Search (3 results, page 1 of 1)

  • × author_ss:"Yi, K."
  1. Yi, K.: Harnessing collective intelligence in social tagging using Delicious (2012) 0.03 
    0.02878214 = product of:
  0.08634642 = sum of:
    0.08634642 = sum of:
      0.047006465 = weight(_text_:indexing in 515) [ClassicSimilarity], result of:
        0.047006465 = score(doc=515,freq=2.0), product of:
          0.22229293 = queryWeight, product of:
            3.8278677 = idf(docFreq=2614, maxDocs=44218)
            0.05807226 = queryNorm
          0.21146181 = fieldWeight in 515, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.8278677 = idf(docFreq=2614, maxDocs=44218)
            0.0390625 = fieldNorm(doc=515)
      0.039339952 = weight(_text_:22 in 515) [ClassicSimilarity], result of:
        0.039339952 = score(doc=515,freq=2.0), product of:
          0.20335917 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05807226 = queryNorm
          0.19345059 = fieldWeight in 515, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=515)
  0.33333334 = coord(1/3)
    Abstract
    A new collaborative approach in information organization and sharing has recently arisen, known as collaborative tagging or social indexing. A key element of collaborative tagging is the concept of collective intelligence (CI), which is a shared intelligence among all participants. This research investigates the phenomenon of social tagging in the context of CI with the aim to serve as a stepping-stone towards the mining of truly valuable social tags for web resources. This study focuses on assessing and evaluating the degree of CI embedded in social tagging over time in terms of two-parameter values, number of participants, and top frequency ranking window. Five different metrics were adopted and utilized for assessing the similarity between ranking lists: overlapList, overlapRank, Footrule, Fagin's measure, and the Inverse Rank measure. The result of this study demonstrates that a substantial degree of CI is most likely to be achieved when somewhere between the first 200 and 400 people have participated in tagging, and that a target degree of CI can be projected by controlling the two factors along with the selection of a similarity metric. The study also tests some experimental conditions for detecting social tags with high CI degree. The results of this study can be applicable to the study of filtering social tags based on CI; filtered social tags may be utilized for the metadata creation of tagged resources and possibly for the retrieval of tagged resources.
    Date
    25.12.2012 15:22:37
  2. Yi, K.; Choi, N.; Kim, Y.S.: ¬A content analysis of Twitter hyperlinks and their application in web resource indexing (2016) 0.01 
    0.013569599 = product of:
  0.040708795 = sum of:
    0.040708795 = product of:
      0.08141759 = sum of:
        0.08141759 = weight(_text_:indexing in 3075) [ClassicSimilarity], result of:
          0.08141759 = score(doc=3075,freq=6.0), product of:
            0.22229293 = queryWeight, product of:
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.05807226 = queryNorm
            0.3662626 = fieldWeight in 3075, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3075)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)
    Abstract
    Twitter has emerged as a popular source of sharing and delivering news information. In tweet messages, URLs to web resources and hashtags are often included. This study investigates the potential of the hyperlinks and hashtags as topical clues and indicators to tweet messages. For this study, we crawled and analyzed about 1.5 million tweets for a 3-month period covering any topic or subject. The findings of this study revealed a power law relationship for the ranking and frequency of (a) the host names of URLs, and (b) a pair of hashtags and URLs that appeared in the tweet messages. This study also discovered that the most popular URLs used in tweets come from news and media websites, and a majority of the hyperlinked resources are news web pages. One implication of this study is that Twitter users are becoming more active in sharing already published information than producing new information. Finally, our investigation on hashtags for web resource indexing reveals that hashtags have the potential to be used as indexing terms for co-occurring URLs in the same tweet. We also discuss the implications of this study for web resource recommendation.
  3. Yi, K.: Automatic text classification using library classification schemes : trends, issues and challenges (2007) 0.01 
    0.009179322 = product of:
  0.027537964 = sum of:
    0.027537964 = product of:
      0.05507593 = sum of:
        0.05507593 = weight(_text_:22 in 2560) [ClassicSimilarity], result of:
          0.05507593 = score(doc=2560,freq=2.0), product of:
            0.20335917 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05807226 = queryNorm
            0.2708308 = fieldWeight in 2560, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2560)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)
    Date
    22. 9.2008 18:31:54

Authors

Years

Themes