Summary of Temporal Knowledge Graph Question Answering: a Survey, by Miao Su et al.
Temporal Knowledge Graph Question Answering: A Survey
by Miao Su, Zixuan Li, Zhuo Chen, Long Bai, Xiaolong Jin, Jiafeng Guo
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper provides a thorough survey on Temporal Knowledge Graph Question Answering (TKGQA), an emerging task that answers temporal questions based on knowledge graphs. The survey covers two perspectives: a taxonomy of temporal questions and methodological categorization for TKGQA. Specifically, the authors establish a detailed taxonomy of temporal questions engaged in prior studies and review techniques from 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TKGQA is an emerging task that answers temporal questions based on knowledge graphs. The paper provides a comprehensive survey on TKGQA, including a taxonomy of temporal questions and methodological categorization for TKGQA. This work aims to serve as a reference for TKGQA and stimulate further research. |
Keywords
» Artificial intelligence » Embedding » Knowledge graph » Question answering » Semantic parsing
