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The study of subject-classification based on journal coupling and expert subject-classification system

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  • Jing Zhang

    (Chinese Academy of Sciences)

  • Xiaomin Liu

    (Chinese Academy of Sciences)

  • Lili Wu

    (Chinese Academy of Sciences)

Abstract

As the framework of scientific research, subject-classification plays an important role in the development of science. In order to combine the development of science with the current expert subject-classification system and further give a more appropriate description of scientific output analysis from subject level, We study the relationship between the natural science related sub-categories of Chinese library classification using objective computerized scientometrics, and give some modification to the first two level subjects of the existing Chinese library classification system. Taking Chinese Science Citation Database as our data source, this article studies the similarity of subjects based on journal coupling strength. Then we try to set up an improved subject-classification system whose top categories are relied on Chinese library classification system and sub-categories are the ensemble clustering result based on journal coupling measure. Further, in order to help identifying and interpreting the rationality of this improved classification system, we make use of some text mining methods, such as key words recognition and topic detection, to explain the cause of similarity between some subjects from the perspective of semantic. Our study shows that the improved subject-classification system constructed in this article not only conforms to previous experience and cognitive but also combines subject development knowledge.

Suggested Citation

  • Jing Zhang & Xiaomin Liu & Lili Wu, 2016. "The study of subject-classification based on journal coupling and expert subject-classification system," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1149-1170, June.
    • Handle: RePEc:spr:scient:v:107:y:2016:i:3:d:10.1007_s11192-016-1890-9
    DOI: 10.1007/s11192-016-1890-9
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    References listed on IDEAS

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    1. Y.F. Chang & C.-M. Chen, 2011. "Classification and visualization of the social science network by the minimum span clustering method," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(12), pages 2404-2413, December.
    2. Ludo Waltman & Nees Jan Eck, 2012. "A new methodology for constructing a publication-level classification system of science," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(12), pages 2378-2392, December.
    3. Chaoqun Ni & Cassidy R. Sugimoto & Jiepu Jiang, 2013. "Venue-author-coupling: A measure for identifying disciplines through author communities," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(2), pages 265-279, February.
    4. Y.F. Chang & C.‐M. Chen, 2011. "Classification and visualization of the social science network by the minimum span clustering method," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(12), pages 2404-2413, December.
    5. M. M. Kessler, 1963. "Bibliographic coupling between scientific papers," American Documentation, Wiley Blackwell, vol. 14(1), pages 10-25, January.
    6. Loet Leydesdorff, 2006. "Can scientific journals be classified in terms of aggregated journal‐journal citation relations using the Journal Citation Reports?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(5), pages 601-613, March.
    7. Chaomei Chen & Fidelia Ibekwe-SanJuan & Jianhua Hou, 2010. "The structure and dynamics of cocitation clusters: A multiple-perspective cocitation analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(7), pages 1386-1409, July.
    8. Gómez-Núñez, Antonio J. & Batagelj, Vladimir & Vargas-Quesada, Benjamín & Moya-Anegón, Félix & Chinchilla-Rodríguez, Zaida, 2014. "Optimizing SCImago Journal & Country Rank classification by community detection," Journal of Informetrics, Elsevier, vol. 8(2), pages 369-383.
    9. Francis Narin & Mark Carpenter & Nancy C. Berlt, 1972. "Interrelationships of scientific journals," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 23(5), pages 323-331, September.
    10. Ludo Waltman & Nees Jan van Eck, 2012. "A new methodology for constructing a publication‐level classification system of science," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(12), pages 2378-2392, December.
    11. Kevin W. Boyack & Richard Klavans & Katy Börner, 2005. "Mapping the backbone of science," Scientometrics, Springer;Akadémiai Kiadó, vol. 64(3), pages 351-374, August.
    12. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    13. Leydesdorff, Loet & Rafols, Ismael, 2012. "Interactive overlays: A new method for generating global journal maps from Web-of-Science data," Journal of Informetrics, Elsevier, vol. 6(2), pages 318-332.
    14. Wolfgang Glänzel & András Schubert, 2003. "A new classification scheme of science fields and subfields designed for scientometric evaluation purposes," Scientometrics, Springer;Akadémiai Kiadó, vol. 56(3), pages 357-367, March.
    15. Chaoqun Ni & Cassidy R. Sugimoto & Jiepu Jiang, 2013. "Venue‐author‐coupling: A measure for identifying disciplines through author communities," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(2), pages 265-279, February.
    16. Loet Leydesdorff, 2002. "Dynamic and evolutionary updates of classificatory schemes in scientific journal structures," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 53(12), pages 987-994, October.
    17. Ahlgren, Per & Colliander, Cristian, 2009. "Document–document similarity approaches and science mapping: Experimental comparison of five approaches," Journal of Informetrics, Elsevier, vol. 3(1), pages 49-63.
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