用知识图谱增强检索生成，提升垂直领域问答的准确性和一致性。
DO-RAG: A Domain-Specific QA Framework Using Knowledge Graph-Enhanced Retrieval-Augmented Generation
- 通过多层级知识图谱与向量检索融合，动态构建结构化知识。
- 在数据库和电气领域实现近似完美召回率，答案相关性超94%。
- 适合需要高精度、可追溯的工业级问答系统开发者使用。
垂直领域问答系统不仅需要生成流畅，更需基于结构化专家知识保证事实准确性。尽管近期检索增强生成（RAG）框架提升了上下文召回能力，但在整合异构数据和维持推理一致性方面仍存在挑战。为此，我们提出DO-RAG，一种可扩展、可定制的混合问答框架，结合多层次知识图谱构建与语义向量检索。系统采用新型代理式思维链架构，从非结构化、多模态文档中提取结构化关系，动态构建知识图谱以增强检索精度。查询时，DO-RAG融合图谱与向量检索结果生成上下文感知响应，并通过基于事实的精炼机制抑制幻觉。在数据库与电气领域的实验表明，系统实现近似完美召回率，答案相关性超过94%，性能优于基线框架最高达33.38%。通过可追溯性、可适应性与性能效率的结合，DO-RAG为大规模多领域高精度问答提供了可靠基础。
原文摘要 · Abstract (English)
Domain-specific QA systems require not just generative fluency but high factual accuracy grounded in structured expert knowledge. While recent Retrieval-Augmented Generation (RAG) frameworks improve context recall, they struggle with integrating heterogeneous data and maintaining reasoning consistency. To address these challenges, we propose DO-RAG, a scalable and customizable hybrid QA framework that integrates multi-level knowledge graph construction with semantic vector retrieval. Our system employs a novel agentic chain-of-thought architecture to extract structured relationships from unstructured, multimodal documents, constructing dynamic knowledge graphs that enhance retrieval precision. At query time, DO-RAG fuses graph and vector retrieval results to generate context-aware responses, followed by hallucination mitigation via grounded refinement. Experimental evaluations in the database and electrical domains show near-perfect recall and over 94% answer relevancy, with DO-RAG outperforming baseline frameworks by up to 33.38%. By combining traceability, adaptability, and performance efficiency, DO-RAG offers a reliable foundation for multi-domain, high-precision QA at scale.
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