  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.
