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An exploration of link-based knowledge map in academic web space

Author

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  • Bo Yang

    (Nanjing Agricultural University)

  • Ying Sun

    (University at Buffalo)

Abstract

The World Wide Web has become an important source of academic information. The linking feature of the Web has been used to study the structure of academic web, as well as the presence of academic and research institutes on the Web. In this paper, we propose an integrated model for exploring the subject macrostructure of a specific academic topic on the Web and automatically depicting the knowledge map that is closer to what a domain expert would expect. The model integrates a hyperlink-induced topic search (HITS)-based link network extending strategy and a semantic based clustering algorithm with the aid of co-link analysis and social network analysis (SNA) to discover subject-based communities in the academic web space. We selected to use websites as analytical units rather than web pages because of the subject stability of a website. Compared with traditional techniques in Webometrics and SNA that have been used for such analyses, our model has the advantages of working on open web space (capability to explore unknown web resources and identify important ones) and of automatically building an extendable and hierarchical web knowledge map. The experiment in the area of Information Retrieval shows the effectiveness of the integrated model in analyzing and portraying of subject clustering phenomenon in academic web space.

Suggested Citation

  • Bo Yang & Ying Sun, 2013. "An exploration of link-based knowledge map in academic web space," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 239-253, July.
    • Handle: RePEc:spr:scient:v:96:y:2013:i:1:d:10.1007_s11192-012-0919-y
    DOI: 10.1007/s11192-012-0919-y
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    References listed on IDEAS

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    1. Junping Qiu & Yejun Li & Jiang Li & Quane Ren, 2008. "An exploratory study on substantive co-link analysis: A modification to total co-link analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 76(2), pages 327-341, August.
    2. Liwen Vaughan, 2006. "Visualizing linguistic and cultural differences using Web co‐link data," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(9), pages 1178-1193, July.
    3. Kim Holmberg, 2010. "Co-inlinking to a municipal Web space: a webometric and content analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(3), pages 851-862, June.
    4. Pamela Lang & Fábio C. Gouveia & Jacqueline Leta, 2010. "Site co-link analysis applied to small networks: a new methodological approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(1), pages 157-166, April.
    5. Gaston Heimeriks & Marianne Hörlesberger & Peter Van Den Besselaar, 2003. "Mapping communication and collaboration in heterogeneous research networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 58(2), pages 391-413, October.
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    Cited by:

    1. Ping Liu & Qiong Wu & Xiangming Mu & Kaipeng Yu & Yiting Guo, 2015. "Detecting the intellectual structure of library and information science based on formal concept analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(3), pages 737-762, September.
    2. David Gunnarsson Lorentzen, 2014. "Webometrics benefitting from web mining? An investigation of methods and applications of two research fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(2), pages 409-445, May.

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