探索知识图谱与大模型的双向协同，提升事实准确性与推理能力。
From Symbolic to Neural and Back: Exploring Knowledge Graph-Large Language Model Synergies
- 双向融合：用知识图谱增强大模型推理，用大模型辅助知识图谱构建
- 有效降低幻觉，支持复杂问答任务，提升系统可信度
- 适合研究智能系统、知识融合与可信赖AI的学者与工程师
将知识图谱（KG）中的结构化知识融入大型语言模型（LLMs），可增强事实依据和推理能力。本文系统梳理了KG与LLM之间的协同机制，将现有方法分为两类：以知识图谱增强大模型，提升推理性能、减少幻觉并支持复杂问答；以及以大模型增强知识图谱，助力知识图谱的构建、补全与查询。通过全面分析，识别出关键差距，并强调双方互益潜力。相比已有综述，本研究特别关注可扩展性、计算效率与数据质量。最后提出未来方向，包括神经符号融合、动态知识图谱更新、数据可靠性及伦理考量，为应对更复杂的现实知识任务提供路径。
原文摘要 · Abstract (English)
Integrating structured knowledge from Knowledge Graphs (KGs) into Large Language Models (LLMs) enhances factual grounding and reasoning capabilities. This survey paper systematically examines the synergy between KGs and LLMs, categorizing existing approaches into two main groups: KG-enhanced LLMs, which improve reasoning, reduce hallucinations, and enable complex question answering; and LLM-augmented KGs, which facilitate KG construction, completion, and querying. Through comprehensive analysis, we identify critical gaps and highlight the mutual benefits of structured knowledge integration. Compared to existing surveys, our study uniquely emphasizes scalability, computational efficiency, and data quality. Finally, we propose future research directions, including neuro-symbolic integration, dynamic KG updating, data reliability, and ethical considerations, paving the way for intelligent systems capable of managing more complex real-world knowledge tasks.
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