HG-RAG: Hierarchy-Guided Retrieval-Augmented Generation for Structured Knowledge Graphs

Jul 17, 2026

HG-RAG is a framework that enhances retrieval-augmented generation (RAG) by performing graph traversal over hierarchical knowledge graphs to provide structured context to large language models. The method retrieves context by resolving named entities and expanding through parent, child, and relational nodes as needed. In evaluations across multiple query types and graph sizes, HG-RAG outperformed flat retrieval baselines on hierarchical, relational, and multi-hop reasoning tasks, and reduced hallucination.

Why it matters: This work advances RAG methods for structured knowledge, enabling more accurate reasoning over complex hierarchical and relational queries.

Full story at: arXiv AI/ML ↗