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Co-cited author retrieval and relevance theory: examples from the humanities

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  • Howard D. White

    (Drexel University)

Abstract

Given a user-selected seed author, a unique experimental system called AuthorWeb can return the 24 authors most frequently co-cited with the seed in a 10-year segment of the Arts and Humanities Citation Index. The Web-based system can then instantly display the seed and the others as a Pathfinder network, a Kohonen self-organizing map, or a pennant diagram. Each display gives a somewhat different overview of the literature cited with the seed in a specialty (e.g., Thomas Mann studies). Each is also a live interface for retrieving (1) the documents that co-cite the seed with another user-selected author, and (2) the works by the seed and the other author that are co-cited. This article describes the Pathfinder and Kohonen maps, but focuses much more on AuthorWeb pennant diagrams, exhibited here for the first time. Pennants are interesting because they unite ego-centered co-citation data from bibliometrics, the TF*IDF formula from information retrieval, and Sperber and Wilson’s relevance theory (RT) from linguistic pragmatics. RT provides a cognitive interpretation of TF*IDF weighting. By making people’s inferential processes a primary concern, RT also yields insights into both topical and non-topical relevance, central matters in information science. Pennants for several authors in the humanities demonstrate these insights.

Suggested Citation

  • Howard D. White, 2015. "Co-cited author retrieval and relevance theory: examples from the humanities," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2275-2299, March.
    • Handle: RePEc:spr:scient:v:102:y:2015:i:3:d:10.1007_s11192-014-1483-4
    DOI: 10.1007/s11192-014-1483-4
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    References listed on IDEAS

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    Cited by:

    1. Moshe Blidstein & Maayan Zhitomirsky-Geffet, 2022. "Towards a new generic framework for citation network generation and analysis in the humanities," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 4275-4297, July.
    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. Roozbeh Haghnazar Koochaksaraei & Frederico Gadelha Guimarães & Babak Hamidzadeh & Sarfaraz Hashemkhani Zolfani, 2021. "Visualization Method for Decision-Making: A Case Study in Bibliometric Analysis," Mathematics, MDPI, vol. 9(9), pages 1-27, April.
    4. Müge Akbulut & Yaşar Tonta & Howard D. White, 2020. "Related records retrieval and pennant retrieval: an exploratory case study," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 957-987, February.
    5. Philipp Mayr & Andrea Scharnhorst, 2015. "Scientometrics and information retrieval: weak-links revitalized," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2193-2199, March.
    6. Daniel Fonseca Costa & Brenda Melissa Fonseca & Lélis Pedro Andrade & Bruno César Melo Moreira, 2023. "Bibliometric and scientometric analysis of the scientific field in taxation," SN Business & Economics, Springer, vol. 3(1), pages 1-28, January.

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