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.

翻译：近年来，针对复杂问答任务的知识图谱时序推理研究备受关注，但距离人类水平仍存在显著差距。本文探索如何将关系图卷积网络泛化应用于时序知识图谱问答任务。具体而言，我们提出了一种新颖、直观且可解释的方案，在卷积过程中根据知识图谱边关联时间段与问题的相关性，调制通过该边传递的消息。同时引入门控机制，预测复杂时序问题答案属于知识图谱实体还是时间实体的可能性，并利用该预测指导评分机制。我们将所构建的系统命名为TwiRGCN，并在近期发布的多跳复杂时序问答挑战数据集TimeQuestions上进行评估。实验表明，在多种问题类型上，TwiRGCN显著优于现有最优系统。值得注意的是，对于最具挑战性的序数类和隐式类问题，TwiRGCN的准确率提升了9至10个百分点。