SciNoBo : A Hierarchical Multi-Label Classifier of Scientific Publications

TL;DR

SciNoBo is a hierarchical multi-label classification system for scientific publications that uses a multilayer network of citations and venues to improve field-of-science categorization, supporting multidisciplinary assignments.

Contribution

It introduces a novel multilayer network approach that unifies publications and venues for enhanced multi-label classification of scientific literature.

Findings

High-quality classification performance demonstrated

Outperforms neural-network baseline in accuracy

Supports multidisciplinary publication categorization

Abstract

Classifying scientific publications according to Field-of-Science (FoS) taxonomies is of crucial importance, allowing funders, publishers, scholars, companies and other stakeholders to organize scientific literature more effectively. Most existing works address classification either at venue level or solely based on the textual content of a research publication. We present SciNoBo, a novel classification system of publications to predefined FoS taxonomies, leveraging the structural properties of a publication and its citations and references organised in a multilayer network. In contrast to other works, our system supports assignments of publications to multiple fields by considering their multidisciplinarity potential. By unifying publications and venues under a common multilayer network structure made up of citing and publishing relationships, classifications at the venue-level can be augmented with publication-level classifications. We evaluate SciNoBo on a publications' dataset extracted from Microsoft Academic Graph and we perform a comparative analysis against a state-of-the-art neural-network baseline. The results reveal that our proposed system is capable of producing high-quality classifications of publications.