Structured Knowledge for Multi-hop QA: A Comparative Study of GraphRAG and RAG

This study comparatively examines the classical RAG approach and the knowledge graph-based GraphRAG architecture in multi-hop question answering. While RAG uses external knowledge in an unstructured manner, GraphRAG aims to provide more controlled and meaningful answers by relying on structured knowledge triples, thereby reducing hallucinations. In the experiments, both architectures were tested on 500 questions selected from the HotpotQA dataset, and their performances were compared. In particular, the questions that the RAG system answered with “I don’t know” were re-evaluated using GraphRAG. In the GraphRAG pipeline, knowledge triples were first extracted from the context, and then the same language model performed question analysis—identifying the question type, the expected reasoning pattern, and selecting the most relevant triples. Answers were generated using only filtered and contextually appropriate structured information.The results show that incorporating structured knowledge provides a clear improvement in semantic answer quality. On average, both cosine similarity and BERT F1 scores increased by 20–30% across the tested subsets. Moreover, GraphRAG successfully answered approximately 80% of the questions that the classical RAG system could not answer. These findings demonstrate that structured knowledge enables more reliable reasoning in multi-step QA and highlight the potential of the GraphRAG approach as a stronger alternative for complex question answering tasks.