IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v123y2020i2d10.1007_s11192-020-03400-0.html
   My bibliography  Save this article

Co-word analysis method based on meta-path of subject knowledge network

Author

Listed:
  • Xiang Zhu

    (Jilin University)

  • Yunqiu Zhang

    (Jilin University)

Abstract

We propose a method of co-word analysis based on the subject knowledge network meta-path to overcome limitations with the current co-word analysis method. First, we construct a subject knowledge network to find the word-to-word meta-path. Second, we use the HeteSim algorithm to calculate the semantic relevance between words based on each meta-path. Then, through matrix operations, standardization, and matrix fusion, we construct a word-to-word semantic relevance matrix (WSRM). We conduct an empirical evaluation to test the proposed method. The results indicate that the WSRM formed by this method is superior to the word-to-word similarity matrix used in traditional co-word analysis in terms of both macro-evaluation indicators (viz., network density, network centralization, network average degree, and cohesive subgroups) and micro-evaluation indicators (viz., core-periphery class, point centrality, and cluster analysis). The method overcomes limitations to the traditional co-word analysis method, and combines multiple semantic relations between words, to reflect the relationship between words more realistically.

Suggested Citation

  • Xiang Zhu & Yunqiu Zhang, 2020. "Co-word analysis method based on meta-path of subject knowledge network," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 753-766, May.
    • Handle: RePEc:spr:scient:v:123:y:2020:i:2:d:10.1007_s11192-020-03400-0
    DOI: 10.1007/s11192-020-03400-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-020-03400-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-020-03400-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Changqing Liu & Yingguang Li & Guanyan Zhou & Weiming Shen, 2018. "A sensor fusion and support vector machine based approach for recognition of complex machining conditions," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1739-1752, December.
    2. 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.
    3. 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.
    4. Zhong-Yi Wang & Gang Li & Chun-Ya Li & Ang Li, 2012. "Research on the semantic-based co-word analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(3), pages 855-875, March.
    5. Nazim Choudhury & Shahadat Uddin, 2016. "Time-aware link prediction to explore network effects on temporal knowledge evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(2), pages 745-776, August.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Xinyuan Zhang & Qing Xie & Chaemin Song & Min Song, 2022. "Mining the evolutionary process of knowledge through multiple relationships between keywords," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2023-2053, April.
    3. 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.
    4. 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.
    5. 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.
    6. Wang, Xiaoguang & He, Jing & Huang, Han & Wang, Hongyu, 2022. "MatrixSim: A new method for detecting the evolution paths of research topics," Journal of Informetrics, Elsevier, vol. 16(4).
    7. Yang, Siluo & Han, Ruizhen & Wolfram, Dietmar & Zhao, Yuehua, 2016. "Visualizing the intellectual structure of information science (2006–2015): Introducing author keyword coupling analysis," Journal of Informetrics, Elsevier, vol. 10(1), pages 132-150.
    8. Karol Król & Dariusz Zdonek, 2023. "Cultural Heritage Topics in Online Queries: A Comparison between English- and Polish-Speaking Internet Users," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
    9. Jinkai Yu & Wenjing Bi, 2019. "Evolution of Marine Environmental Governance Policy in China," Sustainability, MDPI, vol. 11(18), pages 1-14, September.
    10. Lu Huang & Xiang Chen & Yi Zhang & Changtian Wang & Xiaoli Cao & Jiarun Liu, 2022. "Identification of topic evolution: network analytics with piecewise linear representation and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5353-5383, September.
    11. Liu Yang & Keping Li & Hangfei Huang, 2018. "A new network model for extracting text keywords," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 339-361, July.
    12. Behrouzi, Saman & Shafaeipour Sarmoor, Zahra & Hajsadeghi, Khosrow & Kavousi, Kaveh, 2020. "Predicting scientific research trends based on link prediction in keyword networks," Journal of Informetrics, Elsevier, vol. 14(4).
    13. Lu Huang & Yijie Cai & Erdong Zhao & Shengting Zhang & Yue Shu & Jiao Fan, 2022. "Measuring the interdisciplinarity of Information and Library Science interactions using citation analysis and semantic analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6733-6761, November.
    14. Yongcong Luo & Jing Ma & Chi Li, 2020. "Entity name recognition of cross-border e-commerce commodity titles based on TWs-LSTM," Electronic Commerce Research, Springer, vol. 20(2), pages 405-426, June.
    15. Chiarello, Filippo & Fantoni, Gualtiero & Hogarth, Terence & Giordano, Vito & Baltina, Liga & Spada, Irene, 2021. "Towards ESCO 4.0 – Is the European classification of skills in line with Industry 4.0? A text mining approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    16. Choudhury, Nazim & Faisal, Fahim & Khushi, Matloob, 2020. "Mining Temporal Evolution of Knowledge Graphs and Genealogical Features for Literature-based Discovery Prediction," Journal of Informetrics, Elsevier, vol. 14(3).
    17. Xiuli Yang & Xin Miao & Jinli Wu & Ziwei Duan & Rui Yang & Yanhong Tang, 2020. "Towards Holistic Governance of China’s E-Waste Recycling: Evolution of Networked Policies," IJERPH, MDPI, vol. 17(20), pages 1-21, October.
    18. Xin Tong & Qiang Liu & Shiwei Pi & Yao Xiao, 2020. "Real-time machining data application and service based on IMT digital twin," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1113-1132, June.
    19. Xiaoyu Liu & Xuefeng Wang & Donghua Zhu, 2022. "Reviewer recommendation method for scientific research proposals: a case for NSFC," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3343-3366, June.
    20. 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).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:123:y:2020:i:2:d:10.1007_s11192-020-03400-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.