用多智能体LLM自动扩充知识图谱，提升准确率并减少冲突。
KARMA: Leveraging Multi-Agent LLMs for Automated Knowledge Graph Enrichment
- 九个协作智能体分阶段完成实体发现、关系抽取与验证。
- 从1200篇文献中识别出3.8万新实体，正确率达83.1%。
- 适合需要高精度知识更新的研究者和工业级应用。
维护全面且最新的知识图谱对现代AI系统至关重要，但人工整理难以跟上科学文献的快速增长。本文提出KARMA框架，利用多智能体大语言模型（LLMs）通过结构化分析非结构化文本实现知识图谱的自动化扩充。该方法包含九个协同工作的智能体，涵盖实体发现、关系抽取、模式对齐与冲突解决，能迭代解析文档、验证提取的知识，并将其整合到现有图结构中，同时遵循领域特定的模式。在三个不同领域的1,200篇PubMed文章上的实验表明，KARMA在知识图谱扩充方面表现优异，共识别出最多38,230个新实体，经LLM验证的正确率达到83.1%，并通过多层评估将冲突边减少了18.6%。
原文摘要 · Abstract (English)
Maintaining comprehensive and up-to-date knowledge graphs (KGs) is critical for modern AI systems, but manual curation struggles to scale with the rapid growth of scientific literature. This paper presents KARMA, a novel framework employing multi-agent large language models (LLMs) to automate KG enrichment through structured analysis of unstructured text. Our approach employs nine collaborative agents, spanning entity discovery, relation extraction, schema alignment, and conflict resolution that iteratively parse documents, verify extracted knowledge, and integrate it into existing graph structures while adhering to domain-specific schema. Experiments on 1,200 PubMed articles from three different domains demonstrate the effectiveness of KARMA in knowledge graph enrichment, with the identification of up to 38,230 new entities while achieving 83.1\% LLM-verified correctness and reducing conflict edges by 18.6\% through multi-layer assessments.
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