BG-RAG: Concept-Mediated Bipartite Graphs for Scalable RAG

Abstract

Graph-structured retrieval-augmented generation (GraphRAG) has shown strong potential for improving multi-hop reasoning in large language models. However, existing approaches typically depend on dense entity–entity linking, which is computationally expensive, susceptible to hallucinated relations, and difficult to maintain under continual updates. We present Bipartite Graph Retrieval-Augmented Generation (BG-RAG), a lightweight bipartite framework that organizes knowledge into three layers, from chunks to entities to concepts, and replaces direct entity links with concept-mediated connections. This design enables more efficient graph construction, reduces reliance on hallucination-prone relation-edge prediction by avoiding explicit entity–entity relation extraction, and naturally supports incremental updates without global reconstruction. Experiments on HotpotQA, 2Wiki, and MuSiQue show that BG-RAG achieves competitive performance against RAG and GraphRAG baselines. Its concept-mediated structure also provides a lightweight alternative to dense entity–entity graph construction. These results suggest that BG-RAG is a lightweight alternative for retrieval-augmented generation, particularly when avoiding dense entity–entity linking is desirable.