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Masoumi N, Khajavi R. A fuzzy classifier for evaluation of research topics by using keyword co-occurrence network and sponsors information. Scientometrics 2023. [DOI: 10.1007/s11192-022-04618-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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Hu K, Wu H, Qi K, Yu J, Yang S, Yu T, Zheng J, Liu B. A domain keyword analysis approach extending Term Frequency-Keyword Active Index with Google Word2Vec model. Scientometrics 2017. [DOI: 10.1007/s11192-017-2574-9] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Xiao L, Chen G, Sun J, Han S, Zhang C. Exploring the topic hierarchy of digital library research in China using keyword networks: a K-core decomposition approach. Scientometrics 2016. [DOI: 10.1007/s11192-016-2051-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Ding Y, Chowdhury GG, Foo S. Incorporating the results of co-word analyses to increase search variety for information retrieval. J Inf Sci 2016. [DOI: 10.1177/016555150002600606] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This research aims to incorporate the results of co-word analysis into information retrieval as a means to increase search variety for end users in the domain of information retrieval. Relevant data were first collected from Science Citation Index and Social Sciences Citation Index for the period 1987-1997. The results of co-word analysis on the data were compared with similar data obtained from three thesauri: Library and Information Science Abstracts thesaurus, Library of Congress Subject Headings and Thesaurus of Information Technology Terms. The differences detected between them indicate that the search variety may be increased by combining co-word analysis with the use of traditional thesauri. Subsequently, the results of co-word analysis were compared with each other for two different periods (1987-1991 and 1992-1997). The changes among them were identified, implying that co-word analysis may be used directly to identify dynamic changes in its chosen domain area, thereby providing better up-to-date information to aid the information search process.
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Affiliation(s)
- Ying Ding
- Nanyang Technological University, Singapore
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Selecting publication keywords for domain analysis in bibliometrics: A comparison of three methods. J Informetr 2016. [DOI: 10.1016/j.joi.2016.01.006] [Citation(s) in RCA: 99] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Chen G, Xiao L, Hu CP, Zhao XQ. Identifying the research focus of Library and Information Science institutions in China with institution-specific keywords. Scientometrics 2015. [DOI: 10.1007/s11192-015-1545-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Ding Y, Chowdhury GG, Foo S, Qian W. Bibliometric information retrieval system (BIRS): A web search interface utilizing bibliometric research results. ACTA ACUST UNITED AC 2000. [DOI: 10.1002/1097-4571(2000)9999:9999<::aid-asi1031>3.0.co;2-b] [Citation(s) in RCA: 20] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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