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.

我们提出了一种新颖的问题校准和多跳建模（QC-MHM）方法，通过融合知识图谱中的问题和时间约束概念来校准问题表示，并借助图神经网络层完成多跳消息传递，最终将问题表示与图神经网络的嵌入输出相结合以生成最终预测。实证结果验证了所提模型在基准数据集上优于现有模型的性能，尤其是在 CronQuestions 数据集的复杂问题上，QC-MHM 的 Hits@1 和 Hits@10 结果较最佳基准模型分别提高了 5.1% 和 1.2%，此外，QC-MHM 能够生成可解释且可信的预测。