Request Correction

Efficient KG-Augmented RAG with Reusable Graph Community Summaries

Retrieval-augmented generation (RAG) performs well for localized factual queries but struggles with complex questions requiring multi-section evidence integration. Graph-based approaches introduce relational structure, yet their practical integration into QA pipelines involves significant query-time overhead. We present a practical KG-augmented RAG (KG-RAG) design that builds a knowledge graph offline with an LLM, converts graph communities into reusable summaries, and retrieves these summaries jointly with textual evidence at query time. We compare dense RAG, pure GraphRAG, and the proposed hybrid on two benchmarks representing complementary retrieval paradigms: QASPER (intra-document reasoning over scientific papers) and ObliQA (cross-document reasoning over regulatory texts). Results show that pure GraphRAG does not consistently outperform dense retrieval, whereas the hybrid configuration systematically improves relevance, correctness, and completeness while maintaining substantially lower latency than full graph-based inference.