Recent advancements have witnessed the ascension of Large Language Models (LLMs), endowed with prodigious linguistic capabilities, albeit marred by shortcomings including factual inconsistencies and opacity. Conversely, Knowledge Graphs (KGs) harbor verifiable knowledge and symbolic reasoning prowess, thereby complementing LLMs' deficiencies. Against this backdrop, the synergy between KGs and LLMs emerges as a pivotal research direction. Our contribution in this paper is a comprehensive dissection of the latest developments in integrating KGs with LLMs. Through meticulous analysis of their confluence points and methodologies, we introduce a unifying framework designed to elucidate and stimulate further exploration among scholars engaged in cognate disciplines. This framework serves a dual purpose: it consolidates extant knowledge while simultaneously delineating novel avenues for real-world deployment, thereby amplifying the translational impact of academic research.

翻译：近期进展见证了大型语言模型（LLM）的崛起，其虽具备卓越的语言能力，但仍存在事实不一致性和不透明性等缺陷。相比之下，知识图谱（KG）蕴含可验证的知识与符号推理能力，从而能弥补LLM的不足。在此背景下，KG与LLM的协同已成为关键研究方向。本文的贡献在于对KG与LLM融合的最新进展进行全面剖析。通过细致分析两者的交汇点与方法论，我们提出了一个统一框架，旨在阐明并激发相关领域学者的进一步探索。该框架具有双重作用：既整合现有知识，同时勾勒出实际部署的新路径，从而增强学术研究的转化影响力。