Knowledge Base Question Answering (KBQA) has been a long-standing field to answer questions based on knowledge bases. Recently, the evolving dynamics of knowledge have attracted a growing interest in Temporal Knowledge Graph Question Answering (TKGQA), an emerging task to answer temporal questions. However, this field grapples with ambiguities in defining temporal questions and lacks a systematic categorization of existing methods for TKGQA. In response, this paper provides a thorough survey from two perspectives: the taxonomy of temporal questions and the methodological categorization for TKGQA. Specifically, we first establish a detailed taxonomy of temporal questions engaged in prior studies. Subsequently, we provide a comprehensive review of TKGQA techniques of two categories: semantic parsing-based and TKG embedding-based. Building on this review, the paper outlines potential research directions aimed at advancing the field of TKGQA. This work aims to serve as a comprehensive reference for TKGQA and to stimulate further research.

翻译：知识库问答（KBQA）是一个长期存在的领域，旨在基于知识库回答问题。近年来，知识的动态演化特性引发了人们对时序知识图谱问答（TKGQA）日益增长的兴趣，这是一项回答时序问题的新兴任务。然而，该领域在时序问题的定义上存在模糊性，并且缺乏对现有TKGQA方法的系统性分类。为此，本文从两个视角提供了全面的综述：时序问题的分类学以及TKGQA的方法论分类。具体而言，我们首先对先前研究中涉及的时序问题建立了细致的分类体系。随后，我们对基于语义解析和基于TKG嵌入的两类TKGQA技术进行了全面回顾。基于此综述，本文概述了旨在推动TKGQA领域发展的潜在研究方向。本工作旨在为TKGQA提供一个全面的参考，并激励进一步的研究。