AUTHOR=Kurakawa Kei , Sun Yuan , Ando Satoko 

TITLE=Application of a Novel Subject Classification Scheme for a Bibliographic Database Using a Data-Driven Correspondence

JOURNAL=Frontiers in Big Data

VOLUME=2

YEAR=2020

URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2019.00048

DOI=10.3389/fdata.2019.00048

ISSN=2624-909X

ABSTRACT=<p>A novel subject classification scheme should often be applied to a preclassified bibliographic database for the research evaluation task. Generally, adopting a new subject classification scheme is labor intensive and time consuming, and an effective and efficient approach is necessary. Hence, we propose an approach to apply a new subject classification scheme for a subject-classified database using a data-driven correspondence between the new and present ones. In this paper, we define a subject classification model of the bibliographic database comprising a topological space. Then, we show our approach based on this model, wherein forming a compact topological space is required for a novel subject classification scheme. To form the space, a correspondence between two subject classification schemes using a research project database is utilized as data. As a case study, we applied our approach to a practical example. It is a tool used as world proprietary benchmarking for research evaluation based on a citation database. We tried to add a novel subject classification of a research project database.</p>