KARMA: Leveraging Multi-Agent LLMs for Automated Knowledge Graph Enrichment
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911364752080896 |
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| author | Lu, Yuxing Wu, Wei Zhao, Xukai Peng, Rui Wang, Jinzhuo |
| author_facet | Lu, Yuxing Wu, Wei Zhao, Xukai Peng, Rui Wang, Jinzhuo |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_06472 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | KARMA: Leveraging Multi-Agent LLMs for Automated Knowledge Graph Enrichment Lu, Yuxing Wu, Wei Zhao, Xukai Peng, Rui Wang, Jinzhuo Computation and Language Artificial Intelligence Computational Engineering, Finance, and Science Digital Libraries 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. |
| title | KARMA: Leveraging Multi-Agent LLMs for Automated Knowledge Graph Enrichment |
| topic | Computation and Language Artificial Intelligence Computational Engineering, Finance, and Science Digital Libraries |
| url | https://arxiv.org/abs/2502.06472 |