出的嵌入表示相结合生成最终预测。实验结果表明，所提模型在基准数据集上取得了优于当前最优模型的性能。值得注意的是，在CronQuestions数据集的复杂问题子集上，QC-MHM的Hits@1与Hits@10指标相比最优基线分别绝对提升了5.1%和1.2%。此外，QC-MHM可生成可解释且可信的预测结果。