Search (106 results, page 1 of 6)

  • × theme_ss:"Social tagging"
  1. Hotho, A.; Jäschke, R.; Benz, D.; Grahl, M.; Krause, B.; Schmitz, C.; Stumme, G.: Social Bookmarking am Beispiel BibSonomy (2009) 0.01
    0.012662486 = product of:
      0.0886374 = sum of:
        0.021511177 = weight(_text_:system in 4873) [ClassicSimilarity], result of:
          0.021511177 = score(doc=4873,freq=2.0), product of:
            0.07727166 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.02453417 = queryNorm
            0.27838376 = fieldWeight in 4873, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0625 = fieldNorm(doc=4873)
        0.06712622 = product of:
          0.13425244 = sum of:
            0.13425244 = weight(_text_:datenmodell in 4873) [ClassicSimilarity], result of:
              0.13425244 = score(doc=4873,freq=2.0), product of:
                0.19304088 = queryWeight, product of:
                  7.8682456 = idf(docFreq=45, maxDocs=44218)
                  0.02453417 = queryNorm
                0.6954612 = fieldWeight in 4873, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  7.8682456 = idf(docFreq=45, maxDocs=44218)
                  0.0625 = fieldNorm(doc=4873)
          0.5 = coord(1/2)
      0.14285715 = coord(2/14)
    
    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.
  2. Heckner, M.: Tagging, rating, posting : studying forms of user contribution for web-based information management and information retrieval (2009) 0.01
    0.011920691 = product of:
      0.055629887 = sum of:
        0.019013375 = weight(_text_:system in 2931) [ClassicSimilarity], result of:
          0.019013375 = score(doc=2931,freq=4.0), product of:
            0.07727166 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.02453417 = queryNorm
            0.24605882 = fieldWeight in 2931, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2931)
        0.011813596 = weight(_text_:information in 2931) [ClassicSimilarity], result of:
          0.011813596 = score(doc=2931,freq=16.0), product of:
            0.04306919 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.02453417 = queryNorm
            0.27429342 = fieldWeight in 2931, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2931)
        0.024802918 = weight(_text_:retrieval in 2931) [ClassicSimilarity], result of:
          0.024802918 = score(doc=2931,freq=8.0), product of:
            0.07421378 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.02453417 = queryNorm
            0.33420905 = fieldWeight in 2931, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2931)
      0.21428572 = coord(3/14)
    
    Abstract
    Die Entstehung von Social Software ermöglicht es Nutzern, in großem Umfang im Netz zu publizieren. Bisher liegen aber nur wenige empirische Befunde zu funktionalen Eigenschaften sowie Qualitätsaspekten von Nutzerbeiträgen im Kontext von Informationsmanagement und Information Retrieval vor. Diese Arbeit diskutiert grundlegende Partizipationsformen, präsentiert empirische Studien über Social Tagging, Blogbeiträge sowie Relevanzbeurteilungen und entwickelt Design und Implementierung einer "sozialen" Informationsarchitektur für ein partizipatives Onlinehilfesystem.
    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
    RSWK
    World Wide Web 2.0 / Benutzer / Online-Publizieren / Information Retrieval / Soziale Software / Hilfesystem
    Subject
    World Wide Web 2.0 / Benutzer / Online-Publizieren / Information Retrieval / Soziale Software / Hilfesystem
  3. Naderi, H.; Rumpler, B.: PERCIRS: a system to combine personalized and collaborative information retrieval (2010) 0.01
    0.010765608 = product of:
      0.050239503 = sum of:
        0.030421399 = weight(_text_:system in 3960) [ClassicSimilarity], result of:
          0.030421399 = score(doc=3960,freq=16.0), product of:
            0.07727166 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.02453417 = queryNorm
            0.3936941 = fieldWeight in 3960, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03125 = fieldNorm(doc=3960)
        0.005787457 = weight(_text_:information in 3960) [ClassicSimilarity], result of:
          0.005787457 = score(doc=3960,freq=6.0), product of:
            0.04306919 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.02453417 = queryNorm
            0.1343758 = fieldWeight in 3960, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.03125 = fieldNorm(doc=3960)
        0.014030648 = weight(_text_:retrieval in 3960) [ClassicSimilarity], result of:
          0.014030648 = score(doc=3960,freq=4.0), product of:
            0.07421378 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.02453417 = queryNorm
            0.18905719 = fieldWeight in 3960, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03125 = fieldNorm(doc=3960)
      0.21428572 = coord(3/14)
    
    Abstract
    Purpose - This paper aims to discuss and test the claim that utilization of the personalization techniques can be valuable to improve the efficiency of collaborative information retrieval (CIR) systems. Design/methodology/approach - A new personalized CIR system, called PERCIRS, is presented based on the user profile similarity calculation (UPSC) formulas. To this aim, the paper proposes several UPSC formulas as well as two techniques to evaluate them. As the proposed CIR system is personalized, it could not be evaluated by Cranfield, like evaluation techniques (e.g. TREC). Hence, this paper proposes a new user-centric mechanism, which enables PERCIRS to be evaluated. This mechanism is generic and can be used to evaluate any other personalized IR system. Findings - The results show that among the proposed UPSC formulas in this paper, the (query-document)-graph based formula is the most effective. After integrating this formula into PERCIRS and comparing it with nine other IR systems, it is concluded that the results of the system are better than the other IR systems. In addition, the paper shows that the complexity of the system is less that the complexity of the other CIR systems. Research limitations/implications - This system asks the users to explicitly rank the returned documents, while explicit ranking is still not widespread enough. However it believes that the users should actively participate in the IR process in order to aptly satisfy their needs to information. Originality/value - The value of this paper lies in combining collaborative and personalized IR, as well as introducing a mechanism which enables the personalized IR system to be evaluated. The proposed evaluation mechanism is very valuable for developers of personalized IR systems. The paper also introduces some significant user profile similarity calculation formulas, and two techniques to evaluate them. These formulas can also be used to find the user's community in the social networks.
  4. Vaidya, P.; Harinarayana, N.S.: ¬The comparative and analytical study of LibraryThing tags with Library of Congress Subject Headings (2016) 0.01
    0.009485896 = product of:
      0.044267513 = sum of:
        0.016133383 = weight(_text_:system in 2492) [ClassicSimilarity], result of:
          0.016133383 = score(doc=2492,freq=2.0), product of:
            0.07727166 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.02453417 = queryNorm
            0.20878783 = fieldWeight in 2492, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.046875 = fieldNorm(doc=2492)
        0.0070881573 = weight(_text_:information in 2492) [ClassicSimilarity], result of:
          0.0070881573 = score(doc=2492,freq=4.0), product of:
            0.04306919 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.02453417 = queryNorm
            0.16457605 = fieldWeight in 2492, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=2492)
        0.021045974 = weight(_text_:retrieval in 2492) [ClassicSimilarity], result of:
          0.021045974 = score(doc=2492,freq=4.0), product of:
            0.07421378 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.02453417 = queryNorm
            0.2835858 = fieldWeight in 2492, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=2492)
      0.21428572 = coord(3/14)
    
    Abstract
    The internet in its Web 2.0 version has given an opportunity among users to be participative and the chance to enhance the existing system, which makes it dynamic and collaborative. The activity of social tagging among researchers to organize the digital resources is an interesting study among information professionals. The one way of organizing the resources for future retrieval through these user-generated terms makes an interesting analysis by comparing them with professionally created controlled vocabularies. Here in this study, an attempt has been made to compare Library of Congress Subject Headings (LCSH) terms with LibraryThing social tags. In this comparative analysis, the results show that social tags can be used to enhance the metadata for information retrieval. But still, the uncontrolled nature of social tags is a concern and creates uncertainty among researchers.
  5. Golub, K.; Lykke, M.; Tudhope, D.: Enhancing social tagging with automated keywords from the Dewey Decimal Classification (2014) 0.01
    0.008378824 = product of:
      0.03910118 = sum of:
        0.013444485 = weight(_text_:system in 2918) [ClassicSimilarity], result of:
          0.013444485 = score(doc=2918,freq=2.0), product of:
            0.07727166 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.02453417 = queryNorm
            0.17398985 = fieldWeight in 2918, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2918)
        0.004176737 = weight(_text_:information in 2918) [ClassicSimilarity], result of:
          0.004176737 = score(doc=2918,freq=2.0), product of:
            0.04306919 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.02453417 = queryNorm
            0.09697737 = fieldWeight in 2918, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2918)
        0.021479957 = weight(_text_:retrieval in 2918) [ClassicSimilarity], result of:
          0.021479957 = score(doc=2918,freq=6.0), product of:
            0.07421378 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.02453417 = queryNorm
            0.28943354 = fieldWeight in 2918, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2918)
      0.21428572 = coord(3/14)
    
    Abstract
    Purpose - The purpose of this paper is to explore the potential of applying the Dewey Decimal Classification (DDC) as an established knowledge organization system (KOS) for enhancing social tagging, with the ultimate purpose of improving subject indexing and information retrieval. Design/methodology/approach - Over 11.000 Intute metadata records in politics were used. Totally, 28 politics students were each given four tasks, in which a total of 60 resources were tagged in two different configurations, one with uncontrolled social tags only and another with uncontrolled social tags as well as suggestions from a controlled vocabulary. The controlled vocabulary was DDC comprising also mappings from the Library of Congress Subject Headings. Findings - The results demonstrate the importance of controlled vocabulary suggestions for indexing and retrieval: to help produce ideas of which tags to use, to make it easier to find focus for the tagging, to ensure consistency and to increase the number of access points in retrieval. The value and usefulness of the suggestions proved to be dependent on the quality of the suggestions, both as to conceptual relevance to the user and as to appropriateness of the terminology. Originality/value - No research has investigated the enhancement of social tagging with suggestions from the DDC, an established KOS, in a user trial, comparing social tagging only and social tagging enhanced with the suggestions. This paper is a final reflection on all aspects of the study.
  6. Matthews, B.; Jones, C.; Puzon, B.; Moon, J.; Tudhope, D.; Golub, K.; Nielsen, M.L.: ¬An evaluation of enhancing social tagging with a knowledge organization system (2010) 0.01
    0.007997492 = product of:
      0.03732163 = sum of:
        0.019013375 = weight(_text_:system in 4171) [ClassicSimilarity], result of:
          0.019013375 = score(doc=4171,freq=4.0), product of:
            0.07727166 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.02453417 = queryNorm
            0.24605882 = fieldWeight in 4171, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4171)
        0.005906798 = weight(_text_:information in 4171) [ClassicSimilarity], result of:
          0.005906798 = score(doc=4171,freq=4.0), product of:
            0.04306919 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.02453417 = queryNorm
            0.13714671 = fieldWeight in 4171, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4171)
        0.012401459 = weight(_text_:retrieval in 4171) [ClassicSimilarity], result of:
          0.012401459 = score(doc=4171,freq=2.0), product of:
            0.07421378 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.02453417 = queryNorm
            0.16710453 = fieldWeight in 4171, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4171)
      0.21428572 = coord(3/14)
    
    Abstract
    Purpose - Traditional subject indexing and classification are considered infeasible in many digital collections. This paper seeks to investigate ways of enhancing social tagging via knowledge organization systems, with a view to improving the quality of tags for increased information discovery and retrieval performance. Design/methodology/approach - Enhanced tagging interfaces were developed for exemplar online repositories, and trials were undertaken with author and reader groups to evaluate the effectiveness of tagging augmented with control vocabulary for subject indexing of papers in online repositories. Findings - The results showed that using a knowledge organisation system to augment tagging does appear to increase the effectiveness of non-specialist users (that is, without information science training) in subject indexing. Research limitations/implications - While limited by the size and scope of the trials undertaken, these results do point to the usefulness of a mixed approach in supporting the subject indexing of online resources. Originality/value - The value of this work is as a guide to future developments in the practical support for resource indexing in online repositories.
  7. Furner, J.: User tagging of library resources : toward a framework for system evaluation (2007) 0.01
    0.0077201175 = product of:
      0.036027215 = sum of:
        0.016133383 = weight(_text_:system in 703) [ClassicSimilarity], result of:
          0.016133383 = score(doc=703,freq=2.0), product of:
            0.07727166 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.02453417 = queryNorm
            0.20878783 = fieldWeight in 703, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.046875 = fieldNorm(doc=703)
        0.0050120843 = weight(_text_:information in 703) [ClassicSimilarity], result of:
          0.0050120843 = score(doc=703,freq=2.0), product of:
            0.04306919 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.02453417 = queryNorm
            0.116372846 = fieldWeight in 703, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=703)
        0.014881751 = weight(_text_:retrieval in 703) [ClassicSimilarity], result of:
          0.014881751 = score(doc=703,freq=2.0), product of:
            0.07421378 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.02453417 = queryNorm
            0.20052543 = fieldWeight in 703, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=703)
      0.21428572 = coord(3/14)
    
    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.
    Content
    Vortrag anlässlich: WORLD LIBRARY AND INFORMATION CONGRESS: 73RD IFLA GENERAL CONFERENCE AND COUNCIL 19-23 August 2007, Durban, South Africa. - 157 - Classification and Indexing
  8. Rafferty, P.; Hidderley, R.: Flickr and democratic Indexing : dialogic approaches to indexing (2007) 0.01
    0.0077201175 = product of:
      0.036027215 = sum of:
        0.016133383 = weight(_text_:system in 752) [ClassicSimilarity], result of:
          0.016133383 = score(doc=752,freq=2.0), product of:
            0.07727166 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.02453417 = queryNorm
            0.20878783 = fieldWeight in 752, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.046875 = fieldNorm(doc=752)
        0.0050120843 = weight(_text_:information in 752) [ClassicSimilarity], result of:
          0.0050120843 = score(doc=752,freq=2.0), product of:
            0.04306919 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.02453417 = queryNorm
            0.116372846 = fieldWeight in 752, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=752)
        0.014881751 = weight(_text_:retrieval in 752) [ClassicSimilarity], result of:
          0.014881751 = score(doc=752,freq=2.0), product of:
            0.07421378 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.02453417 = queryNorm
            0.20052543 = fieldWeight in 752, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=752)
      0.21428572 = coord(3/14)
    
    Abstract
    Purpose - The purpose of this paper is two-fold: to examine three models of subject indexing (i.e. expert-led indexing, author-generated indexing, and user-orientated indexing); and to compare and contrast two user-orientated indexing approaches (i.e. the theoretically-based Democratic Indexing project, and Flickr, a working system for describing photographs). Design/methodology/approach - The approach to examining Flickr and Democratic Indexing is evaluative. The limitations of Flickr are described and examples are provided. The Democratic Indexing approach, which the authors believe offers a method of marshalling a "free" user-indexed archive to provide useful retrieval functions, is described. Findings - The examination of both Flickr and the Democratic Indexing approach suggests that, despite Shirky's claim of philosophical paradigm shifting for social tagging, there is a residing doubt amongst information professionals that self-organising systems can work without there being some element of control and some form of "representative authority". Originality/value - This paper contributes to the literature of user-based indexing and social tagging.
  9. Hsu, M.-H.; Chen, H.-H.: Efficient and effective prediction of social tags to enhance Web search (2011) 0.01
    0.0068041594 = product of:
      0.031752743 = sum of:
        0.013444485 = weight(_text_:system in 4625) [ClassicSimilarity], result of:
          0.013444485 = score(doc=4625,freq=2.0), product of:
            0.07727166 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.02453417 = queryNorm
            0.17398985 = fieldWeight in 4625, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4625)
        0.005906798 = weight(_text_:information in 4625) [ClassicSimilarity], result of:
          0.005906798 = score(doc=4625,freq=4.0), product of:
            0.04306919 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.02453417 = queryNorm
            0.13714671 = fieldWeight in 4625, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4625)
        0.012401459 = weight(_text_:retrieval in 4625) [ClassicSimilarity], result of:
          0.012401459 = score(doc=4625,freq=2.0), product of:
            0.07421378 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.02453417 = queryNorm
            0.16710453 = fieldWeight in 4625, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4625)
      0.21428572 = coord(3/14)
    
    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.
    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.8, S.1473-1487
  10. Chen, M.; Liu, X.; Qin, J.: Semantic relation extraction from socially-generated tags : a methodology for metadata generation (2008) 0.01
    0.0062282225 = product of:
      0.029065037 = sum of:
        0.008353474 = weight(_text_:information in 2648) [ClassicSimilarity], result of:
          0.008353474 = score(doc=2648,freq=8.0), product of:
            0.04306919 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.02453417 = queryNorm
            0.19395474 = fieldWeight in 2648, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2648)
        0.012401459 = weight(_text_:retrieval in 2648) [ClassicSimilarity], result of:
          0.012401459 = score(doc=2648,freq=2.0), product of:
            0.07421378 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.02453417 = queryNorm
            0.16710453 = fieldWeight in 2648, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2648)
        0.008310104 = product of:
          0.016620208 = sum of:
            0.016620208 = weight(_text_:22 in 2648) [ClassicSimilarity], result of:
              0.016620208 = score(doc=2648,freq=2.0), product of:
                0.085914485 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.02453417 = 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)
          0.5 = coord(1/2)
      0.21428572 = coord(3/14)
    
    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
  11. Heckner, M.; Mühlbacher, S.; Wolff, C.: Tagging tagging : a classification model for user keywords in scientific bibliography management systems (2007) 0.01
    0.0060273483 = product of:
      0.028127626 = sum of:
        0.010755588 = weight(_text_:system in 533) [ClassicSimilarity], result of:
          0.010755588 = score(doc=533,freq=2.0), product of:
            0.07727166 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.02453417 = queryNorm
            0.13919188 = fieldWeight in 533, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03125 = fieldNorm(doc=533)
        0.0033413896 = weight(_text_:information in 533) [ClassicSimilarity], result of:
          0.0033413896 = score(doc=533,freq=2.0), product of:
            0.04306919 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.02453417 = queryNorm
            0.0775819 = fieldWeight in 533, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.03125 = fieldNorm(doc=533)
        0.014030648 = weight(_text_:retrieval in 533) [ClassicSimilarity], result of:
          0.014030648 = score(doc=533,freq=4.0), product of:
            0.07421378 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.02453417 = queryNorm
            0.18905719 = fieldWeight in 533, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03125 = fieldNorm(doc=533)
      0.21428572 = coord(3/14)
    
    Abstract
    Recently, a growing amount of systems that allow personal content annotation (tagging) are being created, ranging from personal sites for organising bookmarks (del.icio.us), photos (flickr.com) or videos (video.google.com, youtube.com) to systems for managing bibliographies for scientific research projects (citeulike.org, connotea.org). Simultaneously, a debate on the pro and cons of allowing users to add personal keywords to digital content has arisen. One recurrent point-of-discussion is whether tagging can solve the well-known vocabulary problem: In order to support successful retrieval in complex environments, it is necessary to index an object with a variety of aliases (cf. Furnas 1987). In this spirit, social tagging enhances the pool of rigid, traditional keywording by adding user-created retrieval vocabularies. Furthermore, tagging goes beyond simple personal content-based keywords by providing meta-keywords like funny or interesting that "identify qualities or characteristics" (Golder and Huberman 2006, Kipp and Campbell 2006, Kipp 2007, Feinberg 2006, Kroski 2005). Contrarily, tagging systems are claimed to lead to semantic difficulties that may hinder the precision and recall of tagging systems (e.g. the polysemy problem, cf. Marlow 2006, Lakoff 2005, Golder and Huberman 2006). Empirical research on social tagging is still rare and mostly from a computer linguistics or librarian point-of-view (Voß 2007) which focus either on the automatic statistical analyses of large data sets, or intellectually inspect single cases of tag usage: Some scientists studied the evolution of tag vocabularies and tag distribution in specific systems (Golder and Huberman 2006, Hammond 2005). Others concentrate on tagging behaviour and tagger characteristics in collaborative systems. (Hammond 2005, Kipp and Campbell 2007, Feinberg 2006, Sen 2006). However, little research has been conducted on the functional and linguistic characteristics of tags.1 An analysis of these patterns could show differences between user wording and conventional keywording. In order to provide a reasonable basis for comparison, a classification system for existing tags is needed.
    Therefore our main research questions are as follows: - Is it possible to discover regular patterns in tag usage and to establish a stable category model? - Does a specific tagging language comparable to internet slang or chatspeak evolve? - How do social tags differ from traditional (author / expert) keywords? - To what degree are social tags taken from or findable in the full text of the tagged resource? - Do tags in a research literature context go beyond simple content description (e.g. tags indicating time or task-related information, cf. Kipp et al. 2006)?
  12. Qin, C.; Liu, Y.; Mou, J.; Chen, J.: User adoption of a hybrid social tagging approach in an online knowledge community (2019) 0.01
    0.0059274402 = product of:
      0.027661387 = sum of:
        0.013444485 = weight(_text_:system in 5492) [ClassicSimilarity], result of:
          0.013444485 = score(doc=5492,freq=2.0), product of:
            0.07727166 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.02453417 = queryNorm
            0.17398985 = fieldWeight in 5492, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5492)
        0.005906798 = weight(_text_:information in 5492) [ClassicSimilarity], result of:
          0.005906798 = score(doc=5492,freq=4.0), product of:
            0.04306919 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.02453417 = queryNorm
            0.13714671 = fieldWeight in 5492, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5492)
        0.008310104 = product of:
          0.016620208 = sum of:
            0.016620208 = weight(_text_:22 in 5492) [ClassicSimilarity], result of:
              0.016620208 = score(doc=5492,freq=2.0), product of:
                0.085914485 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.02453417 = 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)
          0.5 = coord(1/2)
      0.21428572 = coord(3/14)
    
    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
  13. Yi, K.: Harnessing collective intelligence in social tagging using Delicious (2012) 0.01
    0.0057039345 = product of:
      0.02661836 = sum of:
        0.005906798 = weight(_text_:information in 515) [ClassicSimilarity], result of:
          0.005906798 = score(doc=515,freq=4.0), product of:
            0.04306919 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.02453417 = queryNorm
            0.13714671 = fieldWeight in 515, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=515)
        0.012401459 = weight(_text_:retrieval in 515) [ClassicSimilarity], result of:
          0.012401459 = score(doc=515,freq=2.0), product of:
            0.07421378 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.02453417 = queryNorm
            0.16710453 = fieldWeight in 515, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=515)
        0.008310104 = product of:
          0.016620208 = sum of:
            0.016620208 = weight(_text_:22 in 515) [ClassicSimilarity], result of:
              0.016620208 = score(doc=515,freq=2.0), product of:
                0.085914485 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.02453417 = 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)
      0.21428572 = coord(3/14)
    
    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
  14. Peters, I.; Schumann, L.; Terliesner, J.: Folksonomy-basiertes Information Retrieval unter der Lupe (2012) 0.01
    0.005477351 = product of:
      0.038341455 = sum of:
        0.008269517 = weight(_text_:information in 406) [ClassicSimilarity], result of:
          0.008269517 = score(doc=406,freq=4.0), product of:
            0.04306919 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.02453417 = queryNorm
            0.1920054 = fieldWeight in 406, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0546875 = fieldNorm(doc=406)
        0.03007194 = weight(_text_:retrieval in 406) [ClassicSimilarity], result of:
          0.03007194 = score(doc=406,freq=6.0), product of:
            0.07421378 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.02453417 = queryNorm
            0.40520695 = fieldWeight in 406, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0546875 = fieldNorm(doc=406)
      0.14285715 = coord(2/14)
    
    Abstract
    Social Tagging ist eine weitverbreitete Methode, um nutzergenerierte Inhalte in Webdiensten zu indexieren. Dieser Artikel fasst die aktuelle Forschung zu Folksonomies und Effektivität von Tags in Retrievalsystemen zusammen. Es wurde ein TREC-ähnlicher Retrievaltest mit Tags und Ressourcen aus dem Social Bookmarking-Dienst delicious durchgeführt, welcher in Recall- und Precisionwerten für ausschließlich Tag-basierte Suchen resultierte. Außerdem wurden Tags in verschiedenen Stufen bereinigt und auf ihre Retrieval-Effektivität getestet. Testergebnisse zeigen, dass Retrieval in Folksonomies am besten mit kurzen Anfragen funktioniert. Hierbei sind die Recallwerte hoch, die Precisionwerte jedoch eher niedrig. Die Suchfunktion "power tags only" liefert verbesserte Precisionwerte.
    Source
    Information - Wissenschaft und Praxis. 63(2012) H.4, S.273-280
  15. Raban, D.R.; Ronen, I.; Guy, I.: Acting or reacting? : Preferential attachment in a people-tagging system (2011) 0.01
    0.00532555 = product of:
      0.03727885 = sum of:
        0.032266766 = weight(_text_:system in 4371) [ClassicSimilarity], result of:
          0.032266766 = score(doc=4371,freq=8.0), product of:
            0.07727166 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.02453417 = queryNorm
            0.41757566 = fieldWeight in 4371, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.046875 = fieldNorm(doc=4371)
        0.0050120843 = weight(_text_:information in 4371) [ClassicSimilarity], result of:
          0.0050120843 = score(doc=4371,freq=2.0), product of:
            0.04306919 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.02453417 = queryNorm
            0.116372846 = fieldWeight in 4371, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=4371)
      0.14285715 = coord(2/14)
    
    Abstract
    Social technologies tend to attract research on social structure or interaction. In this paper we analyze the individual use of a social technology, specifically an enterprise people-tagging application. We focus on active participants of the system and distinguish between users who initiate activity and those who respond to activity. This distinction is situated within the preferential attachment theory in order to examine which type of participant contributes more to the process of tagging. We analyze the usage of the people-tagging application in a snapshot representing 3 years of activity, focusing on self-tagging compared to tagging by and of others. The main findings are: (1) People who tag themselves are the most productive contributors to the system. (2) Preferential attachment saturation is reached at 12-14 tags per user. (3) The nature of participation is more significant than the number of participants for system growth. The paper concludes with theoretical and practical implications.
    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.4, S.738-747
  16. Peters, I.: Folksonomies : indexing and retrieval in Web 2.0 (2009) 0.00
    0.0048218104 = product of:
      0.033752672 = sum of:
        0.009450877 = weight(_text_:information in 4203) [ClassicSimilarity], result of:
          0.009450877 = score(doc=4203,freq=16.0), product of:
            0.04306919 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.02453417 = queryNorm
            0.21943474 = fieldWeight in 4203, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.03125 = fieldNorm(doc=4203)
        0.024301795 = weight(_text_:retrieval in 4203) [ClassicSimilarity], result of:
          0.024301795 = score(doc=4203,freq=12.0), product of:
            0.07421378 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.02453417 = queryNorm
            0.32745665 = fieldWeight in 4203, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03125 = fieldNorm(doc=4203)
      0.14285715 = coord(2/14)
    
    Abstract
    Kollaborative Informationsdienste im Web 2.0 werden von den Internetnutzern nicht nur dazu genutzt, digitale Informationsressourcen zu produzieren, sondern auch, um sie inhaltlich mit eigenen Schlagworten, sog. Tags, zu erschließen. Dabei müssen die Nutzer nicht wie bei Bibliothekskatalogen auf Regeln achten. Die Menge an nutzergenerierten Tags innerhalb eines Kollaborativen Informationsdienstes wird als Folksonomy bezeichnet. Die Folksonomies dienen den Nutzern zum Wiederauffinden eigener Ressourcen und für die Recherche nach fremden Ressourcen. Das Buch beschäftigt sich mit Kollaborativen Informationsdiensten, Folksonomies als Methode der Wissensrepräsentation und als Werkzeug des Information Retrievals.
    Footnote
    Zugl.: Düsseldorf, Univ., Diss., 2009 u.d.T.: Peters, Isabella: Folksonomies in Wissensrepräsentation und Information Retrieval Rez. in: IWP - Information Wissenschaft & Praxis, 61(2010) Heft 8, S.469-470 (U. Spree): "... Nachdem sich die Rezensentin durch 418 Seiten Text hindurch gelesen hat, bleibt sie unentschieden, wie der auffällige Einsatz langer Zitate (im Durchschnitt drei Zitate, die länger als vier kleingedruckte Zeilen sind, pro Seite) zu bewerten ist, zumal die Zitate nicht selten rein illustrativen Charakter haben bzw. Isabella Peters noch einmal zitiert, was sie bereits in eigenen Worten ausgedrückt hat. Redundanz und Verlängerung der Lesezeit halten sich hier die Waage mit der Möglichkeit, dass sich die Leserin einen unmittelbaren Eindruck von Sprache und Duktus der zitierten Literatur verschaffen kann. Eindeutig unschön ist das Beenden eines Gedankens oder einer Argumentation durch ein Zitat (z. B. S. 170). Im deutschen Original entstehen auf diese Weise die für deutsche wissenschaftliche Qualifikationsarbeiten typischen denglischen Texte. Für alle, die sich für Wissensrepräsentation, Information Retrieval und kollaborative Informationsdienste interessieren, ist "Folksonomies : Indexing and Retrieval in Web 2.0" trotz der angeführten kleinen Mängel zur Lektüre und Anschaffung - wegen seines beinahe enzyklopädischen Charakters auch als Nachschlage- oder Referenzwerk geeignet - unbedingt zu empfehlen. Abschließend möchte ich mich in einem Punkt der Produktinfo von de Gruyter uneingeschränkt anschließen: ein "Grundlagenwerk für Folksonomies".
    RSWK
    Information Retrieval
    Series
    Knowledge and information : studies in information science
    Subject
    Information Retrieval
  17. Huang, S.-L.; Lin, S.-C.; Chan, Y.-C.: Investigating effectiveness and user acceptance of semantic social tagging for knowledge sharing (2012) 0.00
    0.0047079893 = product of:
      0.032955922 = sum of:
        0.027943838 = weight(_text_:system in 2732) [ClassicSimilarity], result of:
          0.027943838 = score(doc=2732,freq=6.0), product of:
            0.07727166 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.02453417 = queryNorm
            0.36163113 = fieldWeight in 2732, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.046875 = fieldNorm(doc=2732)
        0.0050120843 = weight(_text_:information in 2732) [ClassicSimilarity], result of:
          0.0050120843 = score(doc=2732,freq=2.0), product of:
            0.04306919 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.02453417 = queryNorm
            0.116372846 = fieldWeight in 2732, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=2732)
      0.14285715 = coord(2/14)
    
    Abstract
    Social tagging systems enable users to assign arbitrary tags to various digital resources. However, they face vague-meaning problems when users retrieve or present resources with the keyword-based tags. In order to solve these problems, this study takes advantage of Semantic Web technology and the topological characteristics of knowledge maps to develop a system that comprises a semantic tagging mechanism and triple-pattern and visual searching mechanisms. A field experiment was conducted to evaluate the effectiveness and user acceptance of these mechanisms in a knowledge sharing context. The results show that the semantic social tagging system is more effective than a keyword-based system. The visualized knowledge map helps users capture an overview of the knowledge domain, reduce cognitive effort for the search, and obtain more enjoyment. Traditional keyword tagging with a keyword search still has the advantage of ease of use and the users had higher intention to use it. This study also proposes directions for future development of semantic social tagging systems.
    Source
    Information processing and management. 48(2012) no.4, S.599-617
  18. Bentley, C.M.; Labelle, P.R.: ¬A comparison of social tagging designs and user participation (2008) 0.00
    0.0045631477 = product of:
      0.021294689 = sum of:
        0.0047254385 = weight(_text_:information in 2657) [ClassicSimilarity], result of:
          0.0047254385 = score(doc=2657,freq=4.0), product of:
            0.04306919 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.02453417 = queryNorm
            0.10971737 = fieldWeight in 2657, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.03125 = fieldNorm(doc=2657)
        0.009921167 = weight(_text_:retrieval in 2657) [ClassicSimilarity], result of:
          0.009921167 = score(doc=2657,freq=2.0), product of:
            0.07421378 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.02453417 = queryNorm
            0.13368362 = fieldWeight in 2657, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03125 = fieldNorm(doc=2657)
        0.0066480828 = product of:
          0.0132961655 = sum of:
            0.0132961655 = weight(_text_:22 in 2657) [ClassicSimilarity], result of:
              0.0132961655 = score(doc=2657,freq=2.0), product of:
                0.085914485 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.02453417 = 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)
      0.21428572 = coord(3/14)
    
    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
  19. DeZelar-Tiedman, V.: Doing the LibraryThing(TM) in an academic library catalog (2008) 0.00
    0.0044453703 = product of:
      0.020745061 = sum of:
        0.010755588 = weight(_text_:system in 2666) [ClassicSimilarity], result of:
          0.010755588 = score(doc=2666,freq=2.0), product of:
            0.07727166 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.02453417 = queryNorm
            0.13919188 = fieldWeight in 2666, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03125 = fieldNorm(doc=2666)
        0.0033413896 = weight(_text_:information in 2666) [ClassicSimilarity], result of:
          0.0033413896 = score(doc=2666,freq=2.0), product of:
            0.04306919 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.02453417 = queryNorm
            0.0775819 = fieldWeight in 2666, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.03125 = fieldNorm(doc=2666)
        0.0066480828 = product of:
          0.0132961655 = sum of:
            0.0132961655 = weight(_text_:22 in 2666) [ClassicSimilarity], result of:
              0.0132961655 = score(doc=2666,freq=2.0), product of:
                0.085914485 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.02453417 = 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)
      0.21428572 = coord(3/14)
    
    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
  20. Lewen, H.: Personalisierte Ordnung von Objekten basierend auf Vertrauensnetzwerken (2008) 0.00
    0.0040277084 = product of:
      0.028193956 = sum of:
        0.021511177 = weight(_text_:system in 2305) [ClassicSimilarity], result of:
          0.021511177 = score(doc=2305,freq=2.0), product of:
            0.07727166 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.02453417 = queryNorm
            0.27838376 = fieldWeight in 2305, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0625 = fieldNorm(doc=2305)
        0.006682779 = weight(_text_:information in 2305) [ClassicSimilarity], result of:
          0.006682779 = score(doc=2305,freq=2.0), product of:
            0.04306919 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.02453417 = queryNorm
            0.1551638 = fieldWeight in 2305, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0625 = fieldNorm(doc=2305)
      0.14285715 = coord(2/14)
    
    Abstract
    Open Rating Systeme werden zur Be­wertung unterschiedlichster Objekte eingesetzt. Benutzer können Rezensionen über Objekte verfassen, andere Benutzer können die Qualität dieser Rezensionen bewerten. Basierend auf diesen Bewertungen der Rezensionen wird ein Vertrauensnetzwerk (Web of Trust) aufgebaut. Zwei Benutzer werden durch eine gerichtete Kante verbunden, wenn ein Benutzer dem System mitteilt, dass er einem anderen Benutzer vertraut, Inhalte korrekt zu bewerten. Basierend auf diesem persönlichen Vertrauensnetzwerk werden Objekte und auch die Rezensionen für ein bestimmtes Objekt individuell für jeden Benutzer angeordnet.
    Source
    Information - Wissenschaft und Praxis. 59(2008) H.5, S.297-300

Languages

  • e 84
  • d 22

Types

  • a 92
  • el 10
  • m 7
  • s 3
  • b 2
  • x 1
  • More… Less…