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Cluster-based polyrepresentation as science modelling approach for information retrieval

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An Erratum to this article was published on 03 April 2015

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.

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Notes

  1. http://itlab.dbit.dk/isearch/.

  2. http://terrier.org/.

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Acknowledgments

The authors would like to thank anonymous reviewers for their insightful comments.

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Authors and Affiliations

  1. Institute for Research in Applicable Computing, University of Bedfordshire, Park Square, Luton, LU1 3JU, UK

    Muhammad Kamran Abbasi & Ingo Frommholz

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  1. Muhammad Kamran Abbasi
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  2. Ingo Frommholz
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Correspondence to Muhammad Kamran Abbasi.

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Abbasi, M.K., Frommholz, I. Cluster-based polyrepresentation as science modelling approach for information retrieval. Scientometrics 102, 2301–2322 (2015). https://doi.org/10.1007/s11192-014-1478-1

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