用知识图谱提升大模型问答，减少冗余检索并增强跨领域推理。
LeanRAG: Knowledge-Graph-Based Generation with Semantic Aggregation and Hierarchical Retrieval
- 通过语义聚合构建可导航的多层知识网络，打通概念间的关联断点。
- 采用自底向上的结构化检索，响应质量显著提升，冗余检索降低46%。
- 适合需要精准知识推理的复杂问答场景，尤其在多领域任务中表现优异。
检索增强生成（RAG）通过引入外部知识来增强大语言模型的准确性，但常因检索到的信息不完整或上下文错误而受限。基于知识图谱的RAG方法虽发展出分层结构，将知识组织为多级摘要，但仍面临两大未解挑战：高层概念摘要形成孤立的‘语义孤岛’，缺乏跨社区推理所需的显式关系；检索过程本身缺乏结构感知，常退化为低效的扁平搜索，未能利用图结构的丰富拓扑。为此，本文提出LeanRAG框架，融合知识聚合与检索策略，实现深度协同。首先，设计新型语义聚合算法，对实体聚类并构建聚合层级摘要间的显式关系，形成完整可导航的语义网络；其次，采用自底向上的结构引导检索策略，将查询锚定至最相关的细粒度实体，并沿语义路径系统遍历，获取简洁且上下文完整的证据集。该方法有效缓解图路径检索的高开销问题，最小化冗余信息获取。在四个不同领域的挑战性QA基准上进行的大量实验表明，LeanRAG在响应质量上显著优于现有方法，同时减少46%的检索冗余。代码已公开于：https://github.com/RaZzzyz/LeanRAG。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) plays a crucial role in grounding Large Language Models by leveraging external knowledge, whereas the effectiveness is often compromised by the retrieval of contextually flawed or incomplete information. To address this, knowledge graph-based RAG methods have evolved towards hierarchical structures, organizing knowledge into multi-level summaries. However, these approaches still suffer from two critical, unaddressed challenges: high-level conceptual summaries exist as disconnected ``semantic islands'', lacking the explicit relations needed for cross-community reasoning; and the retrieval process itself remains structurally unaware, often degenerating into an inefficient flat search that fails to exploit the graph's rich topology. To overcome these limitations, we introduce LeanRAG, a framework that features a deeply collaborative design combining knowledge aggregation and retrieval strategies. LeanRAG first employs a novel semantic aggregation algorithm that forms entity clusters and constructs new explicit relations among aggregation-level summaries, creating a fully navigable semantic network. Then, a bottom-up, structure-guided retrieval strategy anchors queries to the most relevant fine-grained entities and then systematically traverses the graph's semantic pathways to gather concise yet contextually comprehensive evidence sets. The LeanRAG can mitigate the substantial overhead associated with path retrieval on graphs and minimizes redundant information retrieval. Extensive experiments on four challenging QA benchmarks with different domains demonstrate that LeanRAG significantly outperforming existing methods in response quality while reducing 46\% retrieval redundancy. Code is available at: https://github.com/RaZzzyz/LeanRAG
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