CommunityKG-RAG: Leveraging Community Structures in Knowledge Graphs for Advanced Retrieval-Augmented Generation in Fact-Checking
<|reference_start|>CommunityKG-RAG: Leveraging Community Structures in Knowledge Graphs for Advanced Retrieval-Augmented Generation in Fact-Checking: Despite advancements in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems, their effectiveness is often hindered by a lack of integration with entity relationships and community structures, limiting their ability to provide contextually rich and accurate information retrieval for fact-checking. We introduce CommunityKG-RAG (Community Knowledge Graph-Retrieval Augmented Generation), a novel zero-shot framework that integrates community structures within Knowledge Graphs (KGs) with RAG systems to enhance the fact-checking process. Capable of adapting to new domains and queries without additional training, CommunityKG-RAG utilizes the multi-hop nature of community structures within KGs to significantly improve the accuracy and relevance of information retrieval. Our experimental results demonstrate that CommunityKG-RAG outperforms traditional methods, representing a significant advancement in fact-checking by offering a robust, scalable, and efficient solution.<|reference_end|>
@article{chang2024communitykg-rag:,
    title={CommunityKG-RAG: Leveraging Community Structures in Knowledge Graphs for
  Advanced Retrieval-Augmented Generation in Fact-Checking},
    author={Rong-Ching Chang, Jiawei Zhang},
    journal={arXiv preprint arXiv:2408.08535},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08535},
    primaryClass={cs.CL}
}