用大模型和嵌入技术整合科研关键词，构建细粒度知识分类体系。
SCALE: Scientific Concept Aggregation via LLMs and Embeddings for Fine-Grained Taxonomy Extension
- 通过大模型与图聚类融合，将零散关键词聚合成可解释的概念单元。
- 在OpenAlex分类体系下新增概念层，实现从宏观主题到具体文献的中间抽象。
- 适用于科学计量、研究追踪及未来知识图谱构建，提升分类精度。
科学研究日益专业化，现有分类系统虽能有效表示宽泛学科与研究主题，却难以捕捉当代科学的细粒度概念结构。作者关键词具有更高粒度，但存在碎片化、冗余和术语不一致问题，限制其作为稳定知识组织单元的使用。我们提出SCALE（Scientific Concept Aggregation via LLMs and Embeddings），一个将开放科学分类体系OpenAlex扩展至更细粒度概念层级的框架。该框架不将关键词视为孤立描述符，而是通过科学文本嵌入、大语言模型与基于图的社区检测技术，将语义相关的术语整合为连贯且可解释的概念单元，并融入现有学科层次结构中。由此构建的分类体系使学术文献可在广义主题与单篇文档之间，通过中间概念层级进行阅读。这一视角提供了对科学知识结构、专业化程度及跨学科关联的更细致刻画。通过将异构的作者术语转化为可复用的层级化单位，SCALE为细粒度学术分类、科学计量分析、研究监测及未来本体发展奠定了基础。
原文摘要 · Abstract (English)
The increasing specialization of scientific research challenges existing classification systems, which provide effective representations of broad disciplines and research topics but often fail to capture the fine-grained conceptual structure of contemporary science. Author keywords offer greater specificity, but their fragmentation, redundancy, and terminological variability limit their use as stable units of knowledge organization. We introduce SCALE (Scientific Concept Aggregation via LLMs and Embeddings), a framework that extends the OpenAlex taxonomy with a new level of scientific Concepts below Topics. Rather than treating keywords as isolated descriptors, SCALE organizes semantically related terms into coherent and interpretable conceptual units and integrates them within the existing disciplinary hierarchy. The framework combines scientific text embeddings, large language models, and graph-based community detection to construct this additional layer at scale. The resulting taxonomy enables scientific literature to be read through an intermediate conceptual level between broad research topics and individual documents. This perspective provides a more detailed representation of how scientific knowledge is structured, specialized, and connected across disciplines. By transforming heterogeneous author terminology into reusable hierarchical units, SCALE offers a foundation for fine-grained scholarly classification, scientometric analysis, research monitoring, and future ontology development.
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