Hybrid Retrieval-Augmented Generation: Semantic and Structural Integration for Large Language Model Reasoning

Recent GraphRAG methods based on knowledge graphs (KGs) primarily rely on either under-reasoning or a structural path-level retriever, which prevents them from jointly capturing fine-grained semantic relevance and explicit multi-hop reasoning paths. This separation often results in semantic mismatch—where logical links are missing—or structural over-constraint in reasoning— where rigid dependencies limit flexible reasoning—thereby degrading both answer accuracy and the reliability of evidence in complex KGQA tasks. To address these issues, we propose HybRAG, a hybrid retrieval framework that synergistically integrates a semantic node-level retriever and structural path-level retriever. HybRAG constructs a hybrid subgraph that jointly reflects the semantic proximity of entities and the relational structures encoded in the KG. Furthermore, we incorporate retrieval-augmented fine-tuning, which enables the model to internalize advanced reasoning strategies for interpreting disparate semantic and structural signals, rather than merely memorizing domain facts. Through extensive experiments on the WebQSP and CWQ benchmarks, we demonstrate that HybRAG effectively bridges the gap between LLM-centric semantic approaches and GNN-centric structural approaches, outperforming single-retriever baselines. Our findings, including detailed sensitivity and ablation analyses, provide empirical evidence that the systematic alignment of semantic and structural signals is essential for ensuring the reasoning reliability and scalability of next-generation GraphRAG systems.