Periodical embeddings uncover hidden interdisciplinary patterns in the subject classification scheme of science

TL;DR

This paper introduces a new citation-based embedding method that improves journal classification accuracy and reveals hidden interdisciplinary patterns, surpassing traditional taxonomies in reflecting the structure of scientific knowledge.

Contribution

The study presents a novel periodical embedding framework using citation data, demonstrating its superiority over existing classification schemes in capturing scientific interdisciplinarity.

Findings

Citation-based embeddings outperform traditional taxonomies in classification tasks.

Disciplinary boundaries are reorganized, splitting broad categories and merging fragmented ones.

Interdisciplinary clusters are identified as coherent, dispersed groups in citation space.

Abstract

Subject classification schemes are foundational to the organization, evaluation, and navigation of scientific knowledge. While expert-curated systems like Scopus provide widely used taxonomies, they often suffer from coarse granularity, subjectivity, and limited adaptability to emerging interdisciplinary fields. Data-driven alternatives based on citation networks show promise but lack rigorous, external validation against the semantic content of scientific literature. Here, we propose a novel quantitative framework that leverages classification tasks to evaluate the effectiveness of journal classification schemes. Using over 23 million paper abstracts, we demonstrate that labels derived from k-means clustering on Periodical2Vec (P2V)--a periodical embedding learned from paper-level citations--yield significantly higher classification performance than both Scopus and other data-driven baselines (e.g., citation, co-citation, and Node2Vec variants). By comparing journal partitions across classification schemes, two structural patterns emerge on the map of science: (1) the reorganization of disciplinary boundaries--splitting overly broad categories (e.g., "Medicine" into "Oncology", "Cardiology", and other specialties) while merging artificially fragmented ones (e.g., "Chemistry" and "Chemical Engineering"); and (2) the identification of coherent interdisciplinary clusters--such as "Biomedical Engineering", "Medical Ethics", and "Information Management"--that are dispersed across multiple categories but unified in citation space. These findings underscore that citation-derived periodical embeddings not only outperform traditional taxonomies in predictive validity but also offer a dynamic, fine-grained map of science that better reflects both the specialization and interdisciplinarity inherent in contemporary research.