KARMA: Leveraging Multi-Agent LLMs for Automated Knowledge Graph Enrichment
This work addresses the problem of scaling knowledge graph maintenance for AI systems, particularly in domains with rapid growth of scientific literature, such as biomedical research.
The authors tackled the problem of maintaining comprehensive knowledge graphs by developing KARMA, a framework that automates knowledge graph enrichment, resulting in the identification of up to 38,230 new entities with 83.1% correctness. The framework also reduced conflict edges by 18.6%.
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.