TagRAG: Tag-guided Hierarchical Knowledge Graph Retrieval-Augmented Generation

Retrieval-Augmented Generation enhances language models by retrieving external knowledge to support informed and grounded responses.However, traditional RAG methods rely on fragment-level retrieval, limiting their ability to address query-focused summarization queries.GraphRAG introduces a graphbased paradigm for global knowledge reasoning, yet suffers from inefficiencies in information extraction, costly resource consumption, and poor adaptability to incremental updates.To overcome these limitations, we propose TagRAG, a tag-guided hierarchical knowledge graph RAG framework designed for efficient global reasoning and scalable graph maintenance.TagRAG introduces two key components: (1) Tag Knowledge Graph Construction, which extracts object tags and their relationships from documents and organizes them into hierarchical domain tag chains for structured knowledge representation, and (2) Tag-Guided Retrieval-Augmented Generation, which retrieves domain-centric tag chains to localize and synthesize relevant knowledge during inference.This design significantly adapts to smaller language models, improves retrieval granularity, and supports efficient knowledge increment.Extensive experiments on Ultra-Domain datasets spanning Agriculture, Computer Science, Law, and cross-domain settings demonstrate that TagRAG achieves an average winning rate of 78.36% against baselines while maintaining about 14.6x construction and 1.9x retrieval efficiency compared with GraphRAG.... ...