DualGraphRAG: A Dual-View Graph-Enhanced Retrieval-Augmented Generation Framework for Reliable and Efficient Question Answering

Graph-enhanced Retrieval-Augmented Generation (RAG) frameworks, such as GraphRAG, improve large language model (LLM)-based question answering (QA) by constructing and leveraging structured, knowledge-condensed graph information. However, they still face challenges in complex multi-hop reasoning tasks and often incur substantial time and resource costs, resulting in low efficiency. To address these limitations, we propose DualGraphRAG, a dual-view graph-enhanced RAG framework designed to achieve both high QA performance and computational efficiency for complex reasoning over open-domain corpora. Specifically, DualGraphRAG constructs a knowledge graph (KG) by automatically extracting triples from unstructured text using LLMs, and embeds KG nodes with unified text embeddings. For each query, multiple types of KG nodes are generated through a dedicated query enhancement module. Based on these nodes, DualGraphRAG employs a dual-view retrieval strategy to retrieve both one-hop triples that capture local context and shortest paths that compress global connectivity information, thereby facilitating answer generation. Experimental results show that, compared with NaiveRAG, GraphRAG, and LightRAG, DualGraphRAG achieves the best or competitive performance on benchmark datasets and significantly improves efficiency. Overall, DualGraphRAG organizes and exploits KG information in a dual-view manner, leveraging triples and shortest paths to offer a reliable and efficient framework for open-domain QA with complex multi-hop reasoning.