用AI自动分析城市科学论文，让文献检索更懂语义、更懂上下文。
Automating Bibliometric Analysis with Sentence Transformers and Retrieval-Augmented Generation (RAG): A Pilot Study in Semantic and Contextual Search for Customized Literature Characterization for High-Impact Urban Research
- 用句子嵌入与RAG技术构建智能检索流程，实现语义搜索和上下文理解。
- 在223篇《自然·通讯》城市科学论文上验证，精准生成研究质量与特征摘要。
- 适合科研管理者、领域专家快速掌握高影响力期刊的研究趋势。
引文分析对理解城市科学领域的研究趋势、范围与影响力至关重要，尤其在《自然组合》类高影响力期刊中。然而传统方法依赖关键词搜索和基础NLP技术，难以发现未明示于标题或关键词中的深层洞见，缺乏语义搜索与上下文理解能力，限制了主题分类与研究特征刻画效果。本文提出一种基于生成式AI的自动化引文分析框架，结合向量数据库、Sentence Transformers、高斯混合模型（GMM）、检索代理与大语言模型（LLMs），构建集成工作流，支持上下文搜索、主题排序与个性化研究表征。通过在近十年发表于《自然·通讯》的223篇城市科学相关论文上的试点研究，验证了该方法在生成研究质量、覆盖范围与特征等洞察性统计结果方面的有效性。本研究为城市研究的知识检索与引文分析引入新范式，将AI代理定位为提升研究评估与理解能力的强大工具。
原文摘要 · Abstract (English)
Bibliometric analysis is essential for understanding research trends, scope, and impact in urban science, especially in high-impact journals, such Nature Portfolios. However, traditional methods, relying on keyword searches and basic NLP techniques, often fail to uncover valuable insights not explicitly stated in article titles or keywords. These approaches are unable to perform semantic searches and contextual understanding, limiting their effectiveness in classifying topics and characterizing studies. In this paper, we address these limitations by leveraging Generative AI models, specifically transformers and Retrieval-Augmented Generation (RAG), to automate and enhance bibliometric analysis. We developed a technical workflow that integrates a vector database, Sentence Transformers, a Gaussian Mixture Model (GMM), Retrieval Agent, and Large Language Models (LLMs) to enable contextual search, topic ranking, and characterization of research using customized prompt templates. A pilot study analyzing 223 urban science-related articles published in Nature Communications over the past decade highlights the effectiveness of our approach in generating insightful summary statistics on the quality, scope, and characteristics of papers in high-impact journals. This study introduces a new paradigm for enhancing bibliometric analysis and knowledge retrieval in urban research, positioning an AI agent as a powerful tool for advancing research evaluation and understanding.
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