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  • × author_ss:"Yi, K."
  • × year_i:[2010 TO 2020}
  1. Yi, K.: Harnessing collective intelligence in social tagging using Delicious (2012) 0.02 
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    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
    Type
    a
  2. Yi, K.; Choi, N.; Kim, Y.S.: ¬A content analysis of Twitter hyperlinks and their application in web resource indexing (2016) 0.00 
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    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.
    Type
    a
  3. Yi, K.; Chan, L.M.: Revisiting the syntactical and structural analysis of Library of Congress Subject Headings for the digital environment (2010) 0.00 
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    Abstract
    With the current information environment characterized by the proliferation of digital resources, including collaboratively created and shared resources, Library of Congress Subject Headings (LCSH) is facing the challenges of effective and efficient subject-based organization and retrieval of digital resources. To explore the feasibility of utilizing LCSH in a digital environment, we might need to revisit its basic characteristics. The objectives of our study were to analyze LCSH in both syntactic and relational structures, to discover the structural characteristics of LCSH, and to identify problems and issues for the feasibility of LCSH as an effective subject access tool. This study reports and discusses issues raised by the syntactic and hierarchical structures of LCSH that present challenges to its use in a networked environment. Given the results of this study, we recommend a number of provisional future directions for the development of LCSH towards further becoming a viable system for digital and networked resources.
    Type
    a
  4. Yi, K.: ¬A semantic similarity approach to predicting Library of Congress subject headings for social tags (2010) 0.00 
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    Abstract
    Social tagging or collaborative tagging has become a new trend in the organization, management, and discovery of digital information. The rapid growth of shared information mostly controlled by social tags poses a new challenge for social tag-based information organization and retrieval. A plausible approach for this challenge is linking social tags to a controlled vocabulary. As an introductory step for this approach, this study investigates ways of predicting relevant subject headings for resources from social tags assigned to the resources. The prediction of subject headings was measured by five different similarity measures: tf-idf, cosine-based similarity (CoS), Jaccard similarity (or Jaccard coefficient; JS), Mutual information (MI), and information radius (IRad). Their results were compared to those by professionals. The results show that a CoS measure based on top five social tags was most effective. Inclusions of more social tags only aggravate the performance. The performance of JS is comparable to the performance of CoS while tf-idf is comparable with up to 70% less than the best performance. MI and IRad have inferior performance compared to the other methods. This study demonstrates the application of the similarity measuring techniques to the prediction of correct Library of Congress subject headings.
    Type
    a

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