Search (40 results, page 1 of 2)

0.047288667 = product of:
  0.141866 = sum of:
    0.141866 = weight(_text_:index in 4100) [ClassicSimilarity], result of:
      0.141866 = score(doc=4100,freq=14.0), product of:
        0.2221244 = queryWeight, product of:
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.05083213 = queryNorm
        0.63867813 = fieldWeight in 4100, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4100)
  0.33333334 = coord(1/3)

Abstract

Web 2.0 and social/collaborative tagging have altered the traditional roles of indexer and user. Traditional indexing tools and systems assume the top-down approach to indexing in which a trained professional is responsible for assigning index terms to information sources with a potential user in mind. However, in today's Web, end users create, organize, index, and search for images and other information sources through social tagging and other collaborative activities. One of the impediments to user-centered indexing had been the cost of soliciting user-generated index terms or tags. Social tagging of images such as those on Flickr, an online photo management and sharing application, presents an opportunity that can be seized by designers of indexing tools and systems to bridge the semantic gap between indexer terms and user vocabularies. Empirical research on the differences and similarities between user-generated tags and index terms based on controlled vocabularies has the potential to inform future design of image indexing tools and systems. Toward this end, a random sample of Flickr images and the tags assigned to them were content analyzed and compared with another sample of index terms from a general image collection using established frameworks for image attributes and contents. The results show that there is a fundamental difference between the types of tags and types of index terms used. In light of this, implications for research into and design of user-centered image indexing tools and systems are discussed.

0.04722529 = product of:
  0.07083793 = sum of:
    0.0536203 = weight(_text_:index in 1291) [ClassicSimilarity], result of:
      0.0536203 = score(doc=1291,freq=2.0), product of:
        0.2221244 = queryWeight, product of:
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.05083213 = queryNorm
        0.24139762 = fieldWeight in 1291, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1291)
    0.01721763 = product of:
      0.03443526 = sum of:
        0.03443526 = weight(_text_:22 in 1291) [ClassicSimilarity], result of:
          0.03443526 = score(doc=1291,freq=2.0), product of:
            0.17800546 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05083213 = queryNorm
            0.19345059 = fieldWeight in 1291, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1291)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

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".

0.039338276 = product of:
  0.059007414 = sum of:
    0.04289624 = weight(_text_:index in 533) [ClassicSimilarity], result of:
      0.04289624 = score(doc=533,freq=2.0), product of:
        0.2221244 = queryWeight, product of:
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.05083213 = queryNorm
        0.1931181 = fieldWeight in 533, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.03125 = fieldNorm(doc=533)
    0.016111175 = product of:
      0.03222235 = sum of:
        0.03222235 = weight(_text_:classification in 533) [ClassicSimilarity], result of:
          0.03222235 = score(doc=533,freq=4.0), product of:
            0.16188543 = queryWeight, product of:
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.05083213 = queryNorm
            0.19904417 = fieldWeight in 533, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.03125 = fieldNorm(doc=533)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

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.

0.017873434 = product of:
  0.0536203 = sum of:
    0.0536203 = weight(_text_:index in 4064) [ClassicSimilarity], result of:
      0.0536203 = score(doc=4064,freq=2.0), product of:
        0.2221244 = queryWeight, product of:
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.05083213 = queryNorm
        0.24139762 = fieldWeight in 4064, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4064)
  0.33333334 = coord(1/3)

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.

0.017873434 = product of:
  0.0536203 = sum of:
    0.0536203 = weight(_text_:index in 295) [ClassicSimilarity], result of:
      0.0536203 = score(doc=295,freq=2.0), product of:
        0.2221244 = queryWeight, product of:
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.05083213 = queryNorm
        0.24139762 = fieldWeight in 295, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.0390625 = fieldNorm(doc=295)
  0.33333334 = coord(1/3)

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.

0.01151038 = product of:
  0.03453114 = sum of:
    0.03453114 = product of:
      0.06906228 = sum of:
        0.06906228 = weight(_text_:classification in 3529) [ClassicSimilarity], result of:
          0.06906228 = score(doc=3529,freq=6.0), product of:
            0.16188543 = queryWeight, product of:
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.05083213 = queryNorm
            0.42661208 = fieldWeight in 3529, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3529)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

Describes three units of time helpful for understanding and evaluating classificatory structures: long time (versions and states of classification schemes), short time (the act of indexing as repeated ritual or form), and micro-time (where stages of the interpretation process of indexing are separated out and inventoried). Concludes with a short discussion of how time and the impermanence of classification also conjures up an artistic conceptualization of indexing, and briefly uses that to question the seemingly dominant understanding of classification practice as outcome of scientific management and assembly line thought.

0.009493601 = product of:
  0.028480802 = sum of:
    0.028480802 = product of:
      0.056961603 = sum of:
        0.056961603 = weight(_text_:classification in 3452) [ClassicSimilarity], result of:
          0.056961603 = score(doc=3452,freq=8.0), product of:
            0.16188543 = queryWeight, product of:
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.05083213 = queryNorm
            0.35186368 = fieldWeight in 3452, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3452)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

With the emergence of Web 2.0, sharing personal content, communicating ideas, and interacting with other online users in Web 2.0 communities have become daily routines for online users. User-generated data from Web 2.0 sites provide rich personal information (e.g., personal preferences and interests) and can be utilized to obtain insight about cyber communities and their social networks. Many studies have focused on leveraging user-generated information to analyze blogs and forums, but few studies have applied this approach to video-sharing Web sites. In this study, we propose a text-based framework for video content classification of online-video sharing Web sites. Different types of user-generated data (e.g., titles, descriptions, and comments) were used as proxies for online videos, and three types of text features (lexical, syntactic, and content-specific features) were extracted. Three feature-based classification techniques (C4.5, Naïve Bayes, and Support Vector Machine) were used to classify videos. To evaluate the proposed framework, user-generated data from candidate videos, which were identified by searching user-given keywords on YouTube, were first collected. Then, a subset of the collected data was randomly selected and manually tagged by users as our experiment data. The experimental results showed that the proposed approach was able to classify online videos based on users' interests with accuracy rates up to 87.2%, and all three types of text features contributed to discriminating videos. Support Vector Machine outperformed C4.5 and Naïve Bayes techniques in our experiments. In addition, our case study further demonstrated that accurate video-classification results are very useful for identifying implicit cyber communities on video-sharing Web sites.

0.009493601 = product of:
  0.028480802 = sum of:
    0.028480802 = product of:
      0.056961603 = sum of:
        0.056961603 = weight(_text_:classification in 1806) [ClassicSimilarity], result of:
          0.056961603 = score(doc=1806,freq=2.0), product of:
            0.16188543 = queryWeight, product of:
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.05083213 = queryNorm
            0.35186368 = fieldWeight in 1806, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.078125 = fieldNorm(doc=1806)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Source

Subject access: preparing for the future. Conference on August 20 - 21, 2009 in Florence, the IFLA Classification and Indexing Section sponsored an IFLA satellite conference entitled "Looking at the Past and Preparing for the Future". Eds.: P. Landry et al

0.009493601 = product of:
  0.028480802 = sum of:
    0.028480802 = product of:
      0.056961603 = sum of:
        0.056961603 = weight(_text_:classification in 1807) [ClassicSimilarity], result of:
          0.056961603 = score(doc=1807,freq=2.0), product of:
            0.16188543 = queryWeight, product of:
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.05083213 = queryNorm
            0.35186368 = fieldWeight in 1807, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.078125 = fieldNorm(doc=1807)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Source

Subject access: preparing for the future. Conference on August 20 - 21, 2009 in Florence, the IFLA Classification and Indexing Section sponsored an IFLA satellite conference entitled "Looking at the Past and Preparing for the Future". Eds.: P. Landry et al

0.009182736 = product of:
  0.027548207 = sum of:
    0.027548207 = product of:
      0.055096414 = sum of:
        0.055096414 = weight(_text_:22 in 2882) [ClassicSimilarity], result of:
          0.055096414 = score(doc=2882,freq=2.0), product of:
            0.17800546 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05083213 = 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)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Pages

S.15-22

0.0082217 = product of:
  0.024665099 = sum of:
    0.024665099 = product of:
      0.049330197 = sum of:
        0.049330197 = weight(_text_:classification in 2059) [ClassicSimilarity], result of:
          0.049330197 = score(doc=2059,freq=6.0), product of:
            0.16188543 = queryWeight, product of:
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.05083213 = queryNorm
            0.3047229 = fieldWeight in 2059, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2059)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

The goal of the study presented in this article is to investigate to what extent the classification of a web page by a single genre matches the users' perspective. The extent of agreement on a single genre label for a web page can help understand whether there is a need for a different classification scheme that overrides the single-genre labelling. My hypothesis is that a single genre label does not account for the users' perspective. In order to test this hypothesis, I submitted a restricted number of web pages (25 web pages) to a large number of web users (135 subjects) asking them to assign only a single genre label to each of the web pages. Users could choose from a list of 21 genre labels, or select one of the two 'escape' options, i.e. 'Add a label' and 'I don't know'. The rationale was to observe the level of agreement on a single genre label per web page, and draw some conclusions about the appropriateness of limiting the assignment to only a single label when doing genre classification of web pages. Results show that users largely disagree on the label to be assigned to a web page.

0.008116469 = product of:
  0.024349404 = sum of:
    0.024349404 = product of:
      0.04869881 = sum of:
        0.04869881 = weight(_text_:22 in 2652) [ClassicSimilarity], result of:
          0.04869881 = score(doc=2652,freq=4.0), product of:
            0.17800546 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05083213 = queryNorm
            0.27358043 = fieldWeight in 2652, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2652)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

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

0.008034894 = product of:
  0.02410468 = sum of:
    0.02410468 = product of:
      0.04820936 = sum of:
        0.04820936 = weight(_text_:22 in 2889) [ClassicSimilarity], result of:
          0.04820936 = score(doc=2889,freq=2.0), product of:
            0.17800546 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05083213 = 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)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

21. 6.2009 12:22:44

0.0068870517 = product of:
  0.020661155 = sum of:
    0.020661155 = product of:
      0.04132231 = sum of:
        0.04132231 = weight(_text_:22 in 3387) [ClassicSimilarity], result of:
          0.04132231 = score(doc=3387,freq=2.0), product of:
            0.17800546 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05083213 = queryNorm
            0.23214069 = fieldWeight in 3387, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=3387)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

1. 8.2010 12:35:22

0.0068870517 = product of:
  0.020661155 = sum of:
    0.020661155 = product of:
      0.04132231 = sum of:
        0.04132231 = weight(_text_:22 in 3601) [ClassicSimilarity], result of:
          0.04132231 = score(doc=3601,freq=2.0), product of:
            0.17800546 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05083213 = queryNorm
            0.23214069 = fieldWeight in 3601, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=3601)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

10. 9.2000 17:38:22

0.0068870517 = product of:
  0.020661155 = sum of:
    0.020661155 = product of:
      0.04132231 = sum of:
        0.04132231 = weight(_text_:22 in 3602) [ClassicSimilarity], result of:
          0.04132231 = score(doc=3602,freq=2.0), product of:
            0.17800546 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05083213 = 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)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

10. 9.2000 17:38:22

0.0068870517 = product of:
  0.020661155 = sum of:
    0.020661155 = product of:
      0.04132231 = sum of:
        0.04132231 = weight(_text_:22 in 164) [ClassicSimilarity], result of:
          0.04132231 = score(doc=164,freq=2.0), product of:
            0.17800546 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05083213 = 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)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

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

0.0067129894 = product of:
  0.020138968 = sum of:
    0.020138968 = product of:
      0.040277936 = sum of:
        0.040277936 = weight(_text_:classification in 2918) [ClassicSimilarity], result of:
          0.040277936 = score(doc=2918,freq=4.0), product of:
            0.16188543 = queryWeight, product of:
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.05083213 = queryNorm
            0.24880521 = fieldWeight in 2918, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2918)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

Purpose - The purpose of this paper is to explore the potential of applying the Dewey Decimal Classification (DDC) as an established knowledge organization system (KOS) for enhancing social tagging, with the ultimate purpose of improving subject indexing and information retrieval. Design/methodology/approach - Over 11.000 Intute metadata records in politics were used. Totally, 28 politics students were each given four tasks, in which a total of 60 resources were tagged in two different configurations, one with uncontrolled social tags only and another with uncontrolled social tags as well as suggestions from a controlled vocabulary. The controlled vocabulary was DDC comprising also mappings from the Library of Congress Subject Headings. Findings - The results demonstrate the importance of controlled vocabulary suggestions for indexing and retrieval: to help produce ideas of which tags to use, to make it easier to find focus for the tagging, to ensure consistency and to increase the number of access points in retrieval. The value and usefulness of the suggestions proved to be dependent on the quality of the suggestions, both as to conceptual relevance to the user and as to appropriateness of the terminology. Originality/value - No research has investigated the enhancement of social tagging with suggestions from the DDC, an established KOS, in a user trial, comparing social tagging only and social tagging enhanced with the suggestions. This paper is a final reflection on all aspects of the study.

0.0066455207 = product of:
  0.019936562 = sum of:
    0.019936562 = product of:
      0.039873123 = sum of:
        0.039873123 = weight(_text_:classification in 659) [ClassicSimilarity], result of:
          0.039873123 = score(doc=659,freq=2.0), product of:
            0.16188543 = queryWeight, product of:
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.05083213 = queryNorm
            0.24630459 = fieldWeight in 659, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.0546875 = fieldNorm(doc=659)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

Marginalia or 'scribbling in the margins' is a means for readers to add a more in-depth level of granularity and subject representation to digital documents such as those present in social sharing environments like Flickr and del.icio.us. Social classification and social sharing sites development of user-defined descriptors or tags is discussed in the context of knowledge organization. With this position paper I present a rationale for the use of the resulting folksonomies and tag clouds being developed in these social sharing communities as a rich source of information about our users and their natural organization processes. The knowledge organization community needs to critically examine our understandings of these emerging classificatory schema and determine how best to adapt, augment, revitalize existing knowledge organization structures.

0.0066455207 = product of:
  0.019936562 = sum of:
    0.019936562 = product of:
      0.039873123 = sum of:
        0.039873123 = weight(_text_:classification in 3561) [ClassicSimilarity], result of:
          0.039873123 = score(doc=3561,freq=2.0), product of:
            0.16188543 = queryWeight, product of:
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.05083213 = queryNorm
            0.24630459 = fieldWeight in 3561, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3561)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Source

Cataloging and classification quarterly. 48(2010) no.1, S.94-109