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The symbiotic relationship between information retrieval and informetrics

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  • Dietmar Wolfram

    (University of Wisconsin-Milwaukee)

Abstract

Informetrics and information retrieval (IR) represent fundamental areas of study within information science. Historically, researchers have not fully capitalized on the potential research synergies that exist between these two areas. Data sources used in traditional informetrics studies have their analogues in IR, with similar types of empirical regularities found in IR system content and use. Methods for data collection and analysis used in informetrics can help to inform IR system development and evaluation. Areas of application have included automatic indexing, index term weighting and understanding user query and session patterns through the quantitative analysis of user transaction logs. Similarly, developments in database technology have made the study of informetric phenomena less cumbersome, and recent innovations used in IR research, such as language models and ranking algorithms, provide new tools that may be applied to research problems of interest to informetricians. Building on the author’s previous work (Wolfram in Applied informetrics for information retrieval research, Libraries Unlimited, Westport, 2003), this paper reviews a sample of relevant literature published primarily since 2000 to highlight how each area of study may help to inform and benefit the other.

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  • Dietmar Wolfram, 2015. "The symbiotic relationship between information retrieval and informetrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2201-2214, March.
    • Handle: RePEc:spr:scient:v:102:y:2015:i:3:d:10.1007_s11192-014-1479-0
    DOI: 10.1007/s11192-014-1479-0
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    References listed on IDEAS

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    1. Johan Bollen & Herbert Van de Sompel & Aric Hagberg & Luis Bettencourt & Ryan Chute & Marko A Rodriguez & Lyudmila Balakireva, 2009. "Clickstream Data Yields High-Resolution Maps of Science," PLOS ONE, Public Library of Science, vol. 4(3), pages 1-11, March.
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    5. Yan, Erjia & Ding, Ying & Milojević, Staša & Sugimoto, Cassidy R., 2012. "Topics in dynamic research communities: An exploratory study for the field of information retrieval," Journal of Informetrics, Elsevier, vol. 6(1), pages 140-153.
    6. Bar-Ilan, Judit, 2008. "Informetrics at the beginning of the 21st century—A review," Journal of Informetrics, Elsevier, vol. 2(1), pages 1-52.
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    8. Ying Ding, 2011. "Applying weighted PageRank to author citation networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(2), pages 236-245, February.
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    10. Hui‐Min Chen & Michael D. Cooper, 2001. "Using clustering techniques to detect usage patterns in a Web‐based information system," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 52(11), pages 888-904.
    11. Dietmar Wolfram & Jin Zhang, 2008. "The influence of indexing practices and weighting algorithms on document spaces," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(1), pages 3-11, January.
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    13. Dietmar Wolfram & Peiling Wang & Jin Zhang, 2009. "Identifying Web search session patterns using cluster analysis: A comparison of three search environments," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(5), pages 896-910, May.
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    2. Jensen, Scott & Liu, Xiaozhong & Yu, Yingying & Milojevic, Staša, 2016. "Generation of topic evolution trees from heterogeneous bibliographic networks," Journal of Informetrics, Elsevier, vol. 10(2), pages 606-621.
    3. Philipp Mayr & Andrea Scharnhorst, 2015. "Scientometrics and information retrieval: weak-links revitalized," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2193-2199, March.
    4. 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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