Abstract: With the widespread application of knowledge graphs (KGs) in fields such as intelligent question answering and recommender systems, the technical bottlenecks in large-scale construction and efficient reasoning have become increasingly prominent. Traditional manual or semi-automated construction approaches are costly, while issues such as entity disambiguation and relation extraction accuracy continue to hinder the quality of the resulting graphs. Furthermore, knowledge sparsity and the complexity of reasoning rules limit the generalization capability of KG reasoning. Large language models (LLMs), with their powerful semantic understanding and contextual modeling capabilities, offer promising new avenues to address these challenges. However, current research in this area lacks a systematic review, and the applicability and performance boundaries of various methods remain unclear. To bridge this gap, this paper provides a comprehensive survey of LLM-enhanced knowledge graph construction and reasoning methods. Firstly, this paper introduces the foundational theories of knowledge graphs and large language models. The survey then focuses on four core tasks: knowledge extraction, automated construction, knowledge completion, and reasoning. For knowledge extraction, this paper compares zero-shot extraction methods based on LLMs with domain-adapted extraction through fine-tuning. In terms of automated construction, this paper reviews techniques for LLM-driven ontology generation and iterative graph updates. For knowledge completion, this paper summarizes methods involving pseudo-triple generation via LLMs, prompt-based context planning, and the integration of external retrieval mechanisms. Regarding reasoning tasks, this paper analyzes both static LLM-augmented reasoning and actively planned reasoning approaches. This paper further presents typical application scenarios in domains such as healthcare and education, and compiles a list of general-purpose and domain-specific knowledge graph datasets in both English and Chinese that support research in this area. Finally, this paper highlights the current limitations of existing methods and proposes several future research directions.

Key words: large language models, knowledge graphs, knowledge graph construction, knowledge graph reasoning

摘要： 随着知识图谱（KG）在智能问答、推荐系统等领域的广泛应用，其规模化构建与高效推理的技术瓶颈日益凸显。传统人工与半自动化构建模式成本高昂，实体歧义消解、关系抽取准确率不足等问题导致图谱质量难以保障；知识稀疏性与推理规则复杂性限制了图谱推理的泛化能力。大模型（LLM）凭借强大的语义理解与上下文建模能力，为突破这些瓶颈提供了新路径，但目前相关技术缺乏系统性梳理，不同方法的适用场景与性能边界尚不清晰，因此，对现有的大模型增强知识图谱构建和推理的方法开展系统性综述。介绍了知识图谱和大模型相关的基础理论。聚焦知识抽取、自动化构建、知识补全与推理四类核心任务，剖析现有方法：知识抽取中，对比基于大模型的零样本知识抽取与微调领域适配抽取；自动化构建方面，梳理大模型驱动的本体自动生成、图谱迭代更新技术；知识补全方面，总结了借助大模型生成伪三元组、提示规划上下文与结合外部检索机制的方法；推理任务中，分析了基于大模型的静态增强推理和主动规划推理技术。介绍了该研究在医疗、教育等领域的典型应用场景，梳理了支撑相关研究的中英文通用和垂直领域知识图谱数据集资源。针对现存方法的局限性提出了未来研究方向。

关键词: 大模型, 知识图谱, 知识图谱构建, 知识图谱推理