Search (47 results, page 1 of 3)

  • × theme_ss:"Social tagging"
  1. Catarino, M.E.; Baptista, A.A.: Relating folksonomies with Dublin Core (2008) 0.03
    0.02968728 = product of:
      0.0742182 = sum of:
        0.024150565 = weight(_text_:it in 2652) [ClassicSimilarity], result of:
          0.024150565 = score(doc=2652,freq=2.0), product of:
            0.15115225 = queryWeight, product of:
              2.892262 = idf(docFreq=6664, maxDocs=44218)
              0.052260913 = queryNorm
            0.15977642 = fieldWeight in 2652, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.892262 = idf(docFreq=6664, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2652)
        0.050067633 = weight(_text_:22 in 2652) [ClassicSimilarity], result of:
          0.050067633 = score(doc=2652,freq=4.0), product of:
            0.18300882 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052260913 = 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
  2. Qin, C.; Liu, Y.; Mou, J.; Chen, J.: User adoption of a hybrid social tagging approach in an online knowledge community (2019) 0.03
    0.027822888 = product of:
      0.06955722 = sum of:
        0.034154054 = weight(_text_:it in 5492) [ClassicSimilarity], result of:
          0.034154054 = score(doc=5492,freq=4.0), product of:
            0.15115225 = queryWeight, product of:
              2.892262 = idf(docFreq=6664, maxDocs=44218)
              0.052260913 = queryNorm
            0.22595796 = fieldWeight in 5492, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.892262 = idf(docFreq=6664, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5492)
        0.035403162 = weight(_text_:22 in 5492) [ClassicSimilarity], result of:
          0.035403162 = score(doc=5492,freq=2.0), product of:
            0.18300882 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052260913 = queryNorm
            0.19345059 = fieldWeight in 5492, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5492)
      0.4 = coord(2/5)
    
    Abstract
    Purpose Online knowledge communities make great contributions to global knowledge sharing and innovation. Resource tagging approaches have been widely adopted in such communities to describe, annotate and organize knowledge resources mainly through users' participation. However, it is unclear what causes the adoption of a particular resource tagging approach. The purpose of this paper is to identify factors that drive users to use a hybrid social tagging approach. Design/methodology/approach Technology acceptance model and social cognitive theory are adopted to support an integrated model proposed in this paper. Zhihu, one of the most popular online knowledge communities in China, is taken as the survey context. A survey was conducted with a questionnaire and collected data were analyzed through structural equation model. Findings A new hybrid social resource tagging approach was refined and described. The empirical results revealed that self-efficacy, perceived usefulness (PU) and perceived ease of use exert positive effect on users' attitude. Moreover, social influence, PU and attitude impact significantly on users' intention to use a hybrid social resource tagging approach. Originality/value Theoretically, this study enriches the type of resource tagging approaches and recognizes factors influencing user adoption to use it. Regarding the practical parts, the results provide online information system providers and designers with referential strategies to improve the performance of the current tagging approaches and promote them.
    Date
    20. 1.2015 18:30:22
  3. Danowski, P.: Authority files and Web 2.0 : Wikipedia and the PND. An Example (2007) 0.02
    0.023821492 = product of:
      0.059553728 = sum of:
        0.024150565 = weight(_text_:it in 1291) [ClassicSimilarity], result of:
          0.024150565 = score(doc=1291,freq=2.0), product of:
            0.15115225 = queryWeight, product of:
              2.892262 = idf(docFreq=6664, maxDocs=44218)
              0.052260913 = queryNorm
            0.15977642 = fieldWeight in 1291, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.892262 = idf(docFreq=6664, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1291)
        0.035403162 = weight(_text_:22 in 1291) [ClassicSimilarity], result of:
          0.035403162 = score(doc=1291,freq=2.0), product of:
            0.18300882 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052260913 = 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".
  4. Choi, Y.; Syn, S.Y.: Characteristics of tagging behavior in digitized humanities online collections (2016) 0.02
    0.023821492 = product of:
      0.059553728 = sum of:
        0.024150565 = weight(_text_:it in 2891) [ClassicSimilarity], result of:
          0.024150565 = score(doc=2891,freq=2.0), product of:
            0.15115225 = queryWeight, product of:
              2.892262 = idf(docFreq=6664, maxDocs=44218)
              0.052260913 = queryNorm
            0.15977642 = fieldWeight in 2891, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.892262 = idf(docFreq=6664, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2891)
        0.035403162 = weight(_text_:22 in 2891) [ClassicSimilarity], result of:
          0.035403162 = score(doc=2891,freq=2.0), product of:
            0.18300882 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052260913 = queryNorm
            0.19345059 = fieldWeight in 2891, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2891)
      0.4 = coord(2/5)
    
    Abstract
    The purpose of this study was to examine user tags that describe digitized archival collections in the field of humanities. A collection of 8,310 tags from a digital portal (Nineteenth-Century Electronic Scholarship, NINES) was analyzed to find out what attributes of primary historical resources users described with tags. Tags were categorized to identify which tags describe the content of the resource, the resource itself, and subjective aspects (e.g., usage or emotion). The study's findings revealed that over half were content-related; tags representing opinion, usage context, or self-reference, however, reflected only a small percentage. The study further found that terms related to genre or physical format of a resource were frequently used in describing primary archival resources. It was also learned that nontextual resources had lower numbers of content-related tags and higher numbers of document-related tags than textual resources and bibliographic materials; moreover, textual resources tended to have more user-context-related tags than other resources. These findings help explain users' tagging behavior and resource interpretation in primary resources in the humanities. Such information provided through tags helps information professionals decide to what extent indexing archival and cultural resources should be done for resource description and discovery, and understand users' terminology.
    Date
    21. 4.2016 11:23:22
  5. Vander Wal, T.: Welcome to the Matrix! (2008) 0.02
    0.019057194 = product of:
      0.047642983 = sum of:
        0.019320453 = weight(_text_:it in 2881) [ClassicSimilarity], result of:
          0.019320453 = score(doc=2881,freq=2.0), product of:
            0.15115225 = queryWeight, product of:
              2.892262 = idf(docFreq=6664, maxDocs=44218)
              0.052260913 = queryNorm
            0.12782113 = fieldWeight in 2881, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.892262 = idf(docFreq=6664, maxDocs=44218)
              0.03125 = fieldNorm(doc=2881)
        0.02832253 = weight(_text_:22 in 2881) [ClassicSimilarity], result of:
          0.02832253 = score(doc=2881,freq=2.0), product of:
            0.18300882 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052260913 = queryNorm
            0.15476047 = fieldWeight in 2881, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03125 = fieldNorm(doc=2881)
      0.4 = coord(2/5)
    
    Abstract
    My keynote at the workshop "Social Tagging in Knowledge Organization" was a great opportunity to make and share new experiences. For the first time ever, I sat in my office at home and gave a live web video presentation to a conference audience elsewhere on the globe. At the same time, it was also an opportunity to premier my conceptual model "Matrix of Perception" to an interdisciplinary audience of researchers and practitioners with a variety of backgrounds - reaching from philosophy, psychology, pedagogy and computation to library science and economics. The interdisciplinary approach of the conference is also mirrored in the structure of this volume, with articles on the theoretical background, the empirical analysis and the potential applications of tagging, for instance in university libraries, e-learning, or e-commerce. As an introduction to the topic of "social tagging" I would like to draw your attention to some foundation concepts of the phenomenon I have racked my brain with for the last few month. One thing I have seen missing in recent research and system development is a focus on the variety of user perspectives in social tagging. Different people perceive tagging in complex variegated ways and use this form of knowledge organization for a variety of purposes. My analytical interest lies in understanding the personas and patterns in tagging systems and in being able to label their different perceptions. To come up with a concise picture of user expectations, needs and activities, I have broken down the perspectives on tagging into two different categories, namely "faces" and "depth". When put together, they form the "Matrix of Perception" - a nuanced view of stakeholders and their respective levels of participation.
    Date
    22. 6.2009 9:15:45
  6. Müller-Prove, M.: Modell und Anwendungsperspektive des Social Tagging (2008) 0.01
    0.011329013 = product of:
      0.05664506 = sum of:
        0.05664506 = weight(_text_:22 in 2882) [ClassicSimilarity], result of:
          0.05664506 = score(doc=2882,freq=2.0), product of:
            0.18300882 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052260913 = queryNorm
            0.30952093 = fieldWeight in 2882, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=2882)
      0.2 = coord(1/5)
    
    Pages
    S.15-22
  7. Rafferty, P.; Murphy, H.: Is there nothing outside the tags? : towards a poststructuralist analysis of social tagging (2015) 0.01
    0.01080046 = product of:
      0.0540023 = sum of:
        0.0540023 = weight(_text_:it in 1792) [ClassicSimilarity], result of:
          0.0540023 = score(doc=1792,freq=10.0), product of:
            0.15115225 = queryWeight, product of:
              2.892262 = idf(docFreq=6664, maxDocs=44218)
              0.052260913 = queryNorm
            0.3572709 = fieldWeight in 1792, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              2.892262 = idf(docFreq=6664, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1792)
      0.2 = coord(1/5)
    
    Abstract
    Purpose The purpose of the research is to explore relationships between social tagging and key poststructuralist principles; to devise and construct an analytical framework through which key poststructuralist principles are converted into workable research questions and applied to analyse Librarything tags, and to assess the validity of performing such an analysis. The research hypothesis is that tagging represents an imperfect analogy for the poststructuralist project Design/methodology/approach Tags from LibraryThing and from a library OPAC were compared and constrasted with Library of Congress Subject Headings (LCSH) and publishers' descriptions. Research questions derived from poststructuralism, asked whether tags destabilise meaning, whether and how far the death of the author is expressed in tags, and whether tags deconstruct LCSH. Findings Tags can temporarily destabilise meaning by obfuscating the structure of a word. Meaning is destabilised, perhaps only momentarily, and then it is recreated; it might resemble the original meaning, or it may not, however any attempt to make tags useful or functional necessarily imposes some form of structure. The analysis indicates that in tagging, the author, if not dead, is ignored. Authoritative interpretations are not pervasively mimicked in the tags. In relation to LCSH, tagging decentres the dominant view, but neither exposes nor judges it. Nor does tagging achieve the final stage of the deconstructive process, showing the dominant view to be a constructed reality. Originality/value This is one of very few studies to have attempted a critical theoretical approach to social tagging. It offers a novel methodological approach to undertaking analysis based on poststructuralist theory.
  8. Harrer, A.; Lohmann, S.: Potenziale von Tagging als partizipative Methode für Lehrportale und E-Learning-Kurse (2008) 0.01
    0.009912886 = product of:
      0.04956443 = sum of:
        0.04956443 = weight(_text_:22 in 2889) [ClassicSimilarity], result of:
          0.04956443 = score(doc=2889,freq=2.0), product of:
            0.18300882 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052260913 = queryNorm
            0.2708308 = fieldWeight in 2889, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2889)
      0.2 = coord(1/5)
    
    Date
    21. 6.2009 12:22:44
  9. Kruk, S.R.; Kruk, E.; Stankiewicz, K.: Evaluation of semantic and social technologies for digital libraries (2009) 0.01
    0.008496759 = product of:
      0.042483795 = sum of:
        0.042483795 = weight(_text_:22 in 3387) [ClassicSimilarity], result of:
          0.042483795 = score(doc=3387,freq=2.0), product of:
            0.18300882 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052260913 = 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)
      0.2 = coord(1/5)
    
    Date
    1. 8.2010 12:35:22
  10. Rolla, P.J.: User tags versus Subject headings : can user-supplied data improve subject access to library collections? (2009) 0.01
    0.008496759 = product of:
      0.042483795 = sum of:
        0.042483795 = weight(_text_:22 in 3601) [ClassicSimilarity], result of:
          0.042483795 = score(doc=3601,freq=2.0), product of:
            0.18300882 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052260913 = queryNorm
            0.23214069 = fieldWeight in 3601, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=3601)
      0.2 = coord(1/5)
    
    Date
    10. 9.2000 17:38:22
  11. Strader, C.R.: Author-assigned keywords versus Library of Congress Subject Headings : implications for the cataloging of electronic theses and dissertations (2009) 0.01
    0.008496759 = product of:
      0.042483795 = sum of:
        0.042483795 = weight(_text_:22 in 3602) [ClassicSimilarity], result of:
          0.042483795 = score(doc=3602,freq=2.0), product of:
            0.18300882 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052260913 = queryNorm
            0.23214069 = fieldWeight in 3602, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=3602)
      0.2 = coord(1/5)
    
    Date
    10. 9.2000 17:38:22
  12. Niemann, C.: Tag-Science : Ein Analysemodell zur Nutzbarkeit von Tagging-Daten (2011) 0.01
    0.008496759 = product of:
      0.042483795 = sum of:
        0.042483795 = weight(_text_:22 in 164) [ClassicSimilarity], result of:
          0.042483795 = score(doc=164,freq=2.0), product of:
            0.18300882 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052260913 = queryNorm
            0.23214069 = fieldWeight in 164, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=164)
      0.2 = coord(1/5)
    
    Source
    ¬Die Kraft der digitalen Unordnung: 32. Arbeits- und Fortbildungstagung der ASpB e. V., Sektion 5 im Deutschen Bibliotheksverband, 22.-25. September 2009 in der Universität Karlsruhe. Hrsg: Jadwiga Warmbrunn u.a
  13. Simon, J.: Interdisciplinary knowledge creation : using wikis in science (2006) 0.01
    0.008366001 = product of:
      0.041830003 = sum of:
        0.041830003 = weight(_text_:it in 2516) [ClassicSimilarity], result of:
          0.041830003 = score(doc=2516,freq=6.0), product of:
            0.15115225 = queryWeight, product of:
              2.892262 = idf(docFreq=6664, maxDocs=44218)
              0.052260913 = queryNorm
            0.27674085 = fieldWeight in 2516, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              2.892262 = idf(docFreq=6664, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2516)
      0.2 = coord(1/5)
    
    Abstract
    This article focuses on two aspects of knowledge generation. First, I want to explore how new knowledge is created in interdisciplinary discourses and, second, how this process might be mediated and promoted by the use of wikis. I suggest that it is the noise coming to life in (ex)changes of perspectives that enables the creation of new knowledge. In section 1-4, I am going to examine how the concepts of noise from the mathematical theory of communication (Shannon 1948) on the one hand and theories of organizational knowledge creation (cf. Nonaka 1994) on the other might help to understand the process of interdisciplinary knowledge creation. In section 5 I am going to explore the role wiki technologies can play in supporting interdisciplinary collaborations. This section is influenced by own experiences in a wiki-based interdisciplinary collaboration. It seems that even though certain features of wiki technology make it an excellent tool to externalize and combine individual knowledge leaving room for noise and at the same time documenting this process, the full benefit of wikis can only be obtained if they are embedded into a broader communication context.
  14. Ding, Y.; Jacob, E.K.; Zhang, Z.; Foo, S.; Yan, E.; George, N.L.; Guo, L.: Perspectives on social tagging (2009) 0.01
    0.008196974 = product of:
      0.04098487 = sum of:
        0.04098487 = weight(_text_:it in 3290) [ClassicSimilarity], result of:
          0.04098487 = score(doc=3290,freq=4.0), product of:
            0.15115225 = queryWeight, product of:
              2.892262 = idf(docFreq=6664, maxDocs=44218)
              0.052260913 = queryNorm
            0.27114958 = fieldWeight in 3290, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.892262 = idf(docFreq=6664, maxDocs=44218)
              0.046875 = fieldNorm(doc=3290)
      0.2 = coord(1/5)
    
    Abstract
    Social tagging is one of the major phenomena transforming the World Wide Web from a static platform into an actively shared information space. This paper addresses various aspects of social tagging, including different views on the nature of social tagging, how to make use of social tags, and how to bridge social tagging with other Web functionalities; it discusses the use of facets to facilitate browsing and searching of tagging data; and it presents an analogy between bibliometrics and tagometrics, arguing that established bibliometric methodologies can be applied to analyze tagging behavior on the Web. Based on the Upper Tag Ontology (UTO), a Web crawler was built to harvest tag data from Delicious, Flickr, and YouTube in September 2007. In total, 1.8 million objects, including bookmarks, photos, and videos, 3.1 million taggers, and 12.1 million tags were collected and analyzed. Some tagging patterns and variations are identified and discussed.
  15. Naderi, H.; Rumpler, B.: PERCIRS: a system to combine personalized and collaborative information retrieval (2010) 0.01
    0.0077281813 = product of:
      0.038640905 = sum of:
        0.038640905 = weight(_text_:it in 3960) [ClassicSimilarity], result of:
          0.038640905 = score(doc=3960,freq=8.0), product of:
            0.15115225 = queryWeight, product of:
              2.892262 = idf(docFreq=6664, maxDocs=44218)
              0.052260913 = queryNorm
            0.25564227 = fieldWeight in 3960, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              2.892262 = idf(docFreq=6664, maxDocs=44218)
              0.03125 = fieldNorm(doc=3960)
      0.2 = coord(1/5)
    
    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.
  16. Yoon, J.W.: Towards a user-oriented thesaurus for non-domain-specific image collections (2009) 0.01
    0.0077281813 = product of:
      0.038640905 = sum of:
        0.038640905 = weight(_text_:it in 4221) [ClassicSimilarity], result of:
          0.038640905 = score(doc=4221,freq=8.0), product of:
            0.15115225 = queryWeight, product of:
              2.892262 = idf(docFreq=6664, maxDocs=44218)
              0.052260913 = queryNorm
            0.25564227 = fieldWeight in 4221, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              2.892262 = idf(docFreq=6664, maxDocs=44218)
              0.03125 = fieldNorm(doc=4221)
      0.2 = coord(1/5)
    
    Abstract
    This study explored how user-supplied tags can be applied to designing a thesaurus that reflects the unique features of image documents. Tags from the popular image-sharing Web site Flickr were examined in terms of two central components of a thesaurus-selected concepts and their semantic relations-as well as the features of image documents. Shatford's facet category and Rosch et al.'s basic-level theory were adopted for examining concepts to be included in a thesaurus. The results suggested that the best approach to Color and Generic category descriptors is to focus on basic-level terms and to include frequently used superordinate- and subordinate-level terms. In the Abstract category, it was difficult to specify a set of abstract terms that can be used consistently and dominantly, so it was suggested to enhance browsability using hierarchical and associative relations. Study results also indicate a need for greater inclusion of Specific category terms, which were shown to be an important tool in establishing related tags. Regarding semantic relations, the study indicated that in the identification of related terms, it is important that descriptors not be limited only to the category in which a main entry belongs but broadened to include terms from other categories as well. Although future studies are needed to ensure the effectiveness of this user-oriented approach, this study yielded promising results, demonstrating that user-supplied tags can be a helpful tool in selecting concepts to be included in a thesaurus and in identifying semantic relations among the selected concepts. It is hoped that the results of this study will provide a practical guideline for designing a thesaurus for image documents that takes into account both the unique features of these documents and the unique information-seeking behaviors of general users.
  17. Chen, M.; Liu, X.; Qin, J.: Semantic relation extraction from socially-generated tags : a methodology for metadata generation (2008) 0.01
    0.0070806327 = product of:
      0.035403162 = sum of:
        0.035403162 = weight(_text_:22 in 2648) [ClassicSimilarity], result of:
          0.035403162 = score(doc=2648,freq=2.0), product of:
            0.18300882 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052260913 = 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)
      0.2 = coord(1/5)
    
    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
  18. Kim, H.L.; Scerri, S.; Breslin, J.G.; Decker, S.; Kim, H.G.: ¬The state of the art in tag ontologies : a semantic model for tagging and folksonomies (2008) 0.01
    0.0070806327 = product of:
      0.035403162 = sum of:
        0.035403162 = weight(_text_:22 in 2650) [ClassicSimilarity], result of:
          0.035403162 = score(doc=2650,freq=2.0), product of:
            0.18300882 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052260913 = 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)
      0.2 = coord(1/5)
    
    Source
    Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas
  19. Yi, K.: Harnessing collective intelligence in social tagging using Delicious (2012) 0.01
    0.0070806327 = product of:
      0.035403162 = sum of:
        0.035403162 = weight(_text_:22 in 515) [ClassicSimilarity], result of:
          0.035403162 = score(doc=515,freq=2.0), product of:
            0.18300882 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052260913 = 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)
      0.2 = coord(1/5)
    
    Date
    25.12.2012 15:22:37
  20. Knautz, K.; Stock, W.G.: Collective indexing of emotions in videos (2011) 0.01
    0.006830811 = product of:
      0.034154054 = sum of:
        0.034154054 = weight(_text_:it in 295) [ClassicSimilarity], result of:
          0.034154054 = score(doc=295,freq=4.0), product of:
            0.15115225 = queryWeight, product of:
              2.892262 = idf(docFreq=6664, maxDocs=44218)
              0.052260913 = queryNorm
            0.22595796 = fieldWeight in 295, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.892262 = idf(docFreq=6664, maxDocs=44218)
              0.0390625 = fieldNorm(doc=295)
      0.2 = coord(1/5)
    
    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.

Years

Languages

  • e 44
  • d 3
  • More… Less…

Types

  • a 42
  • el 7
  • b 2
  • m 1
  • More… Less…