Framework assigns detailed scientific concepts to research articles accurately
MATCH: Multi-stage Article Tagging to Concept Hierarchies
Digital Libraries
Summary
Scientific articles are hard to label with very detailed topics because there are so many different specialized subjects. The authors created MATCH, a system that tags articles using their titles, abstracts, and author keywords by combining different methods to pick the best topic labels. They tested MATCH with experts and found it assigns correct tags about 80% of the time. The system can work without relying on expensive external services and has been used on over two million scientific articles. This helps better track and analyze scientific research developments.
What this means in practice
- •For academic publishers: Automatically assign precise scientific concepts to articles to improve search and discovery across large collections.
- •For digital library managers: Maintain up-to-date, fine-grained article tagging for large-scale publication databases facilitating trend monitoring.
Authors
Daniele Raimondi, Feichi Lu, Oliver Grun, Mariia Eremina, Andrea Perlato
Abstract
The increasing specialization of scientific research calls for fine-grained representations of publication content that can be applied at scale. SCALE extends the OpenAlex taxonomy with approximately 114,000 Scientific Concepts derived from author keywords, but assigning these Concepts to individual publications remains challenging. We introduce MATCH (Multi-stage Article Tagging to Concept Hierarchies), a framework that assigns SCALE Concepts using article titles, abstracts, and author keywords. MATCH combines multi-view embedding retrieval, deterministic taxonomy-aware filtering, and language-model reranking to select relevant Concepts while preserving their hierarchical paths. Its modular design supports both API-based and self-hosted inference. In a blind evaluation involving 22 domain experts across 11 fields and 110 articles, the self-hosted and API-based configurations achieved Precision of 81.5% and 83.3%, respectively, and Precision@5 of approximately 89% for both, with no statistically significant difference detected. These findings support the practical viability of the evaluated self-hosted configuration for large-scale Concept assignment without per-token API charges. Deployed on over two million MDPI publications, MATCH assigns a median of five Concepts per article, covers approximately 94% of the SCALE Concepts, and supports daily indexing of newly available publications. By operationalizing the SCALE taxonomy at publication level, MATCH provides a foundation for fine-grained scientometric analysis and monitoring of scientific developments.