StructRAG: Structure-Aware RAG Framework with Scholarly Knowledge Graph for Diverse Question Answering

Recent advances in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) have shown promise in academic question answering. However, existing approaches often fail to fully utilize document structural information and lack diversity in retrieved contexts. This paper presents StructRAG, a structure-aware RAG framework that leverages scholarly knowledge graphs for enhanced question answering. Our framework features three key innovations: (1) an automated knowledge graph construction pipeline based on Deep Document Model (DDM) that preserves document hierarchical structure, (2) a structure-aware retrieval mechanism that combines semantic relevance with source diversity, and (3) a context-enhanced generation approach that integrates structural metadata for improved answer synthesis. Experimental results on 329 computer science papers demonstrate that StructRAG significantly outperforms vanilla RAG baseline. While maintaining comparable semantic accuracy (91% vs 90%), our approach achieves substantially higher diversity in generated answers (Distinct-1: 62% vs 52%, Distinct-2: 89% vs 78%) and better answer quality across all metrics, with notable improvements in relevance (29%) and readability (36.5%). These results demonstrate that StructRAG effectively enhances both the diversity and quality of academic question answering.