On the Evolution of Knowledge Graphs: A Survey and Perspective

Xuhui Jiang, Chengjin Xu, Yinghan Shen, Xun Sun, Lumingyuan Tang +4 more
10/7/2023
cs.AI

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Abstract

Knowledge graphs (KGs) are structured representations of diversified knowledge. They are widely used in various intelligent applications. In this article, we provide a comprehensive survey on the evolution of various types of knowledge graphs (i.e., static KGs, dynamic KGs, temporal KGs, and event KGs) and techniques for knowledge extraction and reasoning. Furthermore, we introduce the practical applications of different types of KGs, including a case study in financial analysis. Finally, we propose our perspective on the future directions of knowledge engineering, including the potential of combining the power of knowledge graphs and large language models (LLMs), and the evolution of knowledge extraction, reasoning, and representation.

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Cite this paper

@article{jiang2023evolution,
  title  = {On the Evolution of Knowledge Graphs: A Survey and Perspective},
  author = {Xuhui Jiang and Chengjin Xu and Yinghan Shen and Xun Sun and Lumingyuan Tang and Saizhuo Wang and Zhongwu Chen and Yuanzhuo Wang and Jian Guo},
  year   = {2023},
  eprint = {2310.04835},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.04835v3}
}

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