TagRAG: Tag-guided Hierarchical Knowledge Graph Retrieval-Augmented Generation
Wenbiao Tao, Xinyuan Li, Yunshi Lan, Weining Qian

TL;DR
TagRAG introduces a hierarchical, tag-guided knowledge graph retrieval framework that enhances efficiency and scalability for knowledge reasoning in language models, especially for query-focused tasks.
Contribution
It proposes a novel tag-guided hierarchical knowledge graph construction and retrieval method that improves efficiency, adaptability, and reasoning capabilities over existing graph-based RAG approaches.
Findings
Achieves 78.36% average winning rate against baselines.
Provides 14.6x faster graph construction and 1.9x faster retrieval.
Demonstrates effectiveness across diverse UltraDomain datasets.
Abstract
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 graph-based 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…
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