KARMA: Leveraging Multi-Agent LLMs for Automated Knowledge Graph Enrichment

Yuxing Lu, Wei Wu, Xukai Zhao, Rui Peng, Jinzhuo Wang

Keywords: Knowledge Graphs, Multi Agent System

TL;DR: KARMA is a multi-agent LLM framework that automatically update and expand knowledge graphs from scientific papers. By using nine specialized agents, KARMA successfully add new entities with high verified accuracy and reduce inconsistencies.

Abstract: 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.

Primary Area: Machine learning for sciences (e.g. climate, health, life sciences, physics, social sciences)

Submission Number: 9292