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Relevance: The whole history

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  • Stefano Mizzaro

Abstract

Relevance is a fundamental, though not completely understood, concept for documentation, information science, and information retrieval. This article presents the history of relevance through an exhaustive review of the literature. Such history being very complex (about 160 papers are discussed), it is not simple to describe it in a comprehensible way. Thus, first of all a framework for establishing a common ground is defined, and then the history itself is illustrated via the presentation in chronological order of the papers on relevance. The history is divided into three periods (“Before 1958,” “1959–1976,” and “1977–present”) and, inside each period, the papers on relevance are analyzed under seven different aspects (methodological foundations, different kinds of relevance, beyond‐topical criteria adopted by users, modes for expression of the relevance judgment, dynamic nature of relevance, types of document representation, and agreement among different judges). © 1997 John Wiley & Sons, Inc.

Suggested Citation

  • Stefano Mizzaro, 1997. "Relevance: The whole history," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 48(9), pages 810-832, September.
  • Handle: RePEc:bla:jamest:v:48:y:1997:i:9:p:810-832
    DOI: 10.1002/(SICI)1097-4571(199709)48:93.0.CO;2-U
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    Cited by:

    1. Timo Breuer & Philipp Schaer & Dirk Tunger, 2022. "Relevance assessments, bibliometrics, and altmetrics: a quantitative study on PubMed and arXiv," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2455-2478, May.
    2. Wayne de Fremery & Michael K. Buckland, 2022. "Context, relevance, and labor," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(9), pages 1268-1278, September.
    3. Ian Ruthven, 2021. "Resonance and the experience of relevance," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(5), pages 554-569, May.
    4. Aurora González-Teruel & Gregorio González-Alcaide & Maite Barrios & María-Francisca Abad-García, 2015. "Mapping recent information behavior research: an analysis of co-authorship and co-citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 687-705, May.
    5. Lauri Rapeli & Sakari Nieminen & Marko Mäkelä, 2021. "Not just the facts: an index for measuring the information density of political communication," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(5), pages 1683-1702, October.
    6. Yao, Jianyu & Xiao, Peng & Zhang, Yunhuai & Zhan, Min & Cheng, Jiangwei, 2011. "A mathematical model of algal blooms based on the characteristics of complex networks theory," Ecological Modelling, Elsevier, vol. 222(20), pages 3727-3733.
    7. Tong, Tong & Wang, Wanru & Ye, Fred Y., 2024. "A complement to the novel disruption indicator based on knowledge entities," Journal of Informetrics, Elsevier, vol. 18(2).
    8. Jean J. Wang & Sarah X. Shao & Fred Y. Ye, 2021. "Identifying 'seed' papers in sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 6001-6011, July.
    9. Alexandra Dumitrescu & Simone Santini, 2021. "Full coverage of a reader's interests in context‐based information filtering," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(8), pages 1011-1027, August.

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