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DO-RAG: A Domain-Specific QA Framework Using Knowledge Graph-Enhanced Retrieval-Augmented Generation

Proposes a hybrid QA framework integrating knowledge graph construction and semantic vector retrieval.

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DO-RAG: A Domain-Specific QA Framework Using Knowledge Graph-Enhanced Retrieval-Augmented Generation

By David Osei Opoku, Ming Sheng, Yong Zhang
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DO-RAG is a scalable and customizable QA framework that integrates multi-level knowledge graph construction with semantic vector retrieval. It employs an agentic chain-of-thought architecture to extract structured relationships from unstructured documents, constructing dynamic knowledge graphs.

The system fuses graph and vector retrieval results to generate context-aware responses.

Abstract

DO-RAG is a scalable and customizable QA framework that integrates multi-level knowledge graph construction with semantic vector retrieval. It employs an agentic chain-of-thought architecture to extract structured relationships from unstructured documents, constructing dynamic knowledge graphs. The system fuses graph and vector retrieval results to generate context-aware responses.

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qa-frameworkknowledge-graph-enhanced-retrieval-augmented-generationdomain-specific-qahybrid-qa-systemKnowledge GraphsRetrieval & RAGLarge Language ModelsSemantic Interoperability
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