用混合检索+自校正生成，自动梳理文献并发现研究空白。
HySemRAG: A Hybrid Semantic Retrieval-Augmented Generation Framework for Automated Literature Synthesis and Methodological Gap Analysis
- 融合语义搜索、关键词过滤与知识图谱实现混合检索
- 自校正机制使单次成功率达68.3%，引文准确率99.0%
- 适合需要快速综述文献或找研究缺口的科研人员
我们提出HySemRAG框架，结合ETL流程与检索增强生成（RAG），实现大规模文献综述与方法论研究空白识别。系统通过多层设计克服现有RAG局限：混合检索融合语义搜索、关键词过滤与知识图谱遍历；代理式自校正框架实现迭代质量保证；事后引文验证确保可追溯性。系统经八个集成阶段处理学术文献：多源元数据获取、异步PDF下载、基于改进Docling架构的定制文档布局分析、参考文献管理、大模型字段提取、主题建模、语义统一与知识图谱构建。产出双类数据产品——支持复杂关系查询的Neo4j知识图谱与支持语义搜索的Qdrant向量集合，构成可验证信息合成的基础架构。在60次测试会话中对643个样本评估显示，结构化字段提取的语义相似度达0.655±0.178，较传统分块方式（0.485±0.204）提升35.1%（p<0.000001）。代理质量保证机制在验证响应中实现68.3%单次成功率与99.0%引文准确率。应用于地缘流行病学领域关于臭氧暴露与心血管疾病的研究文献，成功识别方法趋势与研究空白，证明其在跨科学领域加速证据综合与发现的普适性。
原文摘要 · Abstract (English)
We present HySemRAG, a framework that combines Extract, Transform, Load (ETL) pipelines with Retrieval-Augmented Generation (RAG) to automate large-scale literature synthesis and identify methodological research gaps. The system addresses limitations in existing RAG architectures through a multi-layered approach: hybrid retrieval combining semantic search, keyword filtering, and knowledge graph traversal; an agentic self-correction framework with iterative quality assurance; and post-hoc citation verification ensuring complete traceability. Our implementation processes scholarly literature through eight integrated stages: multi-source metadata acquisition, asynchronous PDF retrieval, custom document layout analysis using modified Docling architecture, bibliographic management, LLM-based field extraction, topic modeling, semantic unification, and knowledge graph construction. The system creates dual data products - a Neo4j knowledge graph enabling complex relationship queries and Qdrant vector collections supporting semantic search - serving as foundational infrastructure for verifiable information synthesis. Evaluation across 643 observations from 60 testing sessions demonstrates structured field extraction achieving 35.1% higher semantic similarity scores (0.655 $\pm$ 0.178) compared to PDF chunking approaches (0.485 $\pm$ 0.204, p < 0.000001). The agentic quality assurance mechanism achieves 68.3% single-pass success rates with 99.0% citation accuracy in validated responses. Applied to geospatial epidemiology literature on ozone exposure and cardiovascular disease, the system identifies methodological trends and research gaps, demonstrating broad applicability across scientific domains for accelerating evidence synthesis and discovery.
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