Recent years have witnessed much interest in temporal reasoning over
knowledge graphs (KG) for complex question answering (QA), but there remains a
substantial gap in human capabilities. We explore how to generalize relational
graph convolutional networks (RGCN) for temporal KGQA. Specifically, we propose
a novel, intuitive and interpretable scheme to modulate the messages passed
through a KG edge during convolution, based on the relevance of its associated
time period to the question. We also introduce a gating device to predict if
the answer to a complex temporal question is likely to be a KG entity or time
and use this prediction to guide our scoring mechanism. We evaluate the
resulting system, which we call TwiRGCN, on TimeQuestions, a recently released,
challenging dataset for multi-hop complex temporal QA. We show that TwiRGCN
significantly outperforms state-of-the-art systems on this dataset across
diverse question types. Notably, TwiRGCN improves accuracy by 9--10 percentage
points for the most difficult ordinal and implicit question types.

通过引入知识图谱中边相关时间的相关性，利用关系图卷积网络（RGCN）来解决复杂时间问题类型并显著提高精度。