On the Evolution of Knowledge Graphs: A Survey and Perspective
It provides a comprehensive overview for researchers and practitioners in AI and knowledge engineering, but is incremental as a survey.
This paper surveys the evolution of knowledge graphs, covering types like static and dynamic KGs, techniques for extraction and reasoning, and applications such as financial analysis, and proposes future directions including integration with large language models.
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.