🤖 AI Summary
Existing scientific classification systems struggle to capture fine-grained conceptual structures in research, while author-provided keywords—though specific—are often fragmented, redundant, and inconsistent in terminology. This work proposes a novel approach that integrates scientific text embeddings, large language models (LLMs), and graph-based community detection to construct an interpretable Concept layer beneath the Topic hierarchy of OpenAlex. By aggregating semantically related keywords into unified, reusable conceptual units and situating them within disciplinary hierarchies, this method enables a systematic transformation from heterogeneous terms to structured knowledge entities. It represents the first large-scale academic taxonomy to incorporate an LLM- and embedding-driven intermediate conceptual layer, substantially enhancing the precision and scalability of scholarly document organization, science mapping, research trend monitoring, and ontology construction.
📝 Abstract
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.