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A social voting approach for scientific domain vocabularies construction

Author

Listed:
  • Hongbing Jiang

    (University of Science and Technology of China
    Zhengzhou University)

  • Chen Yang

    (Shenzhen University)

  • Jian Ma

    (City University of Hong Kong)

  • Thushari Silva

    (City University of Hong Kong)

  • Huaping Chen

    (University of Science and Technology of China)

Abstract

Scientific domain vocabularies play an important role in academic communication and lean research management. Confronted with the dramatic increasing of new keywords, the continuous development of a domain vocabulary is important for the domain to keep its long survival in the scientific context. Current methods based either on statistical or linguistic approaches can automatically generate vocabularies that consist of popular keywords, but these approaches fail to capture high-quality standardized terms due to the lack of human intervention. Manual methods take use of human knowledge, but they are both time-consuming and expensive. In order to overcome these deficiencies, this research proposes a novel social voting approach to construct scientific domain vocabularies. It integrates automatic system and human knowledge based on the theory of linguistic arbitrariness and selects widely accepted standardized set of keywords based on social voting. A social voting system has been implemented to aid scientific domain vocabulary construction in the National Natural Science Foundation of China. Two experiments are conducted to demonstrate the effectiveness and validity of the built system. The results show that the constructed domain vocabulary using this system covers a wide range of areas under a discipline and it facilitates the standardization of scientific terminology.

Suggested Citation

  • Hongbing Jiang & Chen Yang & Jian Ma & Thushari Silva & Huaping Chen, 2016. "A social voting approach for scientific domain vocabularies construction," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(2), pages 803-820, August.
    • Handle: RePEc:spr:scient:v:108:y:2016:i:2:d:10.1007_s11192-016-1990-6
    DOI: 10.1007/s11192-016-1990-6
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    References listed on IDEAS

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    1. Steve Jones & Gordon W. Paynter, 2002. "Automatic extraction of document keyphrases for use in digital libraries: Evaluation and applications," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 53(8), pages 653-677.
    2. Wei Du & Raymond Yiu Keung Lau & Jian Ma & Wei Xu, 2015. "A multi-faceted method for science classification schemes (SCSs) mapping in networking scientific resources," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 2035-2056, December.
    3. Byungun Yoon & Sungjoo Lee & Gwanghee Lee, 2010. "Development and application of a keyword-based knowledge map for effective R&D planning," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(3), pages 803-820, December.
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    Cited by:

    1. Wei Du & Xusen Cheng & Chen Yang & Jianshan Sun & Jian Ma, 2017. "Establishing interoperability among knowledge organization systems for research management: a social network approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1489-1506, September.

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