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

Researchers introduced HG-RAG, a framework that enhances retrieval-augmented generation by traversing hierarchical knowledge graphs to provide structured context to large language models. In evaluations across three world scales and four query types, HG-RAG outperformed flat retrieval baselines on hierarchical, relational, and multi-hop reasoning tasks while reducing hallucination.

arXiv:2607.14095v1 Announce Type: new Abstract: Retrieval Augmented Generation RAG has proven to be a widely successful process at improving the quality of outputs from a Large Language Model LLM for wider context. However, RAG systems typically retrieve context from flat document stores, which struggles when queries require hierarchical or relational reasoning across structured knowledge. I present HG-RAG Hierarchy-Guided RAG , a framework that performs graph-traversal over a hierarchical knowledge graph to deliver structured context to a language model. My retrieval pipeline resolves a named entity anchor from the query, then expands context upward through parent nodes, laterally through relational neighbors, and downward through child nodes when needed. I evaluate HG-RAG against a dense retrieval baseline across three world scales 18-800 nodes with four query types: local fact, hierarchical, neighborhood, and multi-hop. Results show HG-RAG consistently outperforms the flat baseline on hierarchical, relational, and multi-hop reasoning tasks, while reducing hallucination and maintaining locality coherence.