Hierarchical classification of research fields in the MAG science network using deep learning

Abstract

This paper presents a hierarchical classification system that automatically categorizes a scholarly publication, using its abstract, into a three-tier hierarchical label set (discipline, field, subfield) in a multiclass setting. This system enables a holistic categorization of research activities in the mentioned hierarchy in terms of knowledge production through articles and impact through citations, permitting those activities to fall into multiple categories. The classification system distinguishes 44 disciplines, 718 fields, and 1,485 subfields among 160 million abstract snippets in Microsoft Academic Graph (version 2018-05-17). We used batch training in a modularized and distributed fashion to address and allow for interdisciplinary and interfield classifications in single-label and multilabel settings. In total, we have conducted 3,140 experiments in all considered models (Convolutional Neural Networks, Recurrent Neural Networks, and Transformers). The classification accuracy is >90% in 77.13% and 78.19% of the single-label and multilabel classifications, respectively. We examine the advantages of our classification by its ability to better align research texts and output with disciplines, to adequately classify them in an automated way, and to capture the degree of interdisciplinarity. The proposed system (a set of pre-trained models) can serve as a backbone for an interactive system for indexing scientific publications in the future.