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Bibliometric information retrieval system (BIRS): A web search interface utilizing bibliometric research results

Author

Listed:
  • Ying Ding
  • Gobinda G. Chowdhury
  • Schubert Foo
  • Weizhong Qian

Abstract

The aim of this article is to test whether the results obtained from a specific bibliographic research can be applied to a real search environment and enhance the level of utility of an information retrieval session for all levels of end users. In this respect, a Web‐based Bibliometric Information Retrieval System (BIRS) has been designed and created, with facilities to assist the end users to get better understanding of their search domain, formulate and expand their search queries, and visualize the bibliographic research results. There are three specific features in the system design of the BIRS: the information visualization feature of the BIRS (cocitation maps) to guide the end users to identify the important research groups and capture the detailed information about the intellectual structure of the search domain; the multilevel browsing feature to allow the end users to go to different levels of interesting topics; and the common user interface feature to enable the end users to search all kinds of databases regardless of different searching systems, different working platforms, different database producer and supplier, such as different Web search engines, different library OPACs, or different on‐line databases. A preliminary user evaluation study of BIRS revealed that users generally found it easy to form and expand their queries, and that BIRS helped them acquire useful background information about the search domain. They also pointed out aspects of information visualization, multilevel browsing, and common user interface as novel characteristics exhibited by BIRS.

Suggested Citation

  • Ying Ding & Gobinda G. Chowdhury & Schubert Foo & Weizhong Qian, 2000. "Bibliometric information retrieval system (BIRS): A web search interface utilizing bibliometric research results," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 51(13), pages 1190-1204.
  • Handle: RePEc:bla:jamest:v:51:y:2000:i:13:p:1190-1204
    DOI: 10.1002/1097-4571(2000)9999:99993.0.CO;2-B
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    Cited by:

    1. María Pinto & Rosaura Fernández-Pascual & David Caballero-Mariscal & Dora Sales, 2020. "Information literacy trends in higher education (2006–2019): visualizing the emerging field of mobile information literacy," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1479-1510, August.
    2. Alexander Karlsson & Björn Hammarfelt & H. Joe Steinhauer & Göran Falkman & Nasrine Olson & Gustaf Nelhans & Jan Nolin, 2015. "Modeling uncertainty in bibliometrics and information retrieval: an information fusion approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2255-2274, March.
    3. Ding, Ying, 2011. "Community detection: Topological vs. topical," Journal of Informetrics, Elsevier, vol. 5(4), pages 498-514.
    4. Félix Moya-Anegón & Benjamín Vargas-Quesada & Victor Herrero-Solana & Zaida Chinchilla-Rodríguez & Elena Corera-Álvarez & Francisco J. Munoz-Fernández, 2004. "A new technique for building maps of large scientific domains based on the cocitation of classes and categories," Scientometrics, Springer;Akadémiai Kiadó, vol. 61(1), pages 129-145, September.
    5. Manuel Portugal Ferreira & Cláudia Frias Pinto & Fernando Ribeiro Serra, 2014. "The transaction costs theory in international business research: a bibliometric study over three decades," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 1899-1922, March.
    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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