用树形结构重构图数据检索，让大模型更懂层次化知识。
T-Retriever: Tree-based Hierarchical Retrieval Augmented Generation for Textual Graphs
- 将图数据转为语义与结构引导的编码树，动态优化层次划分。
- 在多个图推理任务上超越现有方法，提升回答连贯性与相关性。
- 适合需要理解复杂层级关系的问答系统开发者。
检索增强生成（RAG）显著提升了大语言模型访问外部知识的能力，但现有基于图的RAG方法在处理层次信息时存在两大缺陷：强制分层压缩配额破坏局部图结构，且过度关注拓扑而忽略语义内容。我们提出T-Retriever，一种新框架，将属性图检索重构为基于语义与结构引导的编码树。其核心创新包括：(1) 自适应压缩编码，以全局优化策略替代人工压缩配额，保留图的自然层次组织；(2) 语义-结构熵（S²-Entropy），在构建层次分区时联合优化结构凝聚性与语义一致性。在多种图推理基准上的实验表明，T-Retriever显著优于当前最优RAG方法，对复杂查询生成更连贯、更相关的响应。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) has significantly enhanced Large Language Models' ability to access external knowledge, yet current graph-based RAG approaches face two critical limitations in managing hierarchical information: they impose rigid layer-specific compression quotas that damage local graph structures, and they prioritize topological structure while neglecting semantic content. We introduce T-Retriever, a novel framework that reformulates attributed graph retrieval as tree-based retrieval using a semantic and structure-guided encoding tree. Our approach features two key innovations: (1) Adaptive Compression Encoding, which replaces artificial compression quotas with a global optimization strategy that preserves the graph's natural hierarchical organization, and (2) Semantic-Structural Entropy ($S^2$-Entropy), which jointly optimizes for both structural cohesion and semantic consistency when creating hierarchical partitions. Experiments across diverse graph reasoning benchmarks demonstrate that T-Retriever significantly outperforms state-of-the-art RAG methods, providing more coherent and contextually relevant responses to complex queries.
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