A new methodology for constructing a publication-level classification system of science

TL;DR

This paper introduces a scalable, citation-based methodology for constructing detailed publication-level research classification systems, overcoming journal-level limitations and capable of handling millions of publications.

Contribution

The paper presents a novel, transparent, and computationally efficient citation-based clustering approach for creating detailed research classification systems at the publication level.

Findings

Classified nearly ten million publications into research areas

Methodology is transparent and simple with modest computational requirements

Main limitation is reliance solely on direct citation relations

Abstract

Classifying journals or publications into research areas is an essential element of many bibliometric analyses. Classification usually takes place at the level of journals, where the Web of Science subject categories are the most popular classification system. However, journal-level classification systems have two important limitations: They offer only a limited amount of detail, and they have difficulties with multidisciplinary journals. To avoid these limitations, we introduce a new methodology for constructing classification systems at the level of individual publications. In the proposed methodology, publications are clustered into research areas based on citation relations. The methodology is able to deal with very large numbers of publications. We present an application in which a classification system is produced that includes almost ten million publications. Based on an extensive analysis of this classification system, we discuss the strengths and the limitations of the proposed methodology. Important strengths are the transparency and relative simplicity of the methodology and its fairly modest computing and memory requirements. The main limitation of the methodology is its exclusive reliance on direct citation relations between publications. The accuracy of the methodology can probably be increased by also taking into account other types of relations, for instance based on bibliographic coupling.