Temporal Knowledge Graph (TKG) is an extension of regular knowledge graph by attaching the time scope. Existing temporal knowledge graph question answering (TKGQA) models solely approach simple questions, owing to the prior assumption that each question only contains a single temporal fact with explicit/implicit temporal constraints. Hence, they perform poorly on questions which own multiple temporal facts. In this paper, we propose \textbf{\underline{J}}oint \textbf{\underline{M}}ulti \textbf{\underline{F}}acts \textbf{\underline{R}}easoning \textbf{\underline{N}}etwork (JMFRN), to jointly reasoning multiple temporal facts for accurately answering \emph{complex} temporal questions. Specifically, JMFRN first retrieves question-related temporal facts from TKG for each entity of the given complex question. For joint reasoning, we design two different attention (\ie entity-aware and time-aware) modules, which are suitable for universal settings, to aggregate entities and timestamps information of retrieved facts. Moreover, to filter incorrect type answers, we introduce an additional answer type discrimination task. Extensive experiments demonstrate our proposed method significantly outperforms the state-of-art on the well-known complex temporal question benchmark TimeQuestions.

翻译：时序知识图谱（TKG）是通过附加时间范围对常规知识图谱的扩展。现有的时序知识图谱问答（TKGQA）模型仅能处理简单问题，其前提假设是每个问题仅包含一个具有显式/隐式时间约束的单一时序事实。因此，它们在包含多个时序事实的问题上表现不佳。本文提出联合多事实推理网络（JMFRN），以联合推理多个时序事实，准确回答复杂时序问题。具体而言，JMFRN首先从TKG中检索与问题相关的时序事实，针对给定复杂问题中的每个实体。为了实现联合推理，我们设计了两种不同的注意力机制（即实体感知和时间感知模块），这些模块适用于通用场景，用于聚合检索事实中的实体和时间戳信息。此外，为了过滤错误类型的答案，我们引入了一个额外的答案类型判别任务。大量实验表明，所提方法在著名的复杂时序问题基准TimeQuestions上显著优于现有最先进方法。