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Improving the co-word analysis method based on semantic distance

Author

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  • Jia Feng

    (Jilin University)

  • Yun Qiu Zhang

    (Jilin University)

  • Hao Zhang

    (Jilin University)

Abstract

We propose an improvement over the co-word analysis method based on semantic distance. This combines semantic distance measurements with concept matrices generated from ontologically based concept mapping. Our study suggests that the co-word analysis method based on semantic distance produces a preferable research situation in terms of matrix dimensions and clustering results. Despite this method’s displayed advantages, it has two limitations: first, it is highly dependent on domain ontology; second, its efficiency and accuracy during the concept mapping progress merit further study. Our method optimizes co-word matrix conditions in two aspects. First, by applying concept mapping within the labels of the co-word matrix, it combines words at the concept level to reduce matrix dimensions and create a concept matrix that contains more content. Second, it integrates the logical relationships and concept connotations among studied concepts into a co-word matrix and calculates the semantic distance between concepts based on domain ontology to create the semantic matrix.

Suggested Citation

  • Jia Feng & Yun Qiu Zhang & Hao Zhang, 2017. "Improving the co-word analysis method based on semantic distance," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1521-1531, June.
    • Handle: RePEc:spr:scient:v:111:y:2017:i:3:d:10.1007_s11192-017-2286-1
    DOI: 10.1007/s11192-017-2286-1
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    References listed on IDEAS

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    1. Loet Leydesdorff & Liwen Vaughan, 2006. "Co‐occurrence matrices and their applications in information science: Extending ACA to the Web environment," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(12), pages 1616-1628, October.
    2. Sasson, Elan & Ravid, Gilad & Pliskin, Nava, 2015. "Improving similarity measures of relatedness proximity: Toward augmented concept maps," Journal of Informetrics, Elsevier, vol. 9(3), pages 618-628.
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    Cited by:

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    3. Faraji, Omid & Ezadpour, Mostafa & Rahrovi Dastjerdi, Alireza & Dolatzarei, Ehsan, 2022. "Conceptual structure of balanced scorecard research: A co-word analysis," Evaluation and Program Planning, Elsevier, vol. 94(C).
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    5. Xiaojun Zhang & Weiqiao Wang & Yunan Bai & Yong Ye, 2022. "How Has China Structured Its Ecological Governance Policy System?—A Case from Fujian Province," IJERPH, MDPI, vol. 19(14), pages 1-22, July.
    6. Na Zhou & Qiaosheng Wu & Xiangping Hu, 2020. "Research on the Policy Evolution of China’s New Energy Vehicles Industry," Sustainability, MDPI, vol. 12(9), pages 1-17, May.
    7. Qikai Cheng & Jiamin Wang & Wei Lu & Yong Huang & Yi Bu, 2020. "Keyword-citation-keyword network: a new perspective of discipline knowledge structure analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 1923-1943, September.
    8. Kai Hu & Huayi Wu & Kunlun Qi & Jingmin Yu & Siluo Yang & Tianxing Yu & Jie Zheng & Bo Liu, 2018. "A domain keyword analysis approach extending Term Frequency-Keyword Active Index with Google Word2Vec model," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 1031-1068, March.
    9. Manuel Castriotta & Michela Loi & Elona Marku & Luca Naitana, 2019. "What’s in a name? Exploring the conceptual structure of emerging organizations," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(2), pages 407-437, February.
    10. Salustiano Martínez-Fierro & María Paula Lechuga Sancho, 2021. "Descriptive Elements and Conceptual Structure of Glass Ceiling Research," IJERPH, MDPI, vol. 18(15), pages 1-20, July.

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