This October 23, 2025 Xidian University academic survey systematically reviews the transformative impact of **Large Language Models (LLMs)** on the three core stages of **Knowledge Graph (KG) construction**: ontology engineering, knowledge extraction, and knowledge fusion. The text explains that LLMs are shifting the paradigm from rigid, rule-based systems to **unified, adaptive, and generative frameworks**. The paper is structured to first revisit traditional KG methodologies before examining emerging LLM-driven approaches, which are categorized into **schema-based** (emphasizing structure) and **schema-free** (emphasizing flexibility) paradigms across all stages. The authors outline how LLMs function as either **ontology assistants (top-down)** or as consumers of KGs for grounding and memory **(bottom-up)**, culminating in a discussion of future directions such as KG-based reasoning and dynamic knowledge memory for agentic systems. Ultimately, the work aims to clarify the evolving relationship between symbolic knowledge engineering and neural semantic understanding.

Source:

https://arxiv.org/pdf/2510.20345