Temporal Knowledge Graph Question Answering: A Survey
This is an incremental survey paper that organizes and clarifies the emerging field of TKGQA for researchers and practitioners.
This paper addresses the lack of systematic categorization in Temporal Knowledge Graph Question Answering (TKGQA) by providing a thorough survey that establishes a taxonomy of temporal questions and reviews TKGQA techniques, aiming to serve as a comprehensive reference and stimulate further research.
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.