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