MATCH: Multi-stage Article Tagging to Concept Hierarchies

Overview & Synopsis

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.