Answering factual questions with temporal intent over knowledge graphs (temporal KGQA) attracts rising attention in recent years. In the generation of temporal queries, existing KGQA methods ignore the fact that some intrinsic connections between events can make them temporally related, which may limit their capability. We systematically analyze the possible interpretation of temporal constraints and conclude the interpretation structures as the Semantic Framework of Temporal Constraints, SF-TCons. Based on the semantic framework, we propose a temporal question answering method, SF-TQA, which generates query graphs by exploring the relevant facts of mentioned entities, where the exploring process is restricted by SF-TCons. Our evaluations show that SF-TQA significantly outperforms existing methods on two benchmarks over different knowledge graphs.

翻译：回答基于知识图谱的带时间意图的事实性问题（时序KGQA）近年来受到越来越多的关注。在生成时序查询时，现有KGQA方法忽略了事件间内在联系可能使其具有时间相关性这一事实，从而限制了其性能。我们系统分析了时序约束的可能解释方式，并将其解释结构总结为时序约束语义框架（SF-TCons）。基于该语义框架，我们提出了一种时序问题问答方法SF-TQA，该方法通过探索提及实体的相关事实来生成查询图，其中探索过程受SF-TCons约束。评估结果表明，SF-TQA在不同知识图谱的两个基准数据集上显著优于现有方法。