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LEGO-GraphRAG: Modularizing Graph-based RAG for Design Space Exploration
https://arxiv.org/abs/2411.05844
This
research paper introduces LEGO-GraphRAG, a modular framework for
improving Retrieval-Augmented Generation (RAG) systems that use
knowledge graphs. The framework systematically categorizes existing RAG
techniques and facilitates the creation of new, more efficient and
effective RAG instances. The authors conduct empirical studies,
evaluating various configurations on large-scale real-world graphs, to
analyze the trade-offs between reasoning quality, runtime efficiency,
and resource costs. Their findings highlight the importance of
balancing these factors when designing GraphRAG systems and suggest a
promising strategy combining structure-based and semantic-augmented
methods. The paper concludes by identifying key areas for future
research in this field.
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By KnowledgeDBhttps://arxiv.org/abs/2411.05844
This
research paper introduces LEGO-GraphRAG, a modular framework for
improving Retrieval-Augmented Generation (RAG) systems that use
knowledge graphs. The framework systematically categorizes existing RAG
techniques and facilitates the creation of new, more efficient and
effective RAG instances. The authors conduct empirical studies,
evaluating various configurations on large-scale real-world graphs, to
analyze the trade-offs between reasoning quality, runtime efficiency,
and resource costs. Their findings highlight the importance of
balancing these factors when designing GraphRAG systems and suggest a
promising strategy combining structure-based and semantic-augmented
methods. The paper concludes by identifying key areas for future
research in this field.