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：近些年来,以时间意图对知识图(时空KGQA)回答事实问题,引起越来越多的关注。在生成时间查询时,现有的KGQA方法忽略了这样一个事实,即事件之间的某些内在联系可能使它们与时间有关,从而限制它们的能力。我们系统地分析对时间限制的可能解释,并得出解释结构,作为时间制约的语义框架,SF-TQA。根据语义框架,我们提出了一个时间问题回答方法,SF-TQA。SF-TQA通过探索上述实体的相关事实来生成查询图表,在这些实体中,探索过程受到SF-TCons的限制。我们的评估表明,SF-TQA大大超越了不同知识图的两种基准的现有方法。