Search (72 results, page 1 of 4)

  • × theme_ss:"Social tagging"
  1. Catarino, M.E.; Baptista, A.A.: Relating folksonomies with Dublin Core (2008) 0.05
    0.0515022 = product of:
      0.1030044 = sum of:
        0.1030044 = sum of:
          0.053092297 = weight(_text_:web in 2652) [ClassicSimilarity], result of:
            0.053092297 = score(doc=2652,freq=6.0), product of:
              0.17002425 = queryWeight, product of:
                3.2635105 = idf(docFreq=4597, maxDocs=44218)
                0.052098576 = queryNorm
              0.3122631 = fieldWeight in 2652, product of:
                2.4494898 = tf(freq=6.0), with freq of:
                  6.0 = termFreq=6.0
                3.2635105 = idf(docFreq=4597, maxDocs=44218)
                0.0390625 = fieldNorm(doc=2652)
          0.049912106 = weight(_text_:22 in 2652) [ClassicSimilarity], result of:
            0.049912106 = score(doc=2652,freq=4.0), product of:
              0.18244034 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.052098576 = 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)
      0.5 = coord(1/2)
    
    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
  2. Danowski, P.: Authority files and Web 2.0 : Wikipedia and the PND. An Example (2007) 0.05
    0.048299447 = product of:
      0.09659889 = sum of:
        0.09659889 = sum of:
          0.061305705 = weight(_text_:web in 1291) [ClassicSimilarity], result of:
            0.061305705 = score(doc=1291,freq=8.0), product of:
              0.17002425 = queryWeight, product of:
                3.2635105 = idf(docFreq=4597, maxDocs=44218)
                0.052098576 = queryNorm
              0.36057037 = fieldWeight in 1291, product of:
                2.828427 = tf(freq=8.0), with freq of:
                  8.0 = termFreq=8.0
                3.2635105 = idf(docFreq=4597, maxDocs=44218)
                0.0390625 = fieldNorm(doc=1291)
          0.03529319 = weight(_text_:22 in 1291) [ClassicSimilarity], result of:
            0.03529319 = score(doc=1291,freq=2.0), product of:
              0.18244034 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.052098576 = 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)
      0.5 = coord(1/2)
    
    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".
    Object
    Web 2.0
  3. Kruk, S.R.; Kruk, E.; Stankiewicz, K.: Evaluation of semantic and social technologies for digital libraries (2009) 0.05
    0.04718572 = product of:
      0.09437144 = sum of:
        0.09437144 = sum of:
          0.052019615 = weight(_text_:web in 3387) [ClassicSimilarity], result of:
            0.052019615 = score(doc=3387,freq=4.0), product of:
              0.17002425 = queryWeight, product of:
                3.2635105 = idf(docFreq=4597, maxDocs=44218)
                0.052098576 = queryNorm
              0.3059541 = fieldWeight in 3387, product of:
                2.0 = tf(freq=4.0), with freq of:
                  4.0 = termFreq=4.0
                3.2635105 = idf(docFreq=4597, maxDocs=44218)
                0.046875 = fieldNorm(doc=3387)
          0.042351827 = weight(_text_:22 in 3387) [ClassicSimilarity], result of:
            0.042351827 = score(doc=3387,freq=2.0), product of:
              0.18244034 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.052098576 = 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)
      0.5 = coord(1/2)
    
    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. Rolla, P.J.: User tags versus Subject headings : can user-supplied data improve subject access to library collections? (2009) 0.04
    0.039567623 = product of:
      0.07913525 = sum of:
        0.07913525 = sum of:
          0.03678342 = weight(_text_:web in 3601) [ClassicSimilarity], result of:
            0.03678342 = score(doc=3601,freq=2.0), product of:
              0.17002425 = queryWeight, product of:
                3.2635105 = idf(docFreq=4597, maxDocs=44218)
                0.052098576 = queryNorm
              0.21634221 = fieldWeight in 3601, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.2635105 = idf(docFreq=4597, maxDocs=44218)
                0.046875 = fieldNorm(doc=3601)
          0.042351827 = weight(_text_:22 in 3601) [ClassicSimilarity], result of:
            0.042351827 = score(doc=3601,freq=2.0), product of:
              0.18244034 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.052098576 = 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)
      0.5 = coord(1/2)
    
    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
  5. Yi, K.: Harnessing collective intelligence in social tagging using Delicious (2012) 0.03
    0.03297302 = product of:
      0.06594604 = sum of:
        0.06594604 = sum of:
          0.030652853 = weight(_text_:web in 515) [ClassicSimilarity], result of:
            0.030652853 = score(doc=515,freq=2.0), product of:
              0.17002425 = queryWeight, product of:
                3.2635105 = idf(docFreq=4597, maxDocs=44218)
                0.052098576 = queryNorm
              0.18028519 = fieldWeight in 515, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.2635105 = idf(docFreq=4597, maxDocs=44218)
                0.0390625 = fieldNorm(doc=515)
          0.03529319 = weight(_text_:22 in 515) [ClassicSimilarity], result of:
            0.03529319 = score(doc=515,freq=2.0), product of:
              0.18244034 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.052098576 = 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)
      0.5 = coord(1/2)
    
    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
  6. Weiand, K.; Hartl, A.; Hausmann, S.; Furche, T.; Bry, F.: Keyword-based search over semantic data (2012) 0.03
    0.028673118 = product of:
      0.057346236 = sum of:
        0.057346236 = product of:
          0.11469247 = sum of:
            0.11469247 = weight(_text_:web in 432) [ClassicSimilarity], result of:
              0.11469247 = score(doc=432,freq=28.0), product of:
                0.17002425 = queryWeight, product of:
                  3.2635105 = idf(docFreq=4597, maxDocs=44218)
                  0.052098576 = queryNorm
                0.6745654 = fieldWeight in 432, product of:
                  5.2915025 = tf(freq=28.0), with freq of:
                    28.0 = termFreq=28.0
                  3.2635105 = idf(docFreq=4597, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=432)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    For a long while, the creation of Web content required at least basic knowledge of Web technologies, meaning that for many Web users, the Web was de facto a read-only medium. This changed with the arrival of the "social Web," when Web applications started to allow users to publish Web content without technological expertise. Here, content creation is often an inclusive, iterative, and interactive process. Examples of social Web applications include blogs, social networking sites, as well as many specialized applications, for example, for saving and sharing bookmarks and publishing photos. Social semantic Web applications are social Web applications in which knowledge is expressed not only in the form of text and multimedia but also through informal to formal annotations that describe, reflect, and enhance the content. These annotations often take the shape of RDF graphs backed by ontologies, but less formal annotations such as free-form tags or tags from a controlled vocabulary may also be available. Wikis are one example of social Web applications for collecting and sharing knowledge. They allow users to easily create and edit documents, so-called wiki pages, using a Web browser. The pages in a wiki are often heavily interlinked, which makes it easy to find related information and browse the content.
    Source
    Semantic search over the Web. Eds.: R. De Virgilio, et al
    Theme
    Semantic Web
  7. Bentley, C.M.; Labelle, P.R.: ¬A comparison of social tagging designs and user participation (2008) 0.03
    0.026378417 = product of:
      0.052756835 = sum of:
        0.052756835 = sum of:
          0.024522282 = weight(_text_:web in 2657) [ClassicSimilarity], result of:
            0.024522282 = score(doc=2657,freq=2.0), product of:
              0.17002425 = queryWeight, product of:
                3.2635105 = idf(docFreq=4597, maxDocs=44218)
                0.052098576 = queryNorm
              0.14422815 = fieldWeight in 2657, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.2635105 = idf(docFreq=4597, maxDocs=44218)
                0.03125 = fieldNorm(doc=2657)
          0.028234553 = weight(_text_:22 in 2657) [ClassicSimilarity], result of:
            0.028234553 = score(doc=2657,freq=2.0), product of:
              0.18244034 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.052098576 = 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)
      0.5 = coord(1/2)
    
    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
  8. DeZelar-Tiedman, V.: Doing the LibraryThing(TM) in an academic library catalog (2008) 0.03
    0.026378417 = product of:
      0.052756835 = sum of:
        0.052756835 = sum of:
          0.024522282 = weight(_text_:web in 2666) [ClassicSimilarity], result of:
            0.024522282 = score(doc=2666,freq=2.0), product of:
              0.17002425 = queryWeight, product of:
                3.2635105 = idf(docFreq=4597, maxDocs=44218)
                0.052098576 = queryNorm
              0.14422815 = fieldWeight in 2666, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.2635105 = idf(docFreq=4597, maxDocs=44218)
                0.03125 = fieldNorm(doc=2666)
          0.028234553 = weight(_text_:22 in 2666) [ClassicSimilarity], result of:
            0.028234553 = score(doc=2666,freq=2.0), product of:
              0.18244034 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.052098576 = 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)
      0.5 = coord(1/2)
    
    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
  9. Vander Wal, T.: Welcome to the Matrix! (2008) 0.03
    0.026378417 = product of:
      0.052756835 = sum of:
        0.052756835 = sum of:
          0.024522282 = weight(_text_:web in 2881) [ClassicSimilarity], result of:
            0.024522282 = score(doc=2881,freq=2.0), product of:
              0.17002425 = queryWeight, product of:
                3.2635105 = idf(docFreq=4597, maxDocs=44218)
                0.052098576 = queryNorm
              0.14422815 = fieldWeight in 2881, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.2635105 = idf(docFreq=4597, maxDocs=44218)
                0.03125 = fieldNorm(doc=2881)
          0.028234553 = weight(_text_:22 in 2881) [ClassicSimilarity], result of:
            0.028234553 = score(doc=2881,freq=2.0), product of:
              0.18244034 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.052098576 = 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)
      0.5 = coord(1/2)
    
    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. Santini, M.: Zero, single, or multi? : genre of web pages through the users' perspective (2008) 0.02
    0.024233207 = product of:
      0.048466414 = sum of:
        0.048466414 = product of:
          0.09693283 = sum of:
            0.09693283 = weight(_text_:web in 2059) [ClassicSimilarity], result of:
              0.09693283 = score(doc=2059,freq=20.0), product of:
                0.17002425 = queryWeight, product of:
                  3.2635105 = idf(docFreq=4597, maxDocs=44218)
                  0.052098576 = queryNorm
                0.5701118 = fieldWeight in 2059, product of:
                  4.472136 = tf(freq=20.0), with freq of:
                    20.0 = termFreq=20.0
                  3.2635105 = idf(docFreq=4597, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=2059)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    The goal of the study presented in this article is to investigate to what extent the classification of a web page by a single genre matches the users' perspective. The extent of agreement on a single genre label for a web page can help understand whether there is a need for a different classification scheme that overrides the single-genre labelling. My hypothesis is that a single genre label does not account for the users' perspective. In order to test this hypothesis, I submitted a restricted number of web pages (25 web pages) to a large number of web users (135 subjects) asking them to assign only a single genre label to each of the web pages. Users could choose from a list of 21 genre labels, or select one of the two 'escape' options, i.e. 'Add a label' and 'I don't know'. The rationale was to observe the level of agreement on a single genre label per web page, and draw some conclusions about the appropriateness of limiting the assignment to only a single label when doing genre classification of web pages. Results show that users largely disagree on the label to be assigned to a web page.
  11. Heckner, M.: Tagging, rating, posting : studying forms of user contribution for web-based information management and information retrieval (2009) 0.02
    0.024233207 = product of:
      0.048466414 = sum of:
        0.048466414 = product of:
          0.09693283 = sum of:
            0.09693283 = weight(_text_:web in 2931) [ClassicSimilarity], result of:
              0.09693283 = score(doc=2931,freq=20.0), product of:
                0.17002425 = queryWeight, product of:
                  3.2635105 = idf(docFreq=4597, maxDocs=44218)
                  0.052098576 = queryNorm
                0.5701118 = fieldWeight in 2931, product of:
                  4.472136 = tf(freq=20.0), with freq of:
                    20.0 = termFreq=20.0
                  3.2635105 = idf(docFreq=4597, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=2931)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Content
    The Web of User Contribution - Foundations and Principles of the Social Web - Social Tagging - Rating and Filtering of Digital Resources Empirical Analysisof User Contributions - The Functional and Linguistic Structure of Tags - A Comparative Analysis of Tags for Different Digital Resource Types - Exploring Relevance Assessments in Social IR Systems - Exploring User Contribution Within a Higher Education Scenario - Summary of Empirical Results and Implications for Designing Social Information Systems User Contribution for a Participative Information System - Social Information Architecture for an Online Help System
    Object
    Web 2.0
    RSWK
    World Wide Web 2.0 / Benutzer / Online-Publizieren / Information Retrieval / Soziale Software / Hilfesystem
    Social Tagging / Filter / Web log / World Wide Web 2.0
    Subject
    World Wide Web 2.0 / Benutzer / Online-Publizieren / Information Retrieval / Soziale Software / Hilfesystem
    Social Tagging / Filter / Web log / World Wide Web 2.0
  12. Blumauer, A.; Hochmeister, M.: Tag-Recommender gestützte Annotation von Web-Dokumenten (2009) 0.02
    0.023989651 = product of:
      0.047979303 = sum of:
        0.047979303 = product of:
          0.095958605 = sum of:
            0.095958605 = weight(_text_:web in 4866) [ClassicSimilarity], result of:
              0.095958605 = score(doc=4866,freq=10.0), product of:
                0.17002425 = queryWeight, product of:
                  3.2635105 = idf(docFreq=4597, maxDocs=44218)
                  0.052098576 = queryNorm
                0.5643819 = fieldWeight in 4866, product of:
                  3.1622777 = tf(freq=10.0), with freq of:
                    10.0 = termFreq=10.0
                  3.2635105 = idf(docFreq=4597, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=4866)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    In diesem Kapitel wird die zentrale Bedeutung der Annotation von Webdokumenten bzw. von Ressourcen in einem Semantischen Web diskutiert. Es wird auf aktuelle Methoden und Techniken in diesem Gebiet eingegangen, insbesondere wird das Phänomen "Social Tagging" als zentrales Element eines "Social Semantic Webs" beleuchtet. Weiters wird der Frage nachgegangen, welchen Mehrwert "Tag Recommender" beim Annotationsvorgang bieten, sowohl aus Sicht des End-Users aber auch im Sinne eines kollaborativen Ontologieerstellungsprozesses. Schließlich wird ein Funktionsprinzip für einen semi-automatischen Tag-Recommender vorgestellt unter besonderer Berücksichtigung der Anwendbarkeit in einem Corporate Semantic Web.
    Source
    Social Semantic Web: Web 2.0, was nun? Hrsg.: A. Blumauer u. T. Pellegrini
  13. Hsu, M.-H.; Chen, H.-H.: Efficient and effective prediction of social tags to enhance Web search (2011) 0.02
    0.02167484 = product of:
      0.04334968 = sum of:
        0.04334968 = product of:
          0.08669936 = sum of:
            0.08669936 = weight(_text_:web in 4625) [ClassicSimilarity], result of:
              0.08669936 = score(doc=4625,freq=16.0), product of:
                0.17002425 = queryWeight, product of:
                  3.2635105 = idf(docFreq=4597, maxDocs=44218)
                  0.052098576 = queryNorm
                0.5099235 = fieldWeight in 4625, product of:
                  4.0 = tf(freq=16.0), with freq of:
                    16.0 = termFreq=16.0
                  3.2635105 = idf(docFreq=4597, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=4625)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    As the web has grown into an integral part of daily life, social annotation has become a popular manner for web users to manage resources. This method of management has many potential applications, but it is limited in applicability by the cold-start problem, especially for new resources on the web. In this article, we study automatic tag prediction for web pages comprehensively and utilize the predicted tags to improve search performance. First, we explore the stabilizing phenomenon of tag usage in a social bookmarking system. Then, we propose a two-stage tag prediction approach, which is efficient and is effective in making use of early annotations from users. In the first stage, content-based ranking, candidate tags are selected and ranked to generate an initial tag list. In the second stage, random-walk re-ranking, we adopt a random-walk model that utilizes tag co-occurrence information to re-rank the initial list. The experimental results show that our algorithm effectively proposes appropriate tags for target web pages. In addition, we present a framework to incorporate tag prediction in a general web search. The experimental results of the web search validate the hypothesis that the proposed framework significantly enhances the typical retrieval model.
  14. Voß, J.: Vom Social Tagging zum Semantic Tagging (2008) 0.02
    0.021456998 = product of:
      0.042913996 = sum of:
        0.042913996 = product of:
          0.08582799 = sum of:
            0.08582799 = weight(_text_:web in 2884) [ClassicSimilarity], result of:
              0.08582799 = score(doc=2884,freq=8.0), product of:
                0.17002425 = queryWeight, product of:
                  3.2635105 = idf(docFreq=4597, maxDocs=44218)
                  0.052098576 = queryNorm
                0.50479853 = fieldWeight in 2884, product of:
                  2.828427 = tf(freq=8.0), with freq of:
                    8.0 = termFreq=8.0
                  3.2635105 = idf(docFreq=4597, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=2884)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Social Tagging als freie Verschlagwortung durch Nutzer im Web wird immer häufiger mit der Idee des Semantic Web in Zusammenhang gebracht. Wie beide Konzepte in der Praxis konkret zusammenkommen sollen, bleibt jedoch meist unklar. Dieser Artikel soll hier Aufklärung leisten, indem die Kombination von Social Tagging und Semantic Web in Form von Semantic Tagging mit dem Simple Knowledge Organisation System dargestellt und auf die konkreten Möglichkeiten, Vorteile und offenen Fragen der Semantischen Indexierung eingegangen wird.
    Theme
    Semantic Web
  15. Hotho, A.; Jäschke, R.; Benz, D.; Grahl, M.; Krause, B.; Schmitz, C.; Stumme, G.: Social Bookmarking am Beispiel BibSonomy (2009) 0.02
    0.02123692 = product of:
      0.04247384 = sum of:
        0.04247384 = product of:
          0.08494768 = sum of:
            0.08494768 = weight(_text_:web in 4873) [ClassicSimilarity], result of:
              0.08494768 = score(doc=4873,freq=6.0), product of:
                0.17002425 = queryWeight, product of:
                  3.2635105 = idf(docFreq=4597, maxDocs=44218)
                  0.052098576 = queryNorm
                0.49962097 = fieldWeight in 4873, product of:
                  2.4494898 = tf(freq=6.0), with freq of:
                    6.0 = termFreq=6.0
                  3.2635105 = idf(docFreq=4597, maxDocs=44218)
                  0.0625 = fieldNorm(doc=4873)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    BibSonomy ist ein kooperatives Verschlagwortungssystem (Social Bookmarking System), betrieben vom Fachgebiet Wissensverarbeitung der Universität Kassel. Es erlaubt das Speichern und Organisieren von Web-Lesezeichen und Metadaten für wissenschaftliche Publikationen. In diesem Beitrag beschreiben wir die von BibSonomy bereitgestellte Funktionalität, die dahinter stehende Architektur sowie das zugrunde liegende Datenmodell. Ferner erläutern wir Anwendungsbeispiele und gehen auf Methoden zur Analyse der in BibSonomy und ähnlichen Systemen enthaltenen Daten ein.
    Source
    Social Semantic Web: Web 2.0, was nun? Hrsg.: A. Blumauer u. T. Pellegrini
  16. Web-2.0-Dienste als Ergänzung zu algorithmischen Suchmaschinen (2008) 0.02
    0.02056256 = product of:
      0.04112512 = sum of:
        0.04112512 = product of:
          0.08225024 = sum of:
            0.08225024 = weight(_text_:web in 4323) [ClassicSimilarity], result of:
              0.08225024 = score(doc=4323,freq=10.0), product of:
                0.17002425 = queryWeight, product of:
                  3.2635105 = idf(docFreq=4597, maxDocs=44218)
                  0.052098576 = queryNorm
                0.48375595 = fieldWeight in 4323, product of:
                  3.1622777 = tf(freq=10.0), with freq of:
                    10.0 = termFreq=10.0
                  3.2635105 = idf(docFreq=4597, maxDocs=44218)
                  0.046875 = fieldNorm(doc=4323)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Mit sozialen Suchdiensten - wie z. B. Yahoo Clever, Lycos iQ oder Mister Wong - ist eine Ergänzung und teilweise sogar eine Konkurrenz zu den bisherigen Ansätzen in der Web-Suche entstanden. Während Google und Co. automatisch generierte Trefferlisten bieten, binden soziale Suchdienste die Anwender zu Generierung der Suchergebnisse in den Suchprozess ein. Vor diesem Hintergrund wird in diesem Buch der Frage nachgegangen, inwieweit soziale Suchdienste mit traditionellen Suchmaschinen konkurrieren oder diese qualitativ ergänzen können. Der vorliegende Band beleuchtet die hier aufgeworfene Fragestellung aus verschiedenen Perspektiven, um auf die Bedeutung von sozialen Suchdiensten zu schließen.
    Issue
    Ergebnisse des Fachprojektes "Einbindung von Frage-Antwort-Diensten in die Web-Suche" am Department Information der Hochschule für Angewandte Wissenschaften Hamburg (WS 2007/2008).
    RSWK
    World Wide Web 2.0 / Suchmaschine
    Subject
    World Wide Web 2.0 / Suchmaschine
  17. Huang, C.; Fu, T.; Chen, H.: Text-based video content classification for online video-sharing sites (2010) 0.02
    0.020274958 = product of:
      0.040549915 = sum of:
        0.040549915 = product of:
          0.08109983 = sum of:
            0.08109983 = weight(_text_:web in 3452) [ClassicSimilarity], result of:
              0.08109983 = score(doc=3452,freq=14.0), product of:
                0.17002425 = queryWeight, product of:
                  3.2635105 = idf(docFreq=4597, maxDocs=44218)
                  0.052098576 = queryNorm
                0.47698978 = fieldWeight in 3452, product of:
                  3.7416575 = tf(freq=14.0), with freq of:
                    14.0 = termFreq=14.0
                  3.2635105 = idf(docFreq=4597, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=3452)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    With the emergence of Web 2.0, sharing personal content, communicating ideas, and interacting with other online users in Web 2.0 communities have become daily routines for online users. User-generated data from Web 2.0 sites provide rich personal information (e.g., personal preferences and interests) and can be utilized to obtain insight about cyber communities and their social networks. Many studies have focused on leveraging user-generated information to analyze blogs and forums, but few studies have applied this approach to video-sharing Web sites. In this study, we propose a text-based framework for video content classification of online-video sharing Web sites. Different types of user-generated data (e.g., titles, descriptions, and comments) were used as proxies for online videos, and three types of text features (lexical, syntactic, and content-specific features) were extracted. Three feature-based classification techniques (C4.5, Naïve Bayes, and Support Vector Machine) were used to classify videos. To evaluate the proposed framework, user-generated data from candidate videos, which were identified by searching user-given keywords on YouTube, were first collected. Then, a subset of the collected data was randomly selected and manually tagged by users as our experiment data. The experimental results showed that the proposed approach was able to classify online videos based on users' interests with accuracy rates up to 87.2%, and all three types of text features contributed to discriminating videos. Support Vector Machine outperformed C4.5 and Naïve Bayes techniques in our experiments. In addition, our case study further demonstrated that accurate video-classification results are very useful for identifying implicit cyber communities on video-sharing Web sites.
    Object
    Web 2.0
  18. Watters, C.; Nizam, N.: Knowledge organization on the Web : the emergent role of social classification (2012) 0.02
    0.018582305 = product of:
      0.03716461 = sum of:
        0.03716461 = product of:
          0.07432922 = sum of:
            0.07432922 = weight(_text_:web in 828) [ClassicSimilarity], result of:
              0.07432922 = score(doc=828,freq=6.0), product of:
                0.17002425 = queryWeight, product of:
                  3.2635105 = idf(docFreq=4597, maxDocs=44218)
                  0.052098576 = queryNorm
                0.43716836 = fieldWeight in 828, product of:
                  2.4494898 = tf(freq=6.0), with freq of:
                    6.0 = termFreq=6.0
                  3.2635105 = idf(docFreq=4597, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=828)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    There are close to a billion websites on the Internet with approximately 400 million users worldwide [www.internetworldstats.com]. People go to websites for a wide variety of different information tasks, from finding a restaurant to serious research. Many of the difficulties with searching the Web, as it is structured currently, can be attributed to increases to scale. The content of the Web is now so large that we only have a rough estimate of the number of sites and the range of information is extremely diverse, from blogs and photos to research articles and news videos.
  19. Peters, I.: Folksonomies & Social Tagging (2023) 0.02
    0.018582305 = product of:
      0.03716461 = sum of:
        0.03716461 = product of:
          0.07432922 = sum of:
            0.07432922 = weight(_text_:web in 796) [ClassicSimilarity], result of:
              0.07432922 = score(doc=796,freq=6.0), product of:
                0.17002425 = queryWeight, product of:
                  3.2635105 = idf(docFreq=4597, maxDocs=44218)
                  0.052098576 = queryNorm
                0.43716836 = fieldWeight in 796, product of:
                  2.4494898 = tf(freq=6.0), with freq of:
                    6.0 = termFreq=6.0
                  3.2635105 = idf(docFreq=4597, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=796)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Die Erforschung und der Einsatz von Folksonomies und Social Tagging als nutzerzentrierte Formen der Inhaltserschließung und Wissensrepräsentation haben in den 10 Jahren ab ca. 2005 ihren Höhenpunkt erfahren. Motiviert wurde dies durch die Entwicklung und Verbreitung des Social Web und der wachsenden Nutzung von Social-Media-Plattformen (s. Kapitel E 8 Social Media und Social Web). Beides führte zu einem rasanten Anstieg der im oder über das World Wide Web auffindbaren Menge an potenzieller Information und generierte eine große Nachfrage nach skalierbaren Methoden der Inhaltserschließung.
  20. Ding, Y.; Jacob, E.K.; Zhang, Z.; Foo, S.; Yan, E.; George, N.L.; Guo, L.: Perspectives on social tagging (2009) 0.02
    0.01839171 = product of:
      0.03678342 = sum of:
        0.03678342 = product of:
          0.07356684 = sum of:
            0.07356684 = weight(_text_:web in 3290) [ClassicSimilarity], result of:
              0.07356684 = score(doc=3290,freq=8.0), product of:
                0.17002425 = queryWeight, product of:
                  3.2635105 = idf(docFreq=4597, maxDocs=44218)
                  0.052098576 = queryNorm
                0.43268442 = fieldWeight in 3290, product of:
                  2.828427 = tf(freq=8.0), with freq of:
                    8.0 = termFreq=8.0
                  3.2635105 = idf(docFreq=4597, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3290)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Social tagging is one of the major phenomena transforming the World Wide Web from a static platform into an actively shared information space. This paper addresses various aspects of social tagging, including different views on the nature of social tagging, how to make use of social tags, and how to bridge social tagging with other Web functionalities; it discusses the use of facets to facilitate browsing and searching of tagging data; and it presents an analogy between bibliometrics and tagometrics, arguing that established bibliometric methodologies can be applied to analyze tagging behavior on the Web. Based on the Upper Tag Ontology (UTO), a Web crawler was built to harvest tag data from Delicious, Flickr, and YouTube in September 2007. In total, 1.8 million objects, including bookmarks, photos, and videos, 3.1 million taggers, and 12.1 million tags were collected and analyzed. Some tagging patterns and variations are identified and discussed.

Languages

  • e 49
  • d 22
  • i 1
  • More… Less…

Types

  • a 59
  • m 9
  • el 8
  • s 3
  • b 2
  • x 1
  • More… Less…

Classifications