Temporal Knowledge Graph Question Answering: A Survey

TL;DR

This survey reviews the emerging field of Temporal Knowledge Graph Question Answering (TKGQA), categorizing existing methods and identifying future research directions to address the challenges of temporal question answering.

Contribution

It provides the first systematic taxonomy of temporal questions and categorizes TKGQA methods into semantic parsing-based and embedding-based approaches.

Findings

Established a detailed taxonomy of temporal questions.

Reviewed TKGQA techniques in two main categories.

Outlined future research directions in TKGQA.

Abstract

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.