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.

翻译：维护全面且最新的知识图谱对于现代人工智能系统至关重要，但人工构建方式难以适应科学文献的快速增长。本文提出KARMA，一种利用多智能体大语言模型通过非结构化文本的结构化分析实现知识图谱自动增强的新型框架。该方法部署九个协同智能体，涵盖实体发现、关系抽取、模式对齐和冲突消解，这些智能体迭代解析文档、验证抽取知识，并将其整合至现有图结构，同时遵循领域特定模式。在来自三个不同领域的1,200篇PubMed文献上的实验表明，KARMA在知识图谱增强方面具有显著效果，最多识别出38,230个新实体，同时达到83.1%的LLM验证正确率，并通过多层评估将冲突边减少了18.6%。