用问题驱动分图，让多智能体协作检索，提升大知识图谱的问答效率与准确率。
Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning
- 根据问题类型动态划分知识图谱为语义一致的子图
- 仅激活相关子图，搜索空间缩小，推理效率显著提升
- 多智能体协同+分层合并，适合复杂多跳问答场景
检索增强生成（RAG）系统为大语言模型提供外部知识，但在扩展至大规模知识图谱时面临效率与准确率的权衡。现有方法多采用整体图检索，简单问题产生冗余延迟，复杂多跳问题则导致推理碎片化。本文提出SPLIT-RAG，一种基于问题驱动的语义图分割与多智能体协作的RAG框架。首先对关联信息进行语义分区，构建类型特化的知识库；通过属性感知的图分割，将知识图谱划分为语义连贯的子图，使其与不同查询类型对齐；轻量级LLM智能体被分配至各子图，仅在检索时激活相关分区，从而缩减搜索空间并提升效率；最后，通过层次化合并模块，基于逻辑验证解决子图答案间的不一致性。大量实验表明，该方法在多个基准上显著优于现有方案。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) systems empower large language models (LLMs) with external knowledge, yet struggle with efficiency-accuracy trade-offs when scaling to large knowledge graphs. Existing approaches often rely on monolithic graph retrieval, incurring unnecessary latency for simple queries and fragmented reasoning for complex multi-hop questions. To address these challenges, this paper propose SPLIT-RAG, a multi-agent RAG framework that addresses these limitations with question-driven semantic graph partitioning and collaborative subgraph retrieval. The innovative framework first create Semantic Partitioning of Linked Information, then use the Type-Specialized knowledge base to achieve Multi-Agent RAG. The attribute-aware graph segmentation manages to divide knowledge graphs into semantically coherent subgraphs, ensuring subgraphs align with different query types, while lightweight LLM agents are assigned to partitioned subgraphs, and only relevant partitions are activated during retrieval, thus reduce search space while enhancing efficiency. Finally, a hierarchical merging module resolves inconsistencies across subgraph-derived answers through logical verifications. Extensive experimental validation demonstrates considerable improvements compared to existing approaches.
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