摘要
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 per-formance 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 extrac-tion,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 con-struction,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.关键词
大模型/知识图谱/知识图谱构建/知识图谱推理Key words
large language models/knowledge graphs/knowledge graph construction/knowledge graph reasoning分类
计算机与自动化
