SciNoBo : A Hierarchical Multi-Label Classifier of Scientific Publications
This addresses the need for funders, publishers, and scholars to organize literature more effectively, though it appears incremental by building on existing network-based methods.
The authors tackled the problem of classifying scientific publications into Field-of-Science taxonomies by proposing SciNoBo, a system that uses a multilayer network of citations and references to support multi-label assignments, and it achieved high-quality classifications compared to a state-of-the-art neural-network baseline.
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.