2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering 2010
DOI: 10.1109/iske.2010.5680768
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A fuzzy linguistic quality evaluation model for digital libraries

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Cited by 2 publications
(3 citation statements)
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“…Furthermore, as mentioned in Section 1, since DLs are destined to serve user communities, the user’s opinion is one of the main aspects to be considered in the quality evaluation of DLs. For these reasons, techniques that require the user’s participation such as interviews and questionnaires are the focus of this review, and the prime methods for collecting qualitative data [8, 18, 25, 3133, 35, 36, 3847, 49–53]. In addition, observations in which user actions are recorded are also common methods for obtaining data [23, 24, 28, 63].…”
Section: Criteria Measurement and Analysismentioning
confidence: 99%
See 1 more Smart Citation
“…Furthermore, as mentioned in Section 1, since DLs are destined to serve user communities, the user’s opinion is one of the main aspects to be considered in the quality evaluation of DLs. For these reasons, techniques that require the user’s participation such as interviews and questionnaires are the focus of this review, and the prime methods for collecting qualitative data [8, 18, 25, 3133, 35, 36, 3847, 49–53]. In addition, observations in which user actions are recorded are also common methods for obtaining data [23, 24, 28, 63].…”
Section: Criteria Measurement and Analysismentioning
confidence: 99%
“…In [47, 49, 51–53], to represent the user’s perceptions of the quality of the DL, fuzzy linguistic information [66, 67, 7174] is used instead of numerical values. In particular, ordinal fuzzy linguistic modelling [66, 67] is used to represent the user’s perceptions.…”
Section: Criteria Measurement and Analysismentioning
confidence: 99%
“…In the previous research, Herrera-Viedma, E. et al provided several evaluation methods for the information quality of specific Web sites, where fuzzy linguistic model and semantic Web technologies are jointly used to improve the evaluation process [16,15,17,14], which gives some helpful inspiration to the features extraction, especially semantic features extraction in statistical learning based methods.…”
Section: Introductionmentioning
confidence: 99%