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Cluster-based polyrepresentation as science modelling approach for information retrieval

Author

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  • Muhammad Kamran Abbasi

    (University of Bedfordshire)

  • Ingo Frommholz

    (University of Bedfordshire)

Abstract

The increasing number of publications make searching and accessing the produced literature a challenging task. A recent development in bibliographic databases is to use advanced information retrieval techniques in combination with bibliographic means like citations. In this work we will present an approach that combines a cognitive information retrieval framework based on the principle of polyrepresentation with document clustering to enable the user to explore a collection more interactively than by just examining a ranked result list. Our approach uses information need representations as well as different document representations including citations. To evaluate our ideas we employ a simulated user strategy utilising a cluster ranking approach. We report on the possible effectiveness of our approach and on several strategies how users can achieve a higher search effectiveness through cluster browsing. Our results confirm that our proposed polyrepresentative cluster browsing strategy can in principle significantly improve the search effectiveness. However, further evaluations including a more refined user simulation are needed.

Suggested Citation

  • Muhammad Kamran Abbasi & Ingo Frommholz, 2015. "Cluster-based polyrepresentation as science modelling approach for information retrieval," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2301-2322, March.
    • Handle: RePEc:spr:scient:v:102:y:2015:i:3:d:10.1007_s11192-014-1478-1
    DOI: 10.1007/s11192-014-1478-1
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    References listed on IDEAS

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    1. Birger Larsen, 2002. "Exploiting citation overlaps for Information Retrieval: Generating a boomerang effect from the network of scientific papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 54(2), pages 155-178, June.
    2. 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.
    3. Peter Mutschke & Philipp Mayr & Philipp Schaer & York Sure, 2011. "Science models as value-added services for scholarly information systems," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(1), pages 349-364, October.
    4. Patrick Glenisson & Wolfgang Glänzel & Olle Persson, 2005. "Combining full-text analysis and bibliometric indicators. A pilot study," Scientometrics, Springer;Akadémiai Kiadó, vol. 63(1), pages 163-180, March.
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    Cited by:

    1. Philipp Mayr & Andrea Scharnhorst, 2015. "Scientometrics and information retrieval: weak-links revitalized," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2193-2199, March.
    2. Juan Pablo Bascur & Suzan Verberne & Nees Jan Eck & Ludo Waltman, 2023. "Academic information retrieval using citation clusters: in-depth evaluation based on systematic reviews," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 2895-2921, May.

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