Search (108 results, page 1 of 6)

  • × theme_ss:"Social tagging"
  1. Strader, C.R.: Author-assigned keywords versus Library of Congress Subject Headings : implications for the cataloging of electronic theses and dissertations (2009) 0.02
    0.020407092 = product of:
      0.06122127 = sum of:
        0.06122127 = sum of:
          0.025606511 = weight(_text_:of in 3602) [ClassicSimilarity], result of:
            0.025606511 = score(doc=3602,freq=26.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = queryNorm
              0.37376386 = fieldWeight in 3602, product of:
                5.0990195 = tf(freq=26.0), with freq of:
                  26.0 = termFreq=26.0
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.046875 = fieldNorm(doc=3602)
          0.03561476 = weight(_text_:22 in 3602) [ClassicSimilarity], result of:
            0.03561476 = score(doc=3602,freq=2.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = queryNorm
              0.23214069 = fieldWeight in 3602, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046875 = fieldNorm(doc=3602)
      0.33333334 = coord(1/3)
    
    Abstract
    This study is an examination of the overlap between author-assigned keywords and cataloger-assigned Library of Congress Subject Headings (LCSH) for a set of electronic theses and dissertations in Ohio State University's online catalog. The project is intended to contribute to the literature on the issue of keywords versus controlled vocabularies in the use of online catalogs and databases. Findings support previous studies' conclusions that both keywords and controlled vocabularies complement one another. Further, even in the presence of bibliographic record enhancements, such as abstracts or summaries, keywords and subject headings provided a significant number of unique terms that could affect the success of keyword searches. Implications for the maintenance of controlled vocabularies such as LCSH also are discussed in light of the patterns of matches and nonmatches found between the keywords and their corresponding subject headings.
    Date
    10. 9.2000 17:38:22
  2. Catarino, M.E.; Baptista, A.A.: Relating folksonomies with Dublin Core (2008) 0.02
    0.019210255 = product of:
      0.057630762 = sum of:
        0.057630762 = sum of:
          0.015658367 = weight(_text_:of in 2652) [ClassicSimilarity], result of:
            0.015658367 = score(doc=2652,freq=14.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = queryNorm
              0.22855641 = fieldWeight in 2652, product of:
                3.7416575 = tf(freq=14.0), with freq of:
                  14.0 = termFreq=14.0
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.0390625 = fieldNorm(doc=2652)
          0.041972395 = weight(_text_:22 in 2652) [ClassicSimilarity], result of:
            0.041972395 = score(doc=2652,freq=4.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = queryNorm
              0.27358043 = fieldWeight in 2652, product of:
                2.0 = tf(freq=4.0), with freq of:
                  4.0 = termFreq=4.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.0390625 = fieldNorm(doc=2652)
      0.33333334 = coord(1/3)
    
    Abstract
    Folksonomy is the result of describing Web resources with tags created by Web users. Although it has become a popular application for the description of resources, in general terms Folksonomies are not being conveniently integrated in metadata. However, if the appropriate metadata elements are identified, then further work may be conducted to automatically assign tags to these elements (RDF properties) and use them in Semantic Web applications. This article presents research carried out to continue the project Kinds of Tags, which intends to identify elements required for metadata originating from folksonomies and to propose an application profile for DC Social Tagging. The work provides information that may be used by software applications to assign tags to metadata elements and, therefore, means for tags to be conveniently gathered by metadata interoperability tools. Despite the unquestionably high value of DC and the significance of the already existing properties in DC Terms, the pilot study show revealed a significant number of tags for which no corresponding properties yet existed. A need for new properties, such as Action, Depth, Rate, and Utility was determined. Those potential new properties will have to be validated in a later stage by the DC Social Tagging Community.
    Pages
    S.14-22
    Source
    Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas
  3. Kruk, S.R.; Kruk, E.; Stankiewicz, K.: Evaluation of semantic and social technologies for digital libraries (2009) 0.02
    0.018567387 = product of:
      0.055702157 = sum of:
        0.055702157 = sum of:
          0.020087399 = weight(_text_:of in 3387) [ClassicSimilarity], result of:
            0.020087399 = score(doc=3387,freq=16.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = queryNorm
              0.2932045 = fieldWeight in 3387, product of:
                4.0 = tf(freq=16.0), with freq of:
                  16.0 = termFreq=16.0
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.046875 = fieldNorm(doc=3387)
          0.03561476 = weight(_text_:22 in 3387) [ClassicSimilarity], result of:
            0.03561476 = score(doc=3387,freq=2.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = queryNorm
              0.23214069 = fieldWeight in 3387, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046875 = fieldNorm(doc=3387)
      0.33333334 = coord(1/3)
    
    Abstract
    Libraries are the tools we use to learn and to answer our questions. The quality of our work depends, among others, on the quality of the tools we use. Recent research in digital libraries is focused, on one hand on improving the infrastructure of the digital library management systems (DLMS), and on the other on improving the metadata models used to annotate collections of objects maintained by DLMS. The latter includes, among others, the semantic web and social networking technologies. Recently, the semantic web and social networking technologies are being introduced to the digital libraries domain. The expected outcome is that the overall quality of information discovery in digital libraries can be improved by employing social and semantic technologies. In this chapter we present the results of an evaluation of social and semantic end-user information discovery services for the digital libraries.
    Date
    1. 8.2010 12:35:22
  4. Yi, K.: Harnessing collective intelligence in social tagging using Delicious (2012) 0.02
    0.018026924 = product of:
      0.05408077 = sum of:
        0.05408077 = sum of:
          0.024401804 = weight(_text_:of in 515) [ClassicSimilarity], result of:
            0.024401804 = score(doc=515,freq=34.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = queryNorm
              0.35617945 = fieldWeight in 515, product of:
                5.8309517 = tf(freq=34.0), with freq of:
                  34.0 = termFreq=34.0
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.0390625 = fieldNorm(doc=515)
          0.029678967 = weight(_text_:22 in 515) [ClassicSimilarity], result of:
            0.029678967 = score(doc=515,freq=2.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = 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
    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.12, S.2488-2502
  5. Rolla, P.J.: User tags versus Subject headings : can user-supplied data improve subject access to library collections? (2009) 0.02
    0.017670318 = product of:
      0.053010955 = sum of:
        0.053010955 = sum of:
          0.017396197 = weight(_text_:of in 3601) [ClassicSimilarity], result of:
            0.017396197 = score(doc=3601,freq=12.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = queryNorm
              0.25392252 = fieldWeight in 3601, product of:
                3.4641016 = tf(freq=12.0), with freq of:
                  12.0 = termFreq=12.0
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.046875 = fieldNorm(doc=3601)
          0.03561476 = weight(_text_:22 in 3601) [ClassicSimilarity], result of:
            0.03561476 = score(doc=3601,freq=2.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = queryNorm
              0.23214069 = fieldWeight in 3601, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046875 = fieldNorm(doc=3601)
      0.33333334 = coord(1/3)
    
    Abstract
    Some members of the library community, including the Library of Congress Working Group on the Future of Bibliographic Control, have suggested that libraries should open up their catalogs to allow users to add descriptive tags to the bibliographic data in catalog records. The web site LibraryThing currently permits its members to add such user tags to its records for books and therefore provides a useful resource to contrast with library bibliographic records. A comparison between the LibraryThing tags for a group of books and the library-supplied subject headings for the same books shows that users and catalogers approach these descriptors very differently. Because of these differences, user tags can enhance subject access to library materials, but they cannot entirely replace controlled vocabularies such as the Library of Congress subject headings.
    Date
    10. 9.2000 17:38:22
  6. Chen, M.; Liu, X.; Qin, J.: Semantic relation extraction from socially-generated tags : a methodology for metadata generation (2008) 0.02
    0.016131433 = product of:
      0.048394296 = sum of:
        0.048394296 = sum of:
          0.01871533 = weight(_text_:of in 2648) [ClassicSimilarity], result of:
            0.01871533 = score(doc=2648,freq=20.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = queryNorm
              0.27317715 = fieldWeight in 2648, product of:
                4.472136 = tf(freq=20.0), with freq of:
                  20.0 = termFreq=20.0
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.0390625 = fieldNorm(doc=2648)
          0.029678967 = weight(_text_:22 in 2648) [ClassicSimilarity], result of:
            0.029678967 = score(doc=2648,freq=2.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = queryNorm
              0.19345059 = fieldWeight in 2648, 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=2648)
      0.33333334 = coord(1/3)
    
    Abstract
    The growing predominance of social semantics in the form of tagging presents the metadata community with both opportunities and challenges as for leveraging this new form of information content representation and for retrieval. One key challenge is the absence of contextual information associated with these tags. This paper presents an experiment working with Flickr tags as an example of utilizing social semantics sources for enriching subject metadata. The procedure included four steps: 1) Collecting a sample of Flickr tags, 2) Calculating cooccurrences between tags through mutual information, 3) Tracing contextual information of tag pairs via Google search results, 4) Applying natural language processing and machine learning techniques to extract semantic relations between tags. The experiment helped us to build a context sentence collection from the Google search results, which was then processed by natural language processing and machine learning algorithms. This new approach achieved a reasonably good rate of accuracy in assigning semantic relations to tag pairs. This paper also explores the implications of this approach for using social semantics to enrich subject metadata.
    Source
    Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas
  7. Choi, Y.; Syn, S.Y.: Characteristics of tagging behavior in digitized humanities online collections (2016) 0.02
    0.016131433 = product of:
      0.048394296 = sum of:
        0.048394296 = sum of:
          0.01871533 = weight(_text_:of in 2891) [ClassicSimilarity], result of:
            0.01871533 = score(doc=2891,freq=20.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = queryNorm
              0.27317715 = fieldWeight in 2891, product of:
                4.472136 = tf(freq=20.0), with freq of:
                  20.0 = termFreq=20.0
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.0390625 = fieldNorm(doc=2891)
          0.029678967 = weight(_text_:22 in 2891) [ClassicSimilarity], result of:
            0.029678967 = score(doc=2891,freq=2.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = queryNorm
              0.19345059 = fieldWeight in 2891, 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=2891)
      0.33333334 = coord(1/3)
    
    Abstract
    The purpose of this study was to examine user tags that describe digitized archival collections in the field of humanities. A collection of 8,310 tags from a digital portal (Nineteenth-Century Electronic Scholarship, NINES) was analyzed to find out what attributes of primary historical resources users described with tags. Tags were categorized to identify which tags describe the content of the resource, the resource itself, and subjective aspects (e.g., usage or emotion). The study's findings revealed that over half were content-related; tags representing opinion, usage context, or self-reference, however, reflected only a small percentage. The study further found that terms related to genre or physical format of a resource were frequently used in describing primary archival resources. It was also learned that nontextual resources had lower numbers of content-related tags and higher numbers of document-related tags than textual resources and bibliographic materials; moreover, textual resources tended to have more user-context-related tags than other resources. These findings help explain users' tagging behavior and resource interpretation in primary resources in the humanities. Such information provided through tags helps information professionals decide to what extent indexing archival and cultural resources should be done for resource description and discovery, and understand users' terminology.
    Date
    21. 4.2016 11:23:22
    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.5, S.1089-1104
  8. Qin, C.; Liu, Y.; Mou, J.; Chen, J.: User adoption of a hybrid social tagging approach in an online knowledge community (2019) 0.02
    0.015472822 = product of:
      0.046418466 = sum of:
        0.046418466 = sum of:
          0.016739499 = weight(_text_:of in 5492) [ClassicSimilarity], result of:
            0.016739499 = score(doc=5492,freq=16.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = queryNorm
              0.24433708 = fieldWeight in 5492, product of:
                4.0 = tf(freq=16.0), with freq of:
                  16.0 = termFreq=16.0
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.0390625 = fieldNorm(doc=5492)
          0.029678967 = weight(_text_:22 in 5492) [ClassicSimilarity], result of:
            0.029678967 = score(doc=5492,freq=2.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = queryNorm
              0.19345059 = fieldWeight in 5492, 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=5492)
      0.33333334 = coord(1/3)
    
    Abstract
    Purpose Online knowledge communities make great contributions to global knowledge sharing and innovation. Resource tagging approaches have been widely adopted in such communities to describe, annotate and organize knowledge resources mainly through users' participation. However, it is unclear what causes the adoption of a particular resource tagging approach. The purpose of this paper is to identify factors that drive users to use a hybrid social tagging approach. Design/methodology/approach Technology acceptance model and social cognitive theory are adopted to support an integrated model proposed in this paper. Zhihu, one of the most popular online knowledge communities in China, is taken as the survey context. A survey was conducted with a questionnaire and collected data were analyzed through structural equation model. Findings A new hybrid social resource tagging approach was refined and described. The empirical results revealed that self-efficacy, perceived usefulness (PU) and perceived ease of use exert positive effect on users' attitude. Moreover, social influence, PU and attitude impact significantly on users' intention to use a hybrid social resource tagging approach. Originality/value Theoretically, this study enriches the type of resource tagging approaches and recognizes factors influencing user adoption to use it. Regarding the practical parts, the results provide online information system providers and designers with referential strategies to improve the performance of the current tagging approaches and promote them.
    Date
    20. 1.2015 18:30:22
    Source
    Aslib journal of information management. 71(2019) no.2, S.155-175
  9. Vander Wal, T.: Welcome to the Matrix! (2008) 0.01
    0.014026793 = product of:
      0.042080376 = sum of:
        0.042080376 = sum of:
          0.018337203 = weight(_text_:of in 2881) [ClassicSimilarity], result of:
            0.018337203 = score(doc=2881,freq=30.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = queryNorm
              0.26765788 = fieldWeight in 2881, product of:
                5.477226 = tf(freq=30.0), with freq of:
                  30.0 = termFreq=30.0
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.03125 = fieldNorm(doc=2881)
          0.023743173 = weight(_text_:22 in 2881) [ClassicSimilarity], result of:
            0.023743173 = score(doc=2881,freq=2.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = queryNorm
              0.15476047 = fieldWeight in 2881, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.03125 = fieldNorm(doc=2881)
      0.33333334 = coord(1/3)
    
    Abstract
    My keynote at the workshop "Social Tagging in Knowledge Organization" was a great opportunity to make and share new experiences. For the first time ever, I sat in my office at home and gave a live web video presentation to a conference audience elsewhere on the globe. At the same time, it was also an opportunity to premier my conceptual model "Matrix of Perception" to an interdisciplinary audience of researchers and practitioners with a variety of backgrounds - reaching from philosophy, psychology, pedagogy and computation to library science and economics. The interdisciplinary approach of the conference is also mirrored in the structure of this volume, with articles on the theoretical background, the empirical analysis and the potential applications of tagging, for instance in university libraries, e-learning, or e-commerce. As an introduction to the topic of "social tagging" I would like to draw your attention to some foundation concepts of the phenomenon I have racked my brain with for the last few month. One thing I have seen missing in recent research and system development is a focus on the variety of user perspectives in social tagging. Different people perceive tagging in complex variegated ways and use this form of knowledge organization for a variety of purposes. My analytical interest lies in understanding the personas and patterns in tagging systems and in being able to label their different perceptions. To come up with a concise picture of user expectations, needs and activities, I have broken down the perspectives on tagging into two different categories, namely "faces" and "depth". When put together, they form the "Matrix of Perception" - a nuanced view of stakeholders and their respective levels of participation.
    Date
    22. 6.2009 9:15:45
  10. Kim, H.L.; Scerri, S.; Breslin, J.G.; Decker, S.; Kim, H.G.: ¬The state of the art in tag ontologies : a semantic model for tagging and folksonomies (2008) 0.01
    0.013838528 = product of:
      0.04151558 = sum of:
        0.04151558 = sum of:
          0.0118366135 = weight(_text_:of in 2650) [ClassicSimilarity], result of:
            0.0118366135 = score(doc=2650,freq=8.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = queryNorm
              0.17277241 = fieldWeight in 2650, product of:
                2.828427 = tf(freq=8.0), with freq of:
                  8.0 = termFreq=8.0
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.0390625 = fieldNorm(doc=2650)
          0.029678967 = weight(_text_:22 in 2650) [ClassicSimilarity], result of:
            0.029678967 = score(doc=2650,freq=2.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = queryNorm
              0.19345059 = fieldWeight in 2650, 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=2650)
      0.33333334 = coord(1/3)
    
    Abstract
    There is a growing interest into how we represent and share tagging data in collaborative tagging systems. Conventional tags, meaning freely created tags that are not associated with a structured ontology, are not naturally suited for collaborative processes, due to linguistic and grammatical variations, as well as human typing errors. Additionally, tags reflect personal views of the world by individual users, and are not normalised for synonymy, morphology or any other mapping. Our view is that the conventional approach provides very limited semantic value for collaboration. Moreover, in cases where there is some semantic value, automatically sharing semantics via computer manipulations is extremely problematic. This paper explores these problems by discussing approaches for collaborative tagging activities at a semantic level, and presenting conceptual models for collaborative tagging activities and folksonomies. We present criteria for the comparison of existing tag ontologies and discuss their strengths and weaknesses in relation to these criteria.
    Source
    Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas
  11. Bentley, C.M.; Labelle, P.R.: ¬A comparison of social tagging designs and user participation (2008) 0.01
    0.013819531 = product of:
      0.04145859 = sum of:
        0.04145859 = sum of:
          0.01771542 = weight(_text_:of in 2657) [ClassicSimilarity], result of:
            0.01771542 = score(doc=2657,freq=28.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = queryNorm
              0.25858206 = fieldWeight in 2657, product of:
                5.2915025 = tf(freq=28.0), with freq of:
                  28.0 = termFreq=28.0
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.03125 = fieldNorm(doc=2657)
          0.023743173 = weight(_text_:22 in 2657) [ClassicSimilarity], result of:
            0.023743173 = score(doc=2657,freq=2.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = queryNorm
              0.15476047 = fieldWeight in 2657, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.03125 = fieldNorm(doc=2657)
      0.33333334 = coord(1/3)
    
    Abstract
    Social tagging empowers users to categorize content in a personally meaningful way while harnessing their potential to contribute to a collaborative construction of knowledge (Vander Wal, 2007). In addition, social tagging systems offer innovative filtering mechanisms that facilitate resource discovery and browsing (Mathes, 2004). As a result, social tags may support online communication, informal or intended learning as well as the development of online communities. The purpose of this mixed methods study is to examine how undergraduate students participate in social tagging activities in order to learn about their motivations, behaviours and practices. A better understanding of their knowledge, habits and interactions with such systems will help practitioners and developers identify important factors when designing enhancements. In the first phase of the study, students enrolled at a Canadian university completed 103 questionnaires. Quantitative results focusing on general familiarity with social tagging, frequently used Web 2.0 sites, and the purpose for engaging in social tagging activities were compiled. Eight questionnaire respondents participated in follow-up semi-structured interviews that further explored tagging practices by situating questionnaire responses within concrete experiences using popular websites such as YouTube, Facebook, Del.icio.us, and Flickr. Preliminary results of this study echo findings found in the growing literature concerning social tagging from the fields of computer science (Sen et al., 2006) and information science (Golder & Huberman, 2006; Macgregor & McCulloch, 2006). Generally, two classes of social taggers emerge: those who focus on tagging for individual purposes, and those who view tagging as a way to share or communicate meaning to others. Heavy del.icio.us users, for example, were often focused on simply organizing their own content, and seemed to be conscientiously maintaining their own personally relevant categorizations while, in many cases, placing little importance on the tags of others. Conversely, users tagging items primarily to share content preferred to use specific terms to optimize retrieval and discovery by others. Our findings should inform practitioners of how interaction design can be tailored for different tagging systems applications, and how these findings are positioned within the current debate surrounding social tagging among the resource discovery community. We also hope to direct future research in the field to place a greater importance on exploring the benefits of tagging as a socially-driven endeavour rather than uniquely as a means of managing information.
    Source
    Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas
  12. DeZelar-Tiedman, V.: Doing the LibraryThing(TM) in an academic library catalog (2008) 0.01
    0.013148739 = product of:
      0.039446216 = sum of:
        0.039446216 = sum of:
          0.015703043 = weight(_text_:of in 2666) [ClassicSimilarity], result of:
            0.015703043 = score(doc=2666,freq=22.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = queryNorm
              0.2292085 = fieldWeight in 2666, product of:
                4.690416 = tf(freq=22.0), with freq of:
                  22.0 = termFreq=22.0
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.03125 = fieldNorm(doc=2666)
          0.023743173 = weight(_text_:22 in 2666) [ClassicSimilarity], result of:
            0.023743173 = score(doc=2666,freq=2.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = queryNorm
              0.15476047 = fieldWeight in 2666, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.03125 = fieldNorm(doc=2666)
      0.33333334 = coord(1/3)
    
    Abstract
    Many libraries and other cultural institutions are incorporating Web 2.0 features and enhanced metadata into their catalogs (Trant 2006). These value-added elements include those typically found in commercial and social networking sites, such as book jacket images, reviews, and usergenerated tags. One such site that libraries are exploring as a model is LibraryThing (www.librarything.com) LibraryThing is a social networking site that allows users to "catalog" their own book collections. Members can add tags and reviews to records for books, as well as engage in online discussions. In addition to its service for individuals, LibraryThing offers a feebased service to libraries, where institutions can add LibraryThing tags, recommendations, and other features to their online catalog records. This poster will present data analyzing the quality and quantity of the metadata that a large academic library would expect to gain if utilizing such a service, focusing on the overlap between titles found in the library's catalog and in LibraryThing's database, and on a comparison between the controlled subject headings in the former and the user-generated tags in the latter. During February through April 2008, a random sample of 383 titles from the University of Minnesota Libraries catalog was searched in LibraryThing. Eighty works, or 21 percent of the sample, had corresponding records available in LibraryThing. Golder and Huberman (2006) outline the advantages and disadvantages of using controlled vocabulary for subject access to information resources versus the growing trend of tags supplied by users or by content creators. Using the 80 matched records from the sample, comparisons were made between the user-supplied tags in LibraryThing (social tags) and the subject headings in the library catalog records (controlled vocabulary system). In the library records, terms from all 6XX MARC fields were used. To make a more meaningful comparison, controlled subject terms were broken down into facets according to their headings and subheadings, and each unique facet counted separately. A total of 227 subject terms were applied to the 80 catalog records, an average of 2.84 per record. In LibraryThing, 698 tags were applied to the same 80 titles, an average of 8.73 per title. The poster will further explore the relationships between the terms applied in each source, and identify where overlaps and complementary levels of access occur.
    Source
    Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas
  13. Danowski, P.: Authority files and Web 2.0 : Wikipedia and the PND. An Example (2007) 0.01
    0.012682905 = product of:
      0.038048714 = sum of:
        0.038048714 = sum of:
          0.008369749 = weight(_text_:of in 1291) [ClassicSimilarity], result of:
            0.008369749 = score(doc=1291,freq=4.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = queryNorm
              0.12216854 = fieldWeight in 1291, product of:
                2.0 = tf(freq=4.0), with freq of:
                  4.0 = termFreq=4.0
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.0390625 = fieldNorm(doc=1291)
          0.029678967 = weight(_text_:22 in 1291) [ClassicSimilarity], result of:
            0.029678967 = score(doc=1291,freq=2.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = queryNorm
              0.19345059 = fieldWeight in 1291, 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=1291)
      0.33333334 = coord(1/3)
    
    Abstract
    More and more users index everything on their own in the web 2.0. There are services for links, videos, pictures, books, encyclopaedic articles and scientific articles. All these services are library independent. But must that really be? Can't libraries help with their experience and tools to make user indexing better? On the experience of a project from German language Wikipedia together with the German person authority files (Personen Namen Datei - PND) located at German National Library (Deutsche Nationalbibliothek) I would like to show what is possible. How users can and will use the authority files, if we let them. We will take a look how the project worked and what we can learn for future projects. Conclusions - Authority files can have a role in the web 2.0 - there must be an open interface/ service for retrieval - everything that is indexed on the net with authority files can be easy integrated in a federated search - O'Reilly: You have to found ways that your data get more important that more it will be used
    Content
    Vortrag anlässlich des Workshops: "Extending the multilingual capacity of The European Library in the EDL project Stockholm, Swedish National Library, 22-23 November 2007".
  14. Müller-Prove, M.: Modell und Anwendungsperspektive des Social Tagging (2008) 0.01
    0.007914391 = product of:
      0.023743173 = sum of:
        0.023743173 = product of:
          0.047486346 = sum of:
            0.047486346 = weight(_text_:22 in 2882) [ClassicSimilarity], result of:
              0.047486346 = score(doc=2882,freq=2.0), product of:
                0.15341885 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.043811057 = queryNorm
                0.30952093 = fieldWeight in 2882, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0625 = fieldNorm(doc=2882)
          0.5 = coord(1/2)
      0.33333334 = coord(1/3)
    
    Pages
    S.15-22
  15. Harrer, A.; Lohmann, S.: Potenziale von Tagging als partizipative Methode für Lehrportale und E-Learning-Kurse (2008) 0.01
    0.0069250925 = product of:
      0.020775277 = sum of:
        0.020775277 = product of:
          0.041550554 = sum of:
            0.041550554 = weight(_text_:22 in 2889) [ClassicSimilarity], result of:
              0.041550554 = score(doc=2889,freq=2.0), product of:
                0.15341885 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.043811057 = queryNorm
                0.2708308 = fieldWeight in 2889, 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=2889)
          0.5 = coord(1/2)
      0.33333334 = coord(1/3)
    
    Date
    21. 6.2009 12:22:44
  16. Niemann, C.: Tag-Science : Ein Analysemodell zur Nutzbarkeit von Tagging-Daten (2011) 0.01
    0.0059357933 = product of:
      0.01780738 = sum of:
        0.01780738 = product of:
          0.03561476 = sum of:
            0.03561476 = weight(_text_:22 in 164) [ClassicSimilarity], result of:
              0.03561476 = score(doc=164,freq=2.0), product of:
                0.15341885 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.043811057 = queryNorm
                0.23214069 = fieldWeight in 164, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.046875 = fieldNorm(doc=164)
          0.5 = coord(1/2)
      0.33333334 = coord(1/3)
    
    Source
    ¬Die Kraft der digitalen Unordnung: 32. Arbeits- und Fortbildungstagung der ASpB e. V., Sektion 5 im Deutschen Bibliotheksverband, 22.-25. September 2009 in der Universität Karlsruhe. Hrsg: Jadwiga Warmbrunn u.a
  17. Choi, Y.: ¬A complete assessment of tagging quality : a consolidated methodology (2015) 0.00
    0.004880361 = product of:
      0.014641082 = sum of:
        0.014641082 = product of:
          0.029282164 = sum of:
            0.029282164 = weight(_text_:of in 1730) [ClassicSimilarity], result of:
              0.029282164 = score(doc=1730,freq=34.0), product of:
                0.06850986 = queryWeight, product of:
                  1.5637573 = idf(docFreq=25162, maxDocs=44218)
                  0.043811057 = queryNorm
                0.4274153 = fieldWeight in 1730, product of:
                  5.8309517 = tf(freq=34.0), with freq of:
                    34.0 = termFreq=34.0
                  1.5637573 = idf(docFreq=25162, maxDocs=44218)
                  0.046875 = fieldNorm(doc=1730)
          0.5 = coord(1/2)
      0.33333334 = coord(1/3)
    
    Abstract
    This paper presents a methodological discussion of a study of tagging quality in subject indexing. The data analysis in the study was divided into 3 phases: analysis of indexing consistency, analysis of tagging effectiveness, and analysis of the semantic values of tags. To analyze indexing consistency, this study employed the vector space model-based indexing consistency measures. An analysis of tagging effectiveness with tagging exhaustivity and tag specificity was conducted to ameliorate the drawbacks of consistency analysis based on only the quantitative measures of vocabulary matching. To further investigate the semantic values of tags at various levels of specificity, a latent semantic analysis (LSA) was conducted. To test statistical significance for the relation between tag specificity and semantic quality, correlation analysis was conducted. This research demonstrates the potential of tags for web document indexing with a complete assessment of tagging quality and provides a basis for further study of the strengths and limitations of tagging.
    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.4, S.798-817
  18. Furner, J.: User tagging of library resources : toward a framework for system evaluation (2007) 0.00
    0.0045843013 = product of:
      0.013752903 = sum of:
        0.013752903 = product of:
          0.027505806 = sum of:
            0.027505806 = weight(_text_:of in 703) [ClassicSimilarity], result of:
              0.027505806 = score(doc=703,freq=30.0), product of:
                0.06850986 = queryWeight, product of:
                  1.5637573 = idf(docFreq=25162, maxDocs=44218)
                  0.043811057 = queryNorm
                0.4014868 = fieldWeight in 703, product of:
                  5.477226 = tf(freq=30.0), with freq of:
                    30.0 = termFreq=30.0
                  1.5637573 = idf(docFreq=25162, maxDocs=44218)
                  0.046875 = fieldNorm(doc=703)
          0.5 = coord(1/2)
      0.33333334 = coord(1/3)
    
    Abstract
    Although user tagging of library resources shows substantial promise as a means of improving the quality of users' access to those resources, several important questions about the level and nature of the warrant for basing retrieval tools on user tagging are yet to receive full consideration by library practitioners and researchers. Among these is the simple evaluative question: What, specifically, are the factors that determine whether or not user-tagging services will be successful? If success is to be defined in terms of the effectiveness with which systems perform the particular functions expected of them (rather than simply in terms of popularity), an understanding is needed both of the multifunctional nature of tagging tools, and of the complex nature of users' mental models of that multifunctionality. In this paper, a conceptual framework is developed for the evaluation of systems that integrate user tagging with more traditional methods of library resource description.
  19. Tennis, J.T.: Measured time : imposing a temporal metric to classificatory structures 0.00
    0.0045800544 = product of:
      0.013740162 = sum of:
        0.013740162 = product of:
          0.027480325 = sum of:
            0.027480325 = weight(_text_:of in 3529) [ClassicSimilarity], result of:
              0.027480325 = score(doc=3529,freq=22.0), product of:
                0.06850986 = queryWeight, product of:
                  1.5637573 = idf(docFreq=25162, maxDocs=44218)
                  0.043811057 = queryNorm
                0.40111488 = fieldWeight in 3529, product of:
                  4.690416 = tf(freq=22.0), with freq of:
                    22.0 = termFreq=22.0
                  1.5637573 = idf(docFreq=25162, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=3529)
          0.5 = coord(1/2)
      0.33333334 = coord(1/3)
    
    Abstract
    Describes three units of time helpful for understanding and evaluating classificatory structures: long time (versions and states of classification schemes), short time (the act of indexing as repeated ritual or form), and micro-time (where stages of the interpretation process of indexing are separated out and inventoried). Concludes with a short discussion of how time and the impermanence of classification also conjures up an artistic conceptualization of indexing, and briefly uses that to question the seemingly dominant understanding of classification practice as outcome of scientific management and assembly line thought.
    Source
    Paradigms and conceptual systems in knowledge organization: Proceedings of the Eleventh International ISKO conference, Rome, 23-26 February 2010, ed. Claudio Gnoli, Indeks, Frankfurt M
  20. Stvilia, B.; Jörgensen, C.: Member activities and quality of tags in a collection of historical photographs in Flickr (2010) 0.00
    0.0042995503 = product of:
      0.012898651 = sum of:
        0.012898651 = product of:
          0.025797302 = sum of:
            0.025797302 = weight(_text_:of in 4117) [ClassicSimilarity], result of:
              0.025797302 = score(doc=4117,freq=38.0), product of:
                0.06850986 = queryWeight, product of:
                  1.5637573 = idf(docFreq=25162, maxDocs=44218)
                  0.043811057 = queryNorm
                0.37654874 = fieldWeight in 4117, product of:
                  6.164414 = tf(freq=38.0), with freq of:
                    38.0 = termFreq=38.0
                  1.5637573 = idf(docFreq=25162, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=4117)
          0.5 = coord(1/2)
      0.33333334 = coord(1/3)
    
    Abstract
    To enable and guide effective metadata creation it is essential to understand the structure and patterns of the activities of the community around the photographs, resources used, and scale and quality of the socially created metadata relative to the metadata and knowledge already encoded in existing knowledge organization systems. This article presents an analysis of Flickr member discussions around the photographs of the Library of Congress photostream in Flickr. The article also reports on an analysis of the intrinsic and relational quality of the photostream tags relative to two knowledge organization systems: the Thesaurus for Graphic Materials (TGM) and the Library of Congress Subject Headings (LCSH). Thirty seven percent of the original tag set and 15.3% of the preprocessed set (after the removal of tags with fewer than three characters and URLs) were invalid or misspelled terms. Nouns, named entity terms, and complex terms constituted approximately 77% of the preprocessed set. More than a half of the photostream tags were not found in the TGM and LCSH, and more than a quarter of those terms were regular nouns and noun phrases. This suggests that these terms could be complimentary to more traditional methods of indexing using controlled vocabularies.
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.12, S.2477-2489

Languages

  • e 101
  • d 6
  • i 1
  • More… Less…

Types

  • a 96
  • el 13
  • m 4
  • b 2
  • s 2
  • More… Less…