Search (122 results, page 3 of 7)

  • × theme_ss:"Social tagging"
  1. Yi, K.: ¬A semantic similarity approach to predicting Library of Congress subject headings for social tags (2010) 0.00 
    0.0039051762 = product of:
  0.027336232 = sum of:
    0.012357258 = weight(_text_:information in 3707) [ClassicSimilarity], result of:
      0.012357258 = score(doc=3707,freq=12.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.23754507 = fieldWeight in 3707, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3707)
    0.014978974 = weight(_text_:retrieval in 3707) [ClassicSimilarity], result of:
      0.014978974 = score(doc=3707,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.16710453 = fieldWeight in 3707, 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=3707)
  0.14285715 = coord(2/14)
    Abstract
    Social tagging or collaborative tagging has become a new trend in the organization, management, and discovery of digital information. The rapid growth of shared information mostly controlled by social tags poses a new challenge for social tag-based information organization and retrieval. A plausible approach for this challenge is linking social tags to a controlled vocabulary. As an introductory step for this approach, this study investigates ways of predicting relevant subject headings for resources from social tags assigned to the resources. The prediction of subject headings was measured by five different similarity measures: tf-idf, cosine-based similarity (CoS), Jaccard similarity (or Jaccard coefficient; JS), Mutual information (MI), and information radius (IRad). Their results were compared to those by professionals. The results show that a CoS measure based on top five social tags was most effective. Inclusions of more social tags only aggravate the performance. The performance of JS is comparable to the performance of CoS while tf-idf is comparable with up to 70% less than the best performance. MI and IRad have inferior performance compared to the other methods. This study demonstrates the application of the similarity measuring techniques to the prediction of correct Library of Congress subject headings.
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.8, S.1658-1672
  2. Blumauer, A.; Hochmeister, M.: Tag-Recommender gestützte Annotation von Web-Dokumenten (2009) 0.00 
    0.003898619 = product of:
  0.054580662 = sum of:
    0.054580662 = weight(_text_:web in 4866) [ClassicSimilarity], result of:
      0.054580662 = score(doc=4866,freq=10.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = 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)
  0.071428575 = coord(1/14)
    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
  3. Estellés Arolas, E.; González Ladrón-de-Guevar, F.: Uses of explicit and implicit tags in social bookmarking (2012) 0.00 
    0.0038537113 = product of:
  0.026975978 = sum of:
    0.020922182 = weight(_text_:web in 4984) [ClassicSimilarity], result of:
      0.020922182 = score(doc=4984,freq=2.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.21634221 = fieldWeight in 4984, 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=4984)
    0.0060537956 = weight(_text_:information in 4984) [ClassicSimilarity], result of:
      0.0060537956 = score(doc=4984,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.116372846 = fieldWeight in 4984, 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=4984)
  0.14285715 = coord(2/14)
    Abstract
    Although Web 2.0 contains many tools with different functionalities, they all share a common social nature. One tool in particular, social bookmarking systems (SBSs), allows users to store and share links to different types of resources, i.e., websites, videos, images. To identify and classify these resources so that they can be retrieved and shared, fragments of text are used. These fragments of text, usually words, are called tags. A tag that is found on the inside of a resource text is referred to as an obvious or explicit tag. There are also nonobvious or implicit tags, which don't appear in the resource text. The purpose of this article is to describe the present situation of the SBSs tool and then to also determine the principal features of and how to use explicit tags. It will be taken into special consideration which HTML tags with explicit tags are used more frequently.
    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.2, S.313-322
  4. Fox, M.J.: Communities of practice, gender and social tagging (2012) 0.00 
    0.0038537113 = product of:
  0.026975978 = sum of:
    0.020922182 = weight(_text_:web in 873) [ClassicSimilarity], result of:
      0.020922182 = score(doc=873,freq=2.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.21634221 = fieldWeight in 873, 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=873)
    0.0060537956 = weight(_text_:information in 873) [ClassicSimilarity], result of:
      0.0060537956 = score(doc=873,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.116372846 = fieldWeight in 873, 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=873)
  0.14285715 = coord(2/14)
    Abstract
    Social or collaborative tagging enables users to organize and label resources on the web. Libraries and other information environments hope that tagging can complement professional subject access with user-created terms. But who are the taggers, and does their language represent that of the user population? Some language theorists believe that inherent variables, such as gender or race, can be responsible for language use, whereas other researchers endorse more multiply-influenced practice-based approaches, where interactions with others affect language use more than a single variable. To explore whether linguistic variation in tagging is influenced more by gender or context, in this exploratory study, I will analyze the content and quantity of tags used on LibraryThing. This study seeks to dismantle stereotypical views of women's language use and to suggest a community of practice-based approach to analyzing social tags.
  5. Choi, Y.: ¬A complete assessment of tagging quality : a consolidated methodology (2015) 0.00 
    0.0038537113 = product of:
  0.026975978 = sum of:
    0.020922182 = weight(_text_:web in 1730) [ClassicSimilarity], result of:
      0.020922182 = score(doc=1730,freq=2.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.21634221 = fieldWeight in 1730, 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=1730)
    0.0060537956 = weight(_text_:information in 1730) [ClassicSimilarity], result of:
      0.0060537956 = score(doc=1730,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.116372846 = fieldWeight in 1730, 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=1730)
  0.14285715 = coord(2/14)
    Abstract
    This paper presents a methodological discussion of a study of tagging quality in subject indexing. The data analysis in the study was divided into 3 phases: analysis of indexing consistency, analysis of tagging effectiveness, and analysis of the semantic values of tags. To analyze indexing consistency, this study employed the vector space model-based indexing consistency measures. An analysis of tagging effectiveness with tagging exhaustivity and tag specificity was conducted to ameliorate the drawbacks of consistency analysis based on only the quantitative measures of vocabulary matching. To further investigate the semantic values of tags at various levels of specificity, a latent semantic analysis (LSA) was conducted. To test statistical significance for the relation between tag specificity and semantic quality, correlation analysis was conducted. This research demonstrates the potential of tags for web document indexing with a complete assessment of tagging quality and provides a basis for further study of the strengths and limitations of tagging.
    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.4, S.798-817
  6. Huang, S.-L.; Lin, S.-C.; Chan, Y.-C.: Investigating effectiveness and user acceptance of semantic social tagging for knowledge sharing (2012) 0.00 
    0.0038537113 = product of:
  0.026975978 = sum of:
    0.020922182 = weight(_text_:web in 2732) [ClassicSimilarity], result of:
      0.020922182 = score(doc=2732,freq=2.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.21634221 = fieldWeight in 2732, 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=2732)
    0.0060537956 = weight(_text_:information in 2732) [ClassicSimilarity], result of:
      0.0060537956 = score(doc=2732,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = 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
  7. Knautz, K.; Stock, W.G.: Collective indexing of emotions in videos (2011) 0.00 
    0.0037468998 = product of:
  0.026228298 = sum of:
    0.0050448296 = weight(_text_:information in 295) [ClassicSimilarity], result of:
      0.0050448296 = score(doc=295,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.09697737 = fieldWeight in 295, 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=295)
    0.021183468 = weight(_text_:retrieval in 295) [ClassicSimilarity], result of:
      0.021183468 = score(doc=295,freq=4.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.23632148 = fieldWeight in 295, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=295)
  0.14285715 = coord(2/14)
    Abstract
    Purpose - The object of this empirical research study is emotion, as depicted and aroused in videos. This paper seeks to answer the questions: Are users able to index such emotions consistently? Are the users' votes usable for emotional video retrieval? Design/methodology/approach - The authors worked with a controlled vocabulary for nine basic emotions (love, happiness, fun, surprise, desire, sadness, anger, disgust and fear), a slide control for adjusting the emotions' intensity, and the approach of broad folksonomies. Different users tagged the same videos. The test persons had the task of indexing the emotions of 20 videos (reprocessed clips from YouTube). The authors distinguished between emotions which were depicted in the video and those that were evoked in the user. Data were received from 776 participants and a total of 279,360 slide control values were analyzed. Findings - The consistency of the users' votes is very high; the tag distributions for the particular videos' emotions are stable. The final shape of the distributions will be reached by the tagging activities of only very few users (less than 100). By applying the approach of power tags it is possible to separate the pivotal emotions of every document - if indeed there is any feeling at all. Originality/value - This paper is one of the first steps in the new research area of emotional information retrieval (EmIR). To the authors' knowledge, it is the first research project into the collective indexing of emotions in videos.
  8. Voß, J.: Vom Social Tagging zum Semantic Tagging (2008) 0.00 
    0.0034870307 = product of:
  0.048818428 = sum of:
    0.048818428 = weight(_text_:web in 2884) [ClassicSimilarity], result of:
      0.048818428 = score(doc=2884,freq=8.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = 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)
  0.071428575 = coord(1/14)
    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
  9. Hotho, A.; Jäschke, R.; Benz, D.; Grahl, M.; Krause, B.; Schmitz, C.; Stumme, G.: Social Bookmarking am Beispiel BibSonomy (2009) 0.00 
    0.0034512654 = product of:
  0.04831771 = sum of:
    0.04831771 = weight(_text_:web in 4873) [ClassicSimilarity], result of:
      0.04831771 = score(doc=4873,freq=6.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = 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)
  0.071428575 = coord(1/14)
    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.
    Source
    Social Semantic Web: Web 2.0, was nun? Hrsg.: A. Blumauer u. T. Pellegrini
  10. Furner, J.: User tagging of library resources : toward a framework for system evaluation (2007) 0.00 
    0.0034326524 = product of:
  0.024028566 = sum of:
    0.0060537956 = weight(_text_:information in 703) [ClassicSimilarity], result of:
      0.0060537956 = score(doc=703,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = 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)
    0.01797477 = weight(_text_:retrieval in 703) [ClassicSimilarity], result of:
      0.01797477 = score(doc=703,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = 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)
  0.14285715 = coord(2/14)
    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
  11. Rafferty, P.; Hidderley, R.: Flickr and democratic Indexing : dialogic approaches to indexing (2007) 0.00 
    0.0034326524 = product of:
  0.024028566 = sum of:
    0.0060537956 = weight(_text_:information in 752) [ClassicSimilarity], result of:
      0.0060537956 = score(doc=752,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = 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)
    0.01797477 = weight(_text_:retrieval in 752) [ClassicSimilarity], result of:
      0.01797477 = score(doc=752,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = 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)
  0.14285715 = coord(2/14)
    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.
  12. Kipp, M.E.I.: Searching with tags : do tags help users find things? (2008) 0.00 
    0.0034326524 = product of:
  0.024028566 = sum of:
    0.0060537956 = weight(_text_:information in 2278) [ClassicSimilarity], result of:
      0.0060537956 = score(doc=2278,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.116372846 = fieldWeight in 2278, 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=2278)
    0.01797477 = weight(_text_:retrieval in 2278) [ClassicSimilarity], result of:
      0.01797477 = score(doc=2278,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.20052543 = fieldWeight in 2278, 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=2278)
  0.14285715 = coord(2/14)
    Content
    This study examines the question of whether tags can be useful in the process of information retrieval. Participants were asked to search a social bookmarking tool specialising in academic articles (CiteULike) and an online journal database (Pubmed) in order to determine if users found tags were useful in their search process. The actions of each participants were captured using screen capture software and they were asked to describe their search process. The preliminary study showed that users did indeed make use of tags in their search process, as a guide to searching and as hyperlinks to potentially useful articles. However, users also made use of controlled vocabularies in the journal database.
  13. Naderi, H.; Rumpler, B.: PERCIRS: a system to combine personalized and collaborative information retrieval (2010) 0.00 
    0.003419585 = product of:
  0.023937095 = sum of:
    0.0069903214 = weight(_text_:information in 3960) [ClassicSimilarity], result of:
      0.0069903214 = score(doc=3960,freq=6.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = 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)
    0.016946774 = weight(_text_:retrieval in 3960) [ClassicSimilarity], result of:
      0.016946774 = score(doc=3960,freq=4.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = 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)
  0.14285715 = coord(2/14)
    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.
  14. Kipp, M.E.I.; Campbell, D.G.: Searching with tags : do tags help users find things? (2010) 0.00 
    0.0033881254 = product of:
  0.023716876 = sum of:
    0.008737902 = weight(_text_:information in 4064) [ClassicSimilarity], result of:
      0.008737902 = score(doc=4064,freq=6.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.16796975 = fieldWeight in 4064, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4064)
    0.014978974 = weight(_text_:retrieval in 4064) [ClassicSimilarity], result of:
      0.014978974 = score(doc=4064,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.16710453 = fieldWeight in 4064, 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=4064)
  0.14285715 = coord(2/14)
    Abstract
    The question of whether tags can be useful in the process of information retrieval was examined in this pilot study. Many tags are subject related and could work well as index terms or entry vocabulary; however, folksonomies also include relationships that are traditionally not included in controlled vocabularies including affective or time and task related tags and the user name of the tagger. Participants searched a social bookmarking tool, specialising in academic articles (CiteULike), and an online journal database (Pubmed) for articles relevant to a given information request. Screen capture software was used to collect participant actions and a semi-structured interview asked them to describe their search process. Preliminary results showed that participants did use tags in their search process, as a guide to searching and as hyperlinks to potentially useful articles. However, participants also used controlled vocabularies in the journal database to locate useful search terms and links to related articles supplied by Pubmed. Additionally, participants reported using user names of taggers and group names to help select resources by relevance. The inclusion of subjective and social information from the taggers is very different from the traditional objectivity of indexing and was reported as an asset by a number of participants. This study suggests that while users value social and subjective factors when searching, they also find utility in objective factors such as subject headings. Most importantly, users are interested in the ability of systems to connect them with related articles whether via subject access or other means.
  15. Seeman, D.: Naming names : the ethics of identification in digital library metadata (2012) 0.00 
    0.0033881254 = product of:
  0.023716876 = sum of:
    0.008737902 = weight(_text_:information in 416) [ClassicSimilarity], result of:
      0.008737902 = score(doc=416,freq=6.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.16796975 = fieldWeight in 416, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=416)
    0.014978974 = weight(_text_:retrieval in 416) [ClassicSimilarity], result of:
      0.014978974 = score(doc=416,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.16710453 = fieldWeight in 416, 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=416)
  0.14285715 = coord(2/14)
    Abstract
    In many digital libraries, visual objects are published and metadata attached to allow for search and retrieval. For visual objects in which people appear, names are often added to the metadata so that digital library users can search for people appearing in these objects. Although this seems straightforward, there are ethical implications of adding names to metadata for visual objects. This paper explores the impact of this action and discusses relevant ethical issues it raises. It asserts that an individual's right to privacy and control over personal information must be weighed against the benefit of the object to society and the professional ethic to authentically represent a resource through its metadata. Context and an understanding of the major ethical issues will inform the practical decision of whether to keep objects online and add metadata to them, but items should generally be published unless there are clear ethical violations or a community relationship is in jeopardy.
    Content
    Beitrag aus einem Themenheft zu den Proceedings of the 2nd Milwaukee Conference on Ethics in Information Organization, June 15-16, 2012, School of Information Studies, University of Wisconsin-Milwaukee. Hope A. Olson, Conference Chair. Vgl.: http://www.ergon-verlag.de/isko_ko/downloads/ko_39_2012_5_c.pdf.
  16. Xu, C.; Ma, B.; Chen, X.; Ma, F.: Social tagging in the scholarly world (2013) 0.00 
    0.0033881254 = product of:
  0.023716876 = sum of:
    0.008737902 = weight(_text_:information in 1091) [ClassicSimilarity], result of:
      0.008737902 = score(doc=1091,freq=6.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.16796975 = fieldWeight in 1091, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1091)
    0.014978974 = weight(_text_:retrieval in 1091) [ClassicSimilarity], result of:
      0.014978974 = score(doc=1091,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.16710453 = fieldWeight in 1091, 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=1091)
  0.14285715 = coord(2/14)
    Abstract
    The number of research studies on social tagging has increased rapidly in the past years, but few of them highlight the characteristics and research trends in social tagging. A set of 862 academic documents relating to social tagging and published from 2005 to 2011 was thus examined using bibliometric analysis as well as the social network analysis technique. The results show that social tagging, as a research area, develops rapidly and attracts an increasing number of new entrants. There are no key authors, publication sources, or research groups that dominate the research domain of social tagging. Research on social tagging appears to focus mainly on the following three aspects: (a) components and functions of social tagging (e.g., tags, tagging objects, and tagging network), (b) taggers' behaviors and interface design, and (c) tags' organization and usage in social tagging. The trend suggest that more researchers turn to the latter two integrated with human computer interface and information retrieval, although the first aspect is the fundamental one in social tagging. Also, more studies relating to social tagging pay attention to multimedia tagging objects and not only text tagging. Previous research on social tagging was limited to a few subject domains such as information science and computer science. As an interdisciplinary research area, social tagging is anticipated to attract more researchers from different disciplines. More practical applications, especially in high-tech companies, is an encouraging research trend in social tagging.
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.10, S.2045-2057
  17. Syn, S.Y.; Spring, M.B.: Finding subject terms for classificatory metadata from user-generated social tags (2013) 0.00 
    0.003211426 = product of:
  0.022479981 = sum of:
    0.017435152 = weight(_text_:web in 745) [ClassicSimilarity], result of:
      0.017435152 = score(doc=745,freq=2.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.18028519 = fieldWeight in 745, 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=745)
    0.0050448296 = weight(_text_:information in 745) [ClassicSimilarity], result of:
      0.0050448296 = score(doc=745,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.09697737 = fieldWeight in 745, 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=745)
  0.14285715 = coord(2/14)
    Abstract
    With the increasing popularity of social tagging systems, the potential for using social tags as a source of metadata is being explored. Social tagging systems can simplify the involvement of a large number of users and improve the metadata-generation process. Current research is exploring social tagging systems as a mechanism to allow nonprofessional catalogers to participate in metadata generation. Because social tags are not from controlled vocabularies, there are issues that have to be addressed in finding quality terms to represent the content of a resource. This research explores ways to obtain a set of tags representing the resource from the tags provided by users. Two metrics are introduced. Annotation Dominance (AD) is a measure of the extent to which a tag term is agreed to by users. Cross Resources Annotation Discrimination (CRAD) is a measure of a tag's potential to classify a collection. It is designed to remove tags that are used too broadly or narrowly. Using the proposed measurements, the research selects important tags (meta-terms) and removes meaningless ones (tag noise) from the tags provided by users. To evaluate the proposed approach to find classificatory metadata candidates, we rely on expert users' relevance judgments comparing suggested tag terms and expert metadata terms. The results suggest that processing of user tags using the two measurements successfully identifies the terms that represent the topic categories of web resource content. The suggested tag terms can be further examined in various usages as semantic metadata for the resources.
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.5, S.964-980
  18. 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) 0.00 
    0.0031590632 = product of:
  0.02211344 = sum of:
    0.0071344664 = weight(_text_:information in 4171) [ClassicSimilarity], result of:
      0.0071344664 = score(doc=4171,freq=4.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = 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)
    0.014978974 = weight(_text_:retrieval in 4171) [ClassicSimilarity], result of:
      0.014978974 = score(doc=4171,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = 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)
  0.14285715 = coord(2/14)
    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.
  19. Huang, H.; Jörgensen, C.: Characterizing user tagging and Co-occurring metadata in general and specialized metadata collections (2013) 0.00 
    0.0031590632 = product of:
  0.02211344 = sum of:
    0.0071344664 = weight(_text_:information in 1046) [ClassicSimilarity], result of:
      0.0071344664 = score(doc=1046,freq=4.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.13714671 = fieldWeight in 1046, 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=1046)
    0.014978974 = weight(_text_:retrieval in 1046) [ClassicSimilarity], result of:
      0.014978974 = score(doc=1046,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.16710453 = fieldWeight in 1046, 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=1046)
  0.14285715 = coord(2/14)
    Abstract
    This study aims to identify the categorical characteristics and usage patterns of the most popular image tags in Flickr. The "metadata usage ratio" is introduced as a means of assessing the usage of a popular tag as metadata. We also compare how popular tags are used as image tags or metadata in the Flickr general collection and the Library of Congress's photostream (LCP), also in Flickr. The Flickr popular tags in the list overall are categorically stable, and the changes that do appear reflect Flickr users' evolving technology-driven cultural experience. The popular tags in Flickr had a high number of generic objects and specific locations-related tags and were rarely at the abstract level. Conversely, the popular tags in the LCP describe more in the specific objects and time categories. Flickr users copied the Library of Congress-supplied metadata that related to specific objects or events and standard bibliographic information (e.g., author, format, time references) as popular tags in the LCP. Those popular tags related to generic objects and events showed a high metadata usage ratio, while those related to specific locations and objects showed a low image metadata usage ratio. Popular tags in Flickr appeared less frequently as image metadata when describing specific objects than specific times and locations for historical images in Flickr LCP collections. Understanding how people contribute image tags or image metadata in Flickr helps determine what users need to describe and query images, and could help improve image browsing and retrieval.
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.9, S.1878-1889
  20. Heckner, M.; Mühlbacher, S.; Wolff, C.: Tagging tagging : a classification model for user keywords in scientific bibliography management systems (2007) 0.00 
    0.00299752 = product of:
  0.020982638 = sum of:
    0.0040358636 = weight(_text_:information in 533) [ClassicSimilarity], result of:
      0.0040358636 = score(doc=533,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = 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)
    0.016946774 = weight(_text_:retrieval in 533) [ClassicSimilarity], result of:
      0.016946774 = score(doc=533,freq=4.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = 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)
  0.14285715 = coord(2/14)
    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)?

Authors

Years

Languages

  • e 90
  • d 31
  • i 1
  • More… Less…

Types

  • a 105
  • el 12
  • m 9
  • s 3
  • b 2
  • x 1
  • More… Less…

Themes

Subjects

Classifications