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

Abstract

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.

More on the relationship between interdisciplinary accounting research and citation impact

“…At this point, the cited references were unclassified in terms of the disciplines in which the cited articles were embedded. The authors classified each cited reference by linking the journal of the cited reference to the WoS subject categories, considered by scholars as one of the most popular and comprehensive subject classification lists (Kurakawa et al, 2020). The WoS database includes 227 subject categories across four broad indices; Science Citation Index (SCI), Social Science Citation Index (SSCI), and Arts & Humanities Citation Index (AHCI).…”

Feature Selection by Thematic and Temporal Distinction in Research Grant Applications

“…Other studies analyzing the KAKEN database and Web of Science 3 , which is bibliography information, include studies by Igami et al [11] and Kurakawa et al [12] The study conducted by Shimada et al [13] analyzed mission-driven research grants and curiositydriven grants using the KAKEN and CREST databases as research funding information and the J-Global database as bibliographic information. The study conducted by Kawashima et al [14] used the KAKEN database and Scopus 4 as bibliographic information to verify the accuracy of author IDs in the analysis of researcher information.…”