Mining Related Articles for Automatic Journal Cataloging
Yuqing Mao1,2; Zhiyong Lu2; Yuqing Mao (E-mail: mao.yuqing@msn.com).
2016-06-17
Source PublicationJournal of Data and Information Science
Volume1Issue:2Pages:45-59
Abstract

Purpose: This paper is an investigation of the effectiveness of the method of clustering biomedical journals through mining the content similarity of journal articles.
Design/methodology/approach: 3,265 journals in PubMed are analyzed based on article content similarity and Web usage, respectively. Comparisons of the two analysis approaches and a citation-based approach are given.
Findings: Our results suggest that article content similarity is useful for clustering biomedical journals, and the content-similarity-based journal clustering method is more robust and less subject to human factors compared with the usage-based approach and the citation-based approach.
Research limitations: Our paper currently focuses on clustering journals in the biomedical domain because there are a large volume of freely available resources such as PubMed and MeSH in this field. Further investigation is needed to improve this approach to fit journals in other domains.
Practical implications: Our results show that it is feasible to catalog biomedical journals by mining the article content similarity. This work is also significant in serving practical needs in research portfolio analysis.
Originality/value: To the best of our knowledge, we are among the first to report on clustering journals in the biomedical field through mining the article content similarity. This method can be integrated with existing approaches to create a new paradigm for future studies of journal clustering.

;

Purpose: This paper is an investigation of the effectiveness of the method of clustering biomedical journals through mining the content similarity of journal articles.
Design/methodology/approach: 3,265 journals in PubMed are analyzed based on article content similarity and Web usage, respectively. Comparisons of the two analysis approaches and a citation-based approach are given.
Findings: Our results suggest that article content similarity is useful for clustering biomedical journals, and the content-similarity-based journal clustering method is more robust and less subject to human factors compared with the usage-based approach and the citation-based approach.
Research limitations: Our paper currently focuses on clustering journals in the biomedical domain because there are a large volume of freely available resources such as PubMed and MeSH in this field. Further investigation is needed to improve this approach to fit journals in other domains.
Practical implications: Our results show that it is feasible to catalog biomedical journals by mining the article content similarity. This work is also significant in serving practical needs in research portfolio analysis.
Originality/value: To the best of our knowledge, we are among the first to report on clustering journals in the biomedical field through mining the article content similarity. This method can be integrated with existing approaches to create a new paradigm for future studies of journal clustering.

SubtypeResearch Paper
KeywordPubmed Journals Cluster Catalog Text Mining Research Evaluation
Subject Area新闻学与传播学 ; 图书馆、情报与文献学
DOI10.20309/jdis.201613
URL查看原文
Indexed By其他
Language英语
Funding ProjectNIH Intramural Research Program, National Library of Medicine.
Citation statistics
Document Type期刊论文
Identifierhttp://ir.las.ac.cn/handle/12502/8597
CollectionJournal of Data and Information Science_Journal of Data and Information Science-2016
Corresponding AuthorYuqing Mao (E-mail: mao.yuqing@msn.com).
Affiliation1.School of Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, China
2.National Center for Biotechnology Information, National Library of Medicine, MD 20894, USA
First Author Affilication中国科学院文献情报中心
Recommended Citation
GB/T 7714
Yuqing Mao,Zhiyong Lu,Yuqing Mao . Mining Related Articles for Automatic Journal Cataloging[J]. Journal of Data and Information Science,2016,1(2):45-59.
APA Yuqing Mao,Zhiyong Lu,&Yuqing Mao .(2016).Mining Related Articles for Automatic Journal Cataloging.Journal of Data and Information Science,1(2),45-59.
MLA Yuqing Mao,et al."Mining Related Articles for Automatic Journal Cataloging".Journal of Data and Information Science 1.2(2016):45-59.
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