Search (92 results, page 1 of 5)

0.08551306 = product of:
  0.21378265 = sum of:
    0.12993754 = weight(_text_:semantic in 3387) [ClassicSimilarity], result of:
      0.12993754 = score(doc=3387,freq=12.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.67515236 = fieldWeight in 3387, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.046875 = fieldNorm(doc=3387)
    0.08384511 = sum of:
      0.046217266 = weight(_text_:web in 3387) [ClassicSimilarity], result of:
        0.046217266 = score(doc=3387,freq=4.0), product of:
          0.15105948 = queryWeight, product of:
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.04628742 = queryNorm
          0.3059541 = fieldWeight in 3387, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.046875 = fieldNorm(doc=3387)
      0.03762784 = weight(_text_:22 in 3387) [ClassicSimilarity], result of:
        0.03762784 = score(doc=3387,freq=2.0), product of:
          0.16209066 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.04628742 = queryNorm
          0.23214069 = fieldWeight in 3387, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046875 = fieldNorm(doc=3387)
  0.4 = coord(2/5)

Abstract

Libraries are the tools we use to learn and to answer our questions. The quality of our work depends, among others, on the quality of the tools we use. Recent research in digital libraries is focused, on one hand on improving the infrastructure of the digital library management systems (DLMS), and on the other on improving the metadata models used to annotate collections of objects maintained by DLMS. The latter includes, among others, the semantic web and social networking technologies. Recently, the semantic web and social networking technologies are being introduced to the digital libraries domain. The expected outcome is that the overall quality of information discovery in digital libraries can be improved by employing social and semantic technologies. In this chapter we present the results of an evaluation of social and semantic end-user information discovery services for the digital libraries.

Date

1. 8.2010 12:35:22

Source

Semantic digital libraries. Eds.: S.R. Kruk, B. McDaniel

0.07938525 = product of:
  0.13230875 = sum of:
    0.056153342 = weight(_text_:retrieval in 1370) [ClassicSimilarity], result of:
      0.056153342 = score(doc=1370,freq=8.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.40105087 = fieldWeight in 1370, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=1370)
    0.05304678 = weight(_text_:semantic in 1370) [ClassicSimilarity], result of:
      0.05304678 = score(doc=1370,freq=2.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.2756298 = fieldWeight in 1370, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.046875 = fieldNorm(doc=1370)
    0.023108633 = product of:
      0.046217266 = sum of:
        0.046217266 = weight(_text_:web in 1370) [ClassicSimilarity], result of:
          0.046217266 = score(doc=1370,freq=4.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.3059541 = fieldWeight in 1370, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.046875 = fieldNorm(doc=1370)
      0.5 = coord(1/2)
  0.6 = coord(3/5)

Abstract

Large image collections on the Web need to be organized for effective retrieval. Metadata has a key role in image retrieval but rely on professionally assigned tags which is not a viable option. Current content-based image retrieval systems have not demonstrated sufficient utility on large-scale image sources on the web, and are usually used as a supplement to existing text-based image retrieval systems. We present two social tagging alternatives in the form of photo-sharing networks and image labeling games. Here we analyze these applications to evaluate their usefulness from the semantic point of view, investigating the management of social tagging for indexing. The findings of the study have shown that social tagging can generate a sizeable number of tags that can be classified as in terpretive for an image, and that tagging behaviour has a manageable and adjustable nature depending on tagging guidelines.

0.07649208 = product of:
  0.1274868 = sum of:
    0.023397226 = weight(_text_:retrieval in 2648) [ClassicSimilarity], result of:
      0.023397226 = score(doc=2648,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = 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)
    0.0884113 = weight(_text_:semantic in 2648) [ClassicSimilarity], result of:
      0.0884113 = score(doc=2648,freq=8.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.45938298 = fieldWeight in 2648, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2648)
    0.015678266 = product of:
      0.031356532 = sum of:
        0.031356532 = weight(_text_:22 in 2648) [ClassicSimilarity], result of:
          0.031356532 = score(doc=2648,freq=2.0), product of:
            0.16209066 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04628742 = 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)
  0.6 = coord(3/5)

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

0.07060515 = product of:
  0.17651288 = sum of:
    0.13838558 = weight(_text_:semantic in 2884) [ClassicSimilarity], result of:
      0.13838558 = score(doc=2884,freq=10.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.71904814 = fieldWeight in 2884, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2884)
    0.038127303 = product of:
      0.076254606 = sum of:
        0.076254606 = weight(_text_:web in 2884) [ClassicSimilarity], result of:
          0.076254606 = score(doc=2884,freq=8.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.50479853 = fieldWeight in 2884, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2884)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

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

0.061612546 = product of:
  0.15403137 = sum of:
    0.062516235 = weight(_text_:semantic in 2652) [ClassicSimilarity], result of:
      0.062516235 = score(doc=2652,freq=4.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.32483283 = fieldWeight in 2652, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2652)
    0.09151513 = sum of:
      0.0471703 = weight(_text_:web in 2652) [ClassicSimilarity], result of:
        0.0471703 = score(doc=2652,freq=6.0), product of:
          0.15105948 = queryWeight, product of:
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.04628742 = queryNorm
          0.3122631 = fieldWeight in 2652, product of:
            2.4494898 = tf(freq=6.0), with freq of:
              6.0 = termFreq=6.0
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.0390625 = fieldNorm(doc=2652)
      0.04434483 = weight(_text_:22 in 2652) [ClassicSimilarity], result of:
        0.04434483 = score(doc=2652,freq=4.0), product of:
          0.16209066 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.04628742 = queryNorm
          0.27358043 = fieldWeight in 2652, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=2652)
  0.4 = coord(2/5)

Abstract

Folksonomy is the result of describing Web resources with tags created by Web users. Although it has become a popular application for the description of resources, in general terms Folksonomies are not being conveniently integrated in metadata. However, if the appropriate metadata elements are identified, then further work may be conducted to automatically assign tags to these elements (RDF properties) and use them in Semantic Web applications. This article presents research carried out to continue the project Kinds of Tags, which intends to identify elements required for metadata originating from folksonomies and to propose an application profile for DC Social Tagging. The work provides information that may be used by software applications to assign tags to metadata elements and, therefore, means for tags to be conveniently gathered by metadata interoperability tools. Despite the unquestionably high value of DC and the significance of the already existing properties in DC Terms, the pilot study show revealed a significant number of tags for which no corresponding properties yet existed. A need for new properties, such as Action, Depth, Rate, and Utility was determined. Those potential new properties will have to be validated in a later stage by the DC Social Tagging Community.

Pages

S.14-22

Source

Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas

0.060572736 = product of:
  0.100954555 = sum of:
    0.01871778 = weight(_text_:retrieval in 2657) [ClassicSimilarity], result of:
      0.01871778 = score(doc=2657,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = 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)
    0.03536452 = weight(_text_:semantic in 2657) [ClassicSimilarity], result of:
      0.03536452 = score(doc=2657,freq=2.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.18375319 = fieldWeight in 2657, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.03125 = fieldNorm(doc=2657)
    0.046872254 = sum of:
      0.021787029 = weight(_text_:web in 2657) [ClassicSimilarity], result of:
        0.021787029 = score(doc=2657,freq=2.0), product of:
          0.15105948 = queryWeight, product of:
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.04628742 = queryNorm
          0.14422815 = fieldWeight in 2657, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.03125 = fieldNorm(doc=2657)
      0.025085226 = weight(_text_:22 in 2657) [ClassicSimilarity], result of:
        0.025085226 = score(doc=2657,freq=2.0), product of:
          0.16209066 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.04628742 = 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)
  0.6 = coord(3/5)

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

0.05992825 = product of:
  0.14982063 = sum of:
    0.10719301 = weight(_text_:semantic in 4866) [ClassicSimilarity], result of:
      0.10719301 = score(doc=4866,freq=6.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.55697227 = fieldWeight in 4866, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4866)
    0.042627618 = product of:
      0.085255235 = sum of:
        0.085255235 = weight(_text_:web in 4866) [ClassicSimilarity], result of:
          0.085255235 = score(doc=4866,freq=10.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.5643819 = fieldWeight in 4866, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4866)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

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

0.059918642 = product of:
  0.1497966 = sum of:
    0.09884685 = weight(_text_:semantic in 432) [ClassicSimilarity], result of:
      0.09884685 = score(doc=432,freq=10.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.51360583 = fieldWeight in 432, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.0390625 = fieldNorm(doc=432)
    0.05094975 = product of:
      0.1018995 = sum of:
        0.1018995 = weight(_text_:web in 432) [ClassicSimilarity], result of:
          0.1018995 = score(doc=432,freq=28.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.6745654 = fieldWeight in 432, product of:
              5.2915025 = tf(freq=28.0), with freq of:
                28.0 = termFreq=28.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0390625 = fieldNorm(doc=432)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

For a long while, the creation of Web content required at least basic knowledge of Web technologies, meaning that for many Web users, the Web was de facto a read-only medium. This changed with the arrival of the "social Web," when Web applications started to allow users to publish Web content without technological expertise. Here, content creation is often an inclusive, iterative, and interactive process. Examples of social Web applications include blogs, social networking sites, as well as many specialized applications, for example, for saving and sharing bookmarks and publishing photos. Social semantic Web applications are social Web applications in which knowledge is expressed not only in the form of text and multimedia but also through informal to formal annotations that describe, reflect, and enhance the content. These annotations often take the shape of RDF graphs backed by ontologies, but less formal annotations such as free-form tags or tags from a controlled vocabulary may also be available. Wikis are one example of social Web applications for collecting and sharing knowledge. They allow users to easily create and edit documents, so-called wiki pages, using a Web browser. The pages in a wiki are often heavily interlinked, which makes it easy to find related information and browse the content.

Object

Semantic Wiki

Source

Semantic search over the Web. Eds.: R. De Virgilio, et al

Theme

Semantic Web

0.053982593 = product of:
  0.13495648 = sum of:
    0.118616216 = weight(_text_:semantic in 2894) [ClassicSimilarity], result of:
      0.118616216 = score(doc=2894,freq=10.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.616327 = fieldWeight in 2894, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.046875 = fieldNorm(doc=2894)
    0.01634027 = product of:
      0.03268054 = sum of:
        0.03268054 = weight(_text_:web in 2894) [ClassicSimilarity], result of:
          0.03268054 = score(doc=2894,freq=2.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.21634221 = fieldWeight in 2894, 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=2894)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

Der Begriff "Semantic Weblog" bezeichnet die Idee, zwei Konzepte - nämlich Bloggen und Semantic Web - zusammenzuführen. Ausgangspunkt ist dabei die Tatsache, dass Blogs, die länger bestehen, Wissen über bestimmte Domänen ansammeln. Dieses Wissen wird in einem ersten Schritt durch Volltextanalyse und in einem zweien Schritt durch Kategorie- und Tagging-Mechanismen erschlossen und kann durch weitere Schritte zu einfachen Ontologien ausgebaut werden. Dieser Beitrag gliedert sich in mehrere Teile. Zunächst wird das Konzept und seine ersten Implementierungen sowie mögliche Vernetzung von mehreren Semantic Weblogs vorgestellt. Dann wird ein Einblick in die Erfahrungen aus der Semantic Weblog-Praxis gegeben. Abgeschlossen wird der Artikel durch einen Ausblick.

0.053982593 = product of:
  0.13495648 = sum of:
    0.118616216 = weight(_text_:semantic in 2732) [ClassicSimilarity], result of:
      0.118616216 = score(doc=2732,freq=10.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.616327 = fieldWeight in 2732, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.046875 = fieldNorm(doc=2732)
    0.01634027 = product of:
      0.03268054 = sum of:
        0.03268054 = weight(_text_:web in 2732) [ClassicSimilarity], result of:
          0.03268054 = score(doc=2732,freq=2.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = 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)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

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.

0.048973534 = product of:
  0.122433834 = sum of:
    0.10609356 = weight(_text_:semantic in 1730) [ClassicSimilarity], result of:
      0.10609356 = score(doc=1730,freq=8.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.5512596 = fieldWeight in 1730, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.046875 = fieldNorm(doc=1730)
    0.01634027 = product of:
      0.03268054 = sum of:
        0.03268054 = weight(_text_:web in 1730) [ClassicSimilarity], result of:
          0.03268054 = score(doc=1730,freq=2.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = 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)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

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.

0.045810048 = product of:
  0.11452512 = sum of:
    0.09884685 = weight(_text_:semantic in 2650) [ClassicSimilarity], result of:
      0.09884685 = score(doc=2650,freq=10.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.51360583 = fieldWeight in 2650, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2650)
    0.015678266 = product of:
      0.031356532 = sum of:
        0.031356532 = weight(_text_:22 in 2650) [ClassicSimilarity], result of:
          0.031356532 = score(doc=2650,freq=2.0), product of:
            0.16209066 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04628742 = queryNorm
            0.19345059 = fieldWeight in 2650, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2650)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

There is a growing interest into how we represent and share tagging data in collaborative tagging systems. Conventional tags, meaning freely created tags that are not associated with a structured ontology, are not naturally suited for collaborative processes, due to linguistic and grammatical variations, as well as human typing errors. Additionally, tags reflect personal views of the world by individual users, and are not normalised for synonymy, morphology or any other mapping. Our view is that the conventional approach provides very limited semantic value for collaboration. Moreover, in cases where there is some semantic value, automatically sharing semantics via computer manipulations is extremely problematic. This paper explores these problems by discussing approaches for collaborative tagging activities at a semantic level, and presenting conceptual models for collaborative tagging activities and folksonomies. We present criteria for the comparison of existing tag ontologies and discuss their strengths and weaknesses in relation to these criteria.

Source

Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas

0.043688532 = product of:
  0.109221324 = sum of:
    0.023397226 = weight(_text_:retrieval in 1291) [ClassicSimilarity], result of:
      0.023397226 = score(doc=1291,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.16710453 = fieldWeight in 1291, 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=1291)
    0.0858241 = sum of:
      0.054467574 = weight(_text_:web in 1291) [ClassicSimilarity], result of:
        0.054467574 = score(doc=1291,freq=8.0), product of:
          0.15105948 = queryWeight, product of:
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.04628742 = queryNorm
          0.36057037 = fieldWeight in 1291, product of:
            2.828427 = tf(freq=8.0), with freq of:
              8.0 = termFreq=8.0
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.0390625 = fieldNorm(doc=1291)
      0.031356532 = weight(_text_:22 in 1291) [ClassicSimilarity], result of:
        0.031356532 = score(doc=1291,freq=2.0), product of:
          0.16209066 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.04628742 = queryNorm
          0.19345059 = fieldWeight in 1291, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=1291)
  0.4 = coord(2/5)

Abstract

More and more users index everything on their own in the web 2.0. There are services for links, videos, pictures, books, encyclopaedic articles and scientific articles. All these services are library independent. But must that really be? Can't libraries help with their experience and tools to make user indexing better? On the experience of a project from German language Wikipedia together with the German person authority files (Personen Namen Datei - PND) located at German National Library (Deutsche Nationalbibliothek) I would like to show what is possible. How users can and will use the authority files, if we let them. We will take a look how the project worked and what we can learn for future projects. Conclusions - Authority files can have a role in the web 2.0 - there must be an open interface/ service for retrieval - everything that is indexed on the net with authority files can be easy integrated in a federated search - O'Reilly: You have to found ways that your data get more important that more it will be used

Content

Vortrag anlässlich des Workshops: "Extending the multilingual capacity of The European Library in the EDL project Stockholm, Swedish National Library, 22-23 November 2007".

Object

Web 2.0

0.043386113 = product of:
  0.108465284 = sum of:
    0.07072904 = weight(_text_:semantic in 4873) [ClassicSimilarity], result of:
      0.07072904 = score(doc=4873,freq=2.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.36750638 = fieldWeight in 4873, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.0625 = fieldNorm(doc=4873)
    0.03773624 = product of:
      0.07547248 = sum of:
        0.07547248 = weight(_text_:web in 4873) [ClassicSimilarity], result of:
          0.07547248 = score(doc=4873,freq=6.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.49962097 = fieldWeight in 4873, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0625 = fieldNorm(doc=4873)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

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

0.037064645 = product of:
  0.061774407 = sum of:
    0.011698613 = weight(_text_:retrieval in 3054) [ClassicSimilarity], result of:
      0.011698613 = score(doc=3054,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.08355226 = fieldWeight in 3054, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.01953125 = fieldNorm(doc=3054)
    0.038283218 = weight(_text_:semantic in 3054) [ClassicSimilarity], result of:
      0.038283218 = score(doc=3054,freq=6.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.19891867 = fieldWeight in 3054, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.01953125 = fieldNorm(doc=3054)
    0.011792575 = product of:
      0.02358515 = sum of:
        0.02358515 = weight(_text_:web in 3054) [ClassicSimilarity], result of:
          0.02358515 = score(doc=3054,freq=6.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.15613155 = fieldWeight in 3054, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.01953125 = fieldNorm(doc=3054)
      0.5 = coord(1/2)
  0.6 = coord(3/5)

Abstract

Teile und sammle könnte der moderne Leitspruch für das Phänomen Social Tagging heißen. Die freie und kollaborative Verschlagwortung digitaler Ressourcen im Internet gehört zu den Anwendungen aus dem Kontext von Web 2.0, die sich zunehmender Beliebtheit erfreuen. Der 2003 gegründete Social Bookmarking Dienst Del.icio.us und die 2004 entstandene Bildersammlung Flickr waren erste Anwendungen, die Social Tagging anboten und noch immer einen Großteil der Nutzer/innen an sich binden. Beim Blick in die Literatur wird schnell deutlich, dass Social Tagging polarisiert: Von Befürwortern wird es als eine Form der innovativen Wissensorganisation gefeiert, während Skeptiker die Dienste des Web 2.0 inklusive Social Tagging als globale kulturelle Bedrohung verdammen. Launischer Hype oder Quantensprung was ist dran am Social Tagging? Mit der Zielsetzung, mehr über die Erwartungen, Anwendungsbereiche und Nutzungsweisen zu erfahren, wurde im Frühjahr 2008 am Institut für Wissensmedien (IWM) in Tübingen ein Workshop der Gesellschaft für Medien in der Wissenschaft (GMW) durchgeführt. Die vorliegende Publikation fasst die Ergebnisse der interdisziplinären Veranstaltung zusammen.

Content

- Tagging im Semantic Web Benjamin Birkenhake: Semantic Weblog. Erfahrungen vom Bloggen mit Tags und Ontologien Simone Braun, Andreas Schmidt, Andreas Walter & Valentin Zacharias: Von Tags zu semantischen Beziehungen: kollaborative Ontologiereifung Jakob Voß: Vom Social Tagging zum Semantic Tagging Georg Güntner, Rolf Sint & Rupert Westenthaler: Ein Ansatz zur Unterstützung traditioneller Klassifikation durch Social Tagging Viktoria Pammer, Tobias Ley & Stefanie Lindstaedt: tagr: Unterstützung in kollaborativen Tagging-Umgebungen durch Semantische und Assoziative Netzwerke Matthias Quasthoff Harald Sack & Christoph Meinet: Nutzerfreundliche Internet-Sicherheit durch tag-basierte Zugriffskontrolle

Footnote

Enthält die Beiträge der Tagung "Social Tagging in der Wissensorganisation" am 21.-22.02.2008 am Institut für Wissensmedien (IWM) in Tübingen. Volltext unter: http://www.waxmann.com/kat/inhalt/2039Volltext.pdf. Vgl. die Rez. unter: http://sehepunkte.de/2008/11/14934.html. Rez. in: IWP 60(1009) H.4, S.246-247 (C. Wolff): "Tagging-Systeme erfreuen sich in den letzten Jahren einer ungemein großen Beliebtheit, erlauben sie dem Nutzer doch die Informationserschließung "mit eigenen Worten", also ohne Rekurs auf vorgegebene Ordnungs- und Begriffsysteme und für Medien wie Bild und Video, für die herkömmliche Verfahren des Information Retrieval (noch) versagen. Die Beherrschung der Film- und Bilderfülle, wie wir sie bei Flickr oder YouTube vorfinden, ist mit anderen Mitteln als dem intellektuellen Einsatz der Nutzer nicht vorstellbar - eine professionelle Aufbereitung angesichts der Massendaten (und ihrer zu einem großen Teil auch minderen Qualität) nicht möglich und sinnvoll. Insofern hat sich Tagging als ein probates Mittel der Erschließung herausgebildet, das dort Lücken füllen kann, wo andere Verfahren (Erschließung durch information professionals, automatische Indexierung, Erschließung durch Autoren) fehlen oder nicht anwendbar sind. Unter dem Titel "Good Tags - Bad Tags. Social Tagging in der Wissensorganisation" und der Herausgeberschaft von Birgit Gaiser, Thorsten Hampel und Stefanie Panke sind in der Reihe Medien in der Wissenschaft (Bd. 47) Beiträge eines interdisziplinären Workshops der Gesellschaft für Medien in der Wissenschaft zum Thema Tagging versammelt, der im Frühjahr 2008 am Institut für Wissensmedien in Tübingen stattgefunden hat. . . .

0.03594194 = product of:
  0.08985485 = sum of:
    0.04679445 = weight(_text_:retrieval in 2931) [ClassicSimilarity], result of:
      0.04679445 = score(doc=2931,freq=8.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = 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)
    0.043060396 = product of:
      0.08612079 = sum of:
        0.08612079 = weight(_text_:web in 2931) [ClassicSimilarity], result of:
          0.08612079 = score(doc=2931,freq=20.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.5701118 = fieldWeight in 2931, product of:
              4.472136 = tf(freq=20.0), with freq of:
                20.0 = termFreq=20.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2931)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

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

Object

Web 2.0

RSWK

World Wide Web 2.0 / Benutzer / Online-Publizieren / Information Retrieval / Soziale Software / Hilfesystem
Social Tagging / Filter / Web log / World Wide Web 2.0

Subject

World Wide Web 2.0 / Benutzer / Online-Publizieren / Information Retrieval / Soziale Software / Hilfesystem
Social Tagging / Filter / Web log / World Wide Web 2.0

0.032894712 = product of:
  0.082236774 = sum of:
    0.03536452 = weight(_text_:semantic in 2666) [ClassicSimilarity], result of:
      0.03536452 = score(doc=2666,freq=2.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.18375319 = fieldWeight in 2666, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.03125 = fieldNorm(doc=2666)
    0.046872254 = sum of:
      0.021787029 = weight(_text_:web in 2666) [ClassicSimilarity], result of:
        0.021787029 = score(doc=2666,freq=2.0), product of:
          0.15105948 = queryWeight, product of:
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.04628742 = queryNorm
          0.14422815 = fieldWeight in 2666, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.03125 = fieldNorm(doc=2666)
      0.025085226 = weight(_text_:22 in 2666) [ClassicSimilarity], result of:
        0.025085226 = score(doc=2666,freq=2.0), product of:
          0.16209066 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.04628742 = 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)
  0.4 = coord(2/5)

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

0.03279502 = product of:
  0.081987545 = sum of:
    0.023397226 = weight(_text_:retrieval in 515) [ClassicSimilarity], result of:
      0.023397226 = score(doc=515,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = 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)
    0.05859032 = sum of:
      0.027233787 = weight(_text_:web in 515) [ClassicSimilarity], result of:
        0.027233787 = score(doc=515,freq=2.0), product of:
          0.15105948 = queryWeight, product of:
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.04628742 = queryNorm
          0.18028519 = fieldWeight in 515, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.0390625 = fieldNorm(doc=515)
      0.031356532 = weight(_text_:22 in 515) [ClassicSimilarity], result of:
        0.031356532 = score(doc=515,freq=2.0), product of:
          0.16209066 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.04628742 = 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)
  0.4 = coord(2/5)

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

0.032380622 = product of:
  0.08095156 = sum of:
    0.06188791 = weight(_text_:semantic in 1968) [ClassicSimilarity], result of:
      0.06188791 = score(doc=1968,freq=2.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.32156807 = fieldWeight in 1968, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1968)
    0.019063652 = product of:
      0.038127303 = sum of:
        0.038127303 = weight(_text_:web in 1968) [ClassicSimilarity], result of:
          0.038127303 = score(doc=1968,freq=2.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.25239927 = fieldWeight in 1968, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1968)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Footnote

Contribution in a special issue "Beyond libraries: Subject metadata in the digital environment and Semantic Web" - Enthält Beiträge der gleichnamigen IFLA Satellite Post-Conference, 17-18 August 2012, Tallinn.

0.031828072 = product of:
  0.15914035 = sum of:
    0.15914035 = weight(_text_:semantic in 4648) [ClassicSimilarity], result of:
      0.15914035 = score(doc=4648,freq=18.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.8268894 = fieldWeight in 4648, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.046875 = fieldNorm(doc=4648)
  0.2 = coord(1/5)

Abstract

The purpose of this paper is to determine semantic types for tags in social tagging systems. In social tagging systems, the determination of the semantic type of tags plays an important role in tag classification, increasing the semantic information of tags and establishing mapping relations between tagged resources and a normed ontology. The research reported in this paper constructs the semantic type library that is needed based on the Unified Medical Language System (UMLS) and FrameNet and determines the semantic type of selected tags that have been pretreated via direct matching using the Semantic Navigator tool, the Semantic Type Word Sense Disambiguation (STWSD) tools in UMLS, and artificial matching. And finally, we verify the feasibility of the determination of semantic type for tags by empirical analysis.