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）是基于知识库回答问题的长期研究领域。近年来，知识的动态演化特性引发了学界对时序知识图谱问答（TKGQA）日益增长的研究兴趣——这是一项旨在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，且缺乏对现有TKGQA方法的系统性分类。为此，本文从两个视角展开全面综述：时序问题的分类体系与TKGQA的方法论归类。具体而言，我们首先对已有研究涉及的时序问题建立了细粒度分类体系；随后，系统回顾了两类TKGQA技术：基于语义解析的方法与基于时序知识图谱嵌入的方法。在此基础上，本文进一步展望了推动TKGQA领域发展的潜在研究方向。本工作旨在为时序知识图谱问答提供系统性的研究参考，并促进该领域的深入探索。