Automatic Subject Classification of Korean Journals Based on KSCD
Year: 2015
Type: conference-abstract
Abstract: Journal subject classification is important in terms of being used for scholarly information service and foundation of disciplinary research analysis. Subject classification by subject matter experts or journal information takes a significant amount of time or does not provide accurate information about subject respectively. In order to overcome these current problems, this research suggested automatic subject classification method by using SCI journal information cited by domestic science and engineering journals, and it also investigated the classification results. We found that using the entire cited academic journals has a better accuracy rate than using the most cited three or five academic journals, and this research showed that the more academic journals included in the analysis the more accurate the rate. Especially, this research utilised the subject category of Web of Science as the standard of subject classification and provided foundations for comparing subject category structure in academic research results in KSCI and SCI. Keywords: Automatic Subject Classification, Korean Journals, KSCI
Authors: MuYeong Kang, Jae-Do Shin, Byungkyu Kim
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W1971346945