Temporal Knowledge Graph Question Answering: A Survey
<|reference_start|>Temporal Knowledge Graph Question Answering: A Survey: 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.<|reference_end|>
@article{su2024temporal,
    title={Temporal Knowledge Graph Question Answering: A Survey},
    author={Miao Su, Zixuan Li, Zhuo Chen, Long Bai, Xiaolong Jin, Jiafeng Guo},
    journal={arXiv preprint arXiv:2406.14191},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.14191},
    primaryClass={cs.CL cs.AI cs.LG}
}