Search (2413 results, page 1 of 121)

0.16421914 = product of:
  0.32843828 = sum of:
    0.32843828 = sum of:
      0.3010879 = weight(_text_:tagging in 2657) [ClassicSimilarity], result of:
        0.3010879 = score(doc=2657,freq=30.0), product of:
          0.2979515 = queryWeight, product of:
            5.9038734 = idf(docFreq=327, maxDocs=44218)
            0.05046712 = queryNorm
          1.0105265 = fieldWeight in 2657, product of:
            5.477226 = tf(freq=30.0), with freq of:
              30.0 = termFreq=30.0
            5.9038734 = idf(docFreq=327, maxDocs=44218)
            0.03125 = fieldNorm(doc=2657)
      0.027350392 = weight(_text_:22 in 2657) [ClassicSimilarity], result of:
        0.027350392 = score(doc=2657,freq=2.0), product of:
          0.17672725 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05046712 = queryNorm
          0.15476047 = fieldWeight in 2657, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.03125 = fieldNorm(doc=2657)
  0.5 = coord(1/2)

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

Theme

Social tagging

0.16285753 = product of:
  0.32571507 = sum of:
    0.32571507 = sum of:
      0.29152706 = weight(_text_:tagging in 5492) [ClassicSimilarity], result of:
        0.29152706 = score(doc=5492,freq=18.0), product of:
          0.2979515 = queryWeight, product of:
            5.9038734 = idf(docFreq=327, maxDocs=44218)
            0.05046712 = queryNorm
          0.97843796 = fieldWeight in 5492, product of:
            4.2426405 = tf(freq=18.0), with freq of:
              18.0 = termFreq=18.0
            5.9038734 = idf(docFreq=327, maxDocs=44218)
            0.0390625 = fieldNorm(doc=5492)
      0.03418799 = weight(_text_:22 in 5492) [ClassicSimilarity], result of:
        0.03418799 = score(doc=5492,freq=2.0), product of:
          0.17672725 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05046712 = queryNorm
          0.19345059 = fieldWeight in 5492, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=5492)
  0.5 = coord(1/2)

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

Theme

Social tagging

0.14564537 = product of:
  0.29129073 = sum of:
    0.29129073 = sum of:
      0.25710273 = weight(_text_:tagging in 515) [ClassicSimilarity], result of:
        0.25710273 = score(doc=515,freq=14.0), product of:
          0.2979515 = queryWeight, product of:
            5.9038734 = idf(docFreq=327, maxDocs=44218)
            0.05046712 = queryNorm
          0.8629013 = fieldWeight in 515, product of:
            3.7416575 = tf(freq=14.0), with freq of:
              14.0 = termFreq=14.0
            5.9038734 = idf(docFreq=327, maxDocs=44218)
            0.0390625 = fieldNorm(doc=515)
      0.03418799 = weight(_text_:22 in 515) [ClassicSimilarity], result of:
        0.03418799 = score(doc=515,freq=2.0), product of:
          0.17672725 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05046712 = queryNorm
          0.19345059 = fieldWeight in 515, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=515)
  0.5 = coord(1/2)

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

Theme

Social tagging

0.1365938 = product of:
  0.2731876 = sum of:
    0.2731876 = sum of:
      0.24583721 = weight(_text_:tagging in 2881) [ClassicSimilarity], result of:
        0.24583721 = score(doc=2881,freq=20.0), product of:
          0.2979515 = queryWeight, product of:
            5.9038734 = idf(docFreq=327, maxDocs=44218)
            0.05046712 = queryNorm
          0.8250914 = fieldWeight in 2881, product of:
            4.472136 = tf(freq=20.0), with freq of:
              20.0 = termFreq=20.0
            5.9038734 = idf(docFreq=327, maxDocs=44218)
            0.03125 = fieldNorm(doc=2881)
      0.027350392 = weight(_text_:22 in 2881) [ClassicSimilarity], result of:
        0.027350392 = score(doc=2881,freq=2.0), product of:
          0.17672725 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05046712 = 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)
  0.5 = coord(1/2)

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

Footnote

Vorbemerkung zu den Beiträgen der Tagung "Social Tagging in der Wissensorganisation" am 21.-22.02.2008 am Institut für Wissensmedien (IWM) in Tübingen.

Source

Good tags - bad tags: Social Tagging in der Wissensorganisation. Hrsg.: B. Gaiser, u.a

Theme

Social tagging

0.13610941 = product of:
  0.27221882 = sum of:
    0.27221882 = sum of:
      0.23803084 = weight(_text_:tagging in 2650) [ClassicSimilarity], result of:
        0.23803084 = score(doc=2650,freq=12.0), product of:
          0.2979515 = queryWeight, product of:
            5.9038734 = idf(docFreq=327, maxDocs=44218)
            0.05046712 = queryNorm
          0.79889125 = fieldWeight in 2650, product of:
            3.4641016 = tf(freq=12.0), with freq of:
              12.0 = termFreq=12.0
            5.9038734 = idf(docFreq=327, maxDocs=44218)
            0.0390625 = fieldNorm(doc=2650)
      0.03418799 = weight(_text_:22 in 2650) [ClassicSimilarity], result of:
        0.03418799 = score(doc=2650,freq=2.0), product of:
          0.17672725 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05046712 = 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)

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

Theme

Social tagging

0.10994174 = product of:
  0.21988349 = sum of:
    0.21988349 = product of:
      0.43976697 = sum of:
        0.43976697 = weight(_text_:tagging in 7382) [ClassicSimilarity], result of:
          0.43976697 = score(doc=7382,freq=4.0), product of:
            0.2979515 = queryWeight, product of:
              5.9038734 = idf(docFreq=327, maxDocs=44218)
              0.05046712 = queryNorm
            1.4759684 = fieldWeight in 7382, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.9038734 = idf(docFreq=327, maxDocs=44218)
              0.125 = fieldNorm(doc=7382)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Theme

Social tagging

0.10833117 = product of:
  0.21666235 = sum of:
    0.21666235 = sum of:
      0.16831324 = weight(_text_:tagging in 2652) [ClassicSimilarity], result of:
        0.16831324 = score(doc=2652,freq=6.0), product of:
          0.2979515 = queryWeight, product of:
            5.9038734 = idf(docFreq=327, maxDocs=44218)
            0.05046712 = queryNorm
          0.5649015 = fieldWeight in 2652, product of:
            2.4494898 = tf(freq=6.0), with freq of:
              6.0 = termFreq=6.0
            5.9038734 = idf(docFreq=327, maxDocs=44218)
            0.0390625 = fieldNorm(doc=2652)
      0.048349116 = weight(_text_:22 in 2652) [ClassicSimilarity], result of:
        0.048349116 = score(doc=2652,freq=4.0), product of:
          0.17672725 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05046712 = 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)

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

Theme

Social tagging

0.10833117 = product of:
  0.21666235 = sum of:
    0.21666235 = sum of:
      0.16831324 = weight(_text_:tagging in 1451) [ClassicSimilarity], result of:
        0.16831324 = score(doc=1451,freq=6.0), product of:
          0.2979515 = queryWeight, product of:
            5.9038734 = idf(docFreq=327, maxDocs=44218)
            0.05046712 = queryNorm
          0.5649015 = fieldWeight in 1451, product of:
            2.4494898 = tf(freq=6.0), with freq of:
              6.0 = termFreq=6.0
            5.9038734 = idf(docFreq=327, maxDocs=44218)
            0.0390625 = fieldNorm(doc=1451)
      0.048349116 = weight(_text_:22 in 1451) [ClassicSimilarity], result of:
        0.048349116 = score(doc=1451,freq=4.0), product of:
          0.17672725 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05046712 = queryNorm
          0.27358043 = fieldWeight in 1451, 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=1451)
  0.5 = coord(1/2)

Abstract

Previous studies of user-generated vocabularies (e.g., Golder & Huberman, 2006; Munk & Mork, 2007b; Yoon, 2009) have proposed that a primary source of tag agreement across users is due to wide-spread use of tags at the basic level of abstraction. However, an investigation of levels of abstraction in user-generated tagging vocabularies did not support this notion. This study analyzed approximately 8000 tags generated by 40 subjects. Analysis of 7617 tags assigned to 36 online resources representing four content categories (TOOL, FRUIT, CLOTHING, VEHICLE) and three resource genres (news article, blog, ecommerce) did not find statistically significant preferences in the assignment of tags at the superordinate, subordinate or basic levels of abstraction. Within the framework of Heidegger's (1953/1996) notion of handiness , observed variations in the preferred level of abstraction are both natural and phenomenological in that perception and understanding -- and thus the meaning of "things" -- arise out of the individual's contextualized experiences of engaging with objects. Operationalization of superordinate, subordinate and basic levels of abstraction using Heidegger's notion of handiness may be able to account for differences in the everyday experiences and activities of taggers, thereby leading to a better understanding of user-generated tagging vocabularies.

Date

5. 9.2014 16:22:27

Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

0.10755379 = product of:
  0.21510758 = sum of:
    0.21510758 = product of:
      0.43021515 = sum of:
        0.43021515 = weight(_text_:tagging in 2294) [ClassicSimilarity], result of:
          0.43021515 = score(doc=2294,freq=20.0), product of:
            0.2979515 = queryWeight, product of:
              5.9038734 = idf(docFreq=327, maxDocs=44218)
              0.05046712 = queryNorm
            1.44391 = fieldWeight in 2294, product of:
              4.472136 = tf(freq=20.0), with freq of:
                20.0 = termFreq=20.0
              5.9038734 = idf(docFreq=327, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2294)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Purpose: Investigate the combination of controlled and folksonomy approaches to support resource discovery in repositories and digital collections. Aim: Investigate whether use of an established controlled vocabulary can help improve social tagging for better resource discovery. Objectives: (1) Investigate indexing aspects when using only social tagging versus when using social tagging with suggestions from a controlled vocabulary; (2) Investigate above in two different contexts: tagging by readers and tagging by authors; (3) Investigate influence of only social tagging versus social tagging with a controlled vocabulary on retrieval. - Vgl.: http://www.ukoln.ac.uk/projects/enhanced-tagging/.

Theme

Social tagging

0.10755379 = product of:
  0.21510758 = sum of:
    0.21510758 = product of:
      0.43021515 = sum of:
        0.43021515 = weight(_text_:tagging in 4647) [ClassicSimilarity], result of:
          0.43021515 = score(doc=4647,freq=20.0), product of:
            0.2979515 = queryWeight, product of:
              5.9038734 = idf(docFreq=327, maxDocs=44218)
              0.05046712 = queryNorm
            1.44391 = fieldWeight in 4647, product of:
              4.472136 = tf(freq=20.0), with freq of:
                20.0 = termFreq=20.0
              5.9038734 = idf(docFreq=327, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4647)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

This article examines tagging as knowledge organization. Tagging is a kind of indexing, a process of labelling and categorizing information made to support resource discovery for users. Social tagging generally means the practice whereby internet users generate keywords to describe, categorise or comment on digital content. The value of tagging comes when social tags within a collection are aggregated and shared through a folksonomy. This article examines definitions of tagging and folksonomy, and discusses the functions, advantages and disadvantages of tagging systems in relation to knowledge organization before discussing studies that have compared tagging and conventional library-based knowledge organization systems. Approaches to disciplining tagging practice are examined and tagger motivation discussed. Finally, the article outlines current research fronts.

Theme

Social tagging

0.10589475 = product of:
  0.2117895 = sum of:
    0.2117895 = product of:
      0.423579 = sum of:
        0.423579 = weight(_text_:tagging in 1091) [ClassicSimilarity], result of:
          0.423579 = score(doc=1091,freq=38.0), product of:
            0.2979515 = queryWeight, product of:
              5.9038734 = idf(docFreq=327, maxDocs=44218)
              0.05046712 = queryNorm
            1.4216375 = fieldWeight in 1091, product of:
              6.164414 = tf(freq=38.0), with freq of:
                38.0 = termFreq=38.0
              5.9038734 = idf(docFreq=327, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1091)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The number of research studies on social tagging has increased rapidly in the past years, but few of them highlight the characteristics and research trends in social tagging. A set of 862 academic documents relating to social tagging and published from 2005 to 2011 was thus examined using bibliometric analysis as well as the social network analysis technique. The results show that social tagging, as a research area, develops rapidly and attracts an increasing number of new entrants. There are no key authors, publication sources, or research groups that dominate the research domain of social tagging. Research on social tagging appears to focus mainly on the following three aspects: (a) components and functions of social tagging (e.g., tags, tagging objects, and tagging network), (b) taggers' behaviors and interface design, and (c) tags' organization and usage in social tagging. The trend suggest that more researchers turn to the latter two integrated with human computer interface and information retrieval, although the first aspect is the fundamental one in social tagging. Also, more studies relating to social tagging pay attention to multimedia tagging objects and not only text tagging. Previous research on social tagging was limited to a few subject domains such as information science and computer science. As an interdisciplinary research area, social tagging is anticipated to attract more researchers from different disciplines. More practical applications, especially in high-tech companies, is an encouraging research trend in social tagging.

Theme

Social tagging

0.105090946 = product of:
  0.21018189 = sum of:
    0.21018189 = sum of:
      0.1554811 = weight(_text_:tagging in 695) [ClassicSimilarity], result of:
        0.1554811 = score(doc=695,freq=2.0), product of:
          0.2979515 = queryWeight, product of:
            5.9038734 = idf(docFreq=327, maxDocs=44218)
            0.05046712 = queryNorm
          0.5218336 = fieldWeight in 695, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            5.9038734 = idf(docFreq=327, maxDocs=44218)
            0.0625 = fieldNorm(doc=695)
      0.054700784 = weight(_text_:22 in 695) [ClassicSimilarity], result of:
        0.054700784 = score(doc=695,freq=2.0), product of:
          0.17672725 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05046712 = queryNorm
          0.30952093 = fieldWeight in 695, 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=695)
  0.5 = coord(1/2)

Date

6. 3.1997 16:22:15

0.10125061 = product of:
  0.20250122 = sum of:
    0.20250122 = sum of:
      0.16831324 = weight(_text_:tagging in 2891) [ClassicSimilarity], result of:
        0.16831324 = score(doc=2891,freq=6.0), product of:
          0.2979515 = queryWeight, product of:
            5.9038734 = idf(docFreq=327, maxDocs=44218)
            0.05046712 = queryNorm
          0.5649015 = fieldWeight in 2891, product of:
            2.4494898 = tf(freq=6.0), with freq of:
              6.0 = termFreq=6.0
            5.9038734 = idf(docFreq=327, maxDocs=44218)
            0.0390625 = fieldNorm(doc=2891)
      0.03418799 = weight(_text_:22 in 2891) [ClassicSimilarity], result of:
        0.03418799 = score(doc=2891,freq=2.0), product of:
          0.17672725 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05046712 = 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)
  0.5 = coord(1/2)

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

Theme

Social tagging

0.10066795 = sum of:
  0.080155164 = product of:
    0.24046549 = sum of:
      0.24046549 = weight(_text_:3a in 562) [ClassicSimilarity], result of:
        0.24046549 = score(doc=562,freq=2.0), product of:
          0.4278608 = queryWeight, product of:
            8.478011 = idf(docFreq=24, maxDocs=44218)
            0.05046712 = queryNorm
          0.56201804 = fieldWeight in 562, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            8.478011 = idf(docFreq=24, maxDocs=44218)
            0.046875 = fieldNorm(doc=562)
    0.33333334 = coord(1/3)
  0.020512793 = product of:
    0.041025586 = sum of:
      0.041025586 = weight(_text_:22 in 562) [ClassicSimilarity], result of:
        0.041025586 = score(doc=562,freq=2.0), product of:
          0.17672725 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05046712 = queryNorm
          0.23214069 = fieldWeight in 562, 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=562)
    0.5 = coord(1/2)

Content

Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.

Date

8. 1.2013 10:22:32

0.08745811 = product of:
  0.17491622 = sum of:
    0.17491622 = product of:
      0.34983245 = sum of:
        0.34983245 = weight(_text_:tagging in 3290) [ClassicSimilarity], result of:
          0.34983245 = score(doc=3290,freq=18.0), product of:
            0.2979515 = queryWeight, product of:
              5.9038734 = idf(docFreq=327, maxDocs=44218)
              0.05046712 = queryNorm
            1.1741256 = fieldWeight in 3290, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              5.9038734 = idf(docFreq=327, maxDocs=44218)
              0.046875 = fieldNorm(doc=3290)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.

Theme

Social tagging

0.08745811 = product of:
  0.17491622 = sum of:
    0.17491622 = product of:
      0.34983245 = sum of:
        0.34983245 = weight(_text_:tagging in 309) [ClassicSimilarity], result of:
          0.34983245 = score(doc=309,freq=18.0), product of:
            0.2979515 = queryWeight, product of:
              5.9038734 = idf(docFreq=327, maxDocs=44218)
              0.05046712 = queryNorm
            1.1741256 = fieldWeight in 309, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              5.9038734 = idf(docFreq=327, maxDocs=44218)
              0.046875 = fieldNorm(doc=309)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

This paper describes a survey on how system vendors and libraries handled tagging in OPACs and discovery layers. Tags are user added subject metadata, also called folksonomies. This survey also investigated user behavior when they face the possibility to tag. The findings indicate that legacy/classic systems have no tagging capability. About 47% of the discovery tools provide tagging function. About 49% of the libraries that have a system with tagging capability have turned the tagging function on in their OPACs and discovery tools. Only 40% of the libraries that turned tagging on actually utilized user added subject metadata as access point to collections. Academic library users are less active in tagging than public library users.

Theme

Social tagging

0.085807584 = product of:
  0.17161517 = sum of:
    0.17161517 = sum of:
      0.13742718 = weight(_text_:tagging in 2648) [ClassicSimilarity], result of:
        0.13742718 = score(doc=2648,freq=4.0), product of:
          0.2979515 = queryWeight, product of:
            5.9038734 = idf(docFreq=327, maxDocs=44218)
            0.05046712 = queryNorm
          0.4612401 = fieldWeight in 2648, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            5.9038734 = idf(docFreq=327, maxDocs=44218)
            0.0390625 = fieldNorm(doc=2648)
      0.03418799 = weight(_text_:22 in 2648) [ClassicSimilarity], result of:
        0.03418799 = score(doc=2648,freq=2.0), product of:
          0.17672725 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05046712 = 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)

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

Theme

Social tagging

0.085807584 = product of:
  0.17161517 = sum of:
    0.17161517 = sum of:
      0.13742718 = weight(_text_:tagging in 2754) [ClassicSimilarity], result of:
        0.13742718 = score(doc=2754,freq=4.0), product of:
          0.2979515 = queryWeight, product of:
            5.9038734 = idf(docFreq=327, maxDocs=44218)
            0.05046712 = queryNorm
          0.4612401 = fieldWeight in 2754, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            5.9038734 = idf(docFreq=327, maxDocs=44218)
            0.0390625 = fieldNorm(doc=2754)
      0.03418799 = weight(_text_:22 in 2754) [ClassicSimilarity], result of:
        0.03418799 = score(doc=2754,freq=2.0), product of:
          0.17672725 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05046712 = queryNorm
          0.19345059 = fieldWeight in 2754, 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=2754)
  0.5 = coord(1/2)

Abstract

This article presents an exploratory study of Blobgects, an experimental interface for an online museum catalog that enables social tagging and blogging activity around a set of cultural heritage objects held by a preeminent museum of anthropology and archaeology. This study attempts to understand not just whether social tagging and commenting about these objects is useful but rather whose tags and voices matter in presenting different expert perspectives around digital museum objects. Based on an empirical comparison between two different user groups (Canadian Inuit high-school students and museum studies students in the United States), we found that merely adding the ability to tag and comment to the museum's catalog does not sufficiently allow users to learn about or engage with the objects represented by catalog entries. Rather, the specialist language of the catalog provides too little contextualization for users to enter into the sort of dialog that proponents of Web 2.0 technologies promise. Overall, we propose a more nuanced application of Web 2.0 technologies within museums - one which provides a contextual basis that gives users a starting point for engagement and permits users to make sense of objects in relation to their own needs, uses, and understandings.

Date

22. 3.2009 18:52:32

0.085807584 = product of:
  0.17161517 = sum of:
    0.17161517 = sum of:
      0.13742718 = weight(_text_:tagging in 1434) [ClassicSimilarity], result of:
        0.13742718 = score(doc=1434,freq=4.0), product of:
          0.2979515 = queryWeight, product of:
            5.9038734 = idf(docFreq=327, maxDocs=44218)
            0.05046712 = queryNorm
          0.4612401 = fieldWeight in 1434, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            5.9038734 = idf(docFreq=327, maxDocs=44218)
            0.0390625 = fieldNorm(doc=1434)
      0.03418799 = weight(_text_:22 in 1434) [ClassicSimilarity], result of:
        0.03418799 = score(doc=1434,freq=2.0), product of:
          0.17672725 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05046712 = queryNorm
          0.19345059 = fieldWeight in 1434, 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=1434)
  0.5 = coord(1/2)

Abstract

Knowledge organization of large-scale content on the Web requires substantial amounts of semantic metadata that is expensive to generate manually. Recent developments in Web technologies have enabled any user to tag documents and other forms of content thereby generating metadata that could help organize knowledge. However, merely adding one or more tags to a document is highly inadequate to capture the aboutness of the document and thereby to support powerful semantic functions such as automatic classification, question answering or true semantic search and retrieval. This is true even when the tags used are labels from a well-designed classification system such as a thesaurus or taxonomy. There is a strong need to develop a semantic tagging mechanism with sufficient expressive power to capture the aboutness of each part of a document or dataset or multimedia content in order to enable applications that can benefit from knowledge organization on the Web. This article proposes a highly expressive mechanism of using ontology snippets as semantic tags that map portions of a document or a part of a dataset or a segment of a multimedia content to concepts and relations in an ontology of the domain(s) of interest.

Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

0.08415662 = product of:
  0.16831324 = sum of:
    0.16831324 = product of:
      0.33662647 = sum of:
        0.33662647 = weight(_text_:tagging in 764) [ClassicSimilarity], result of:
          0.33662647 = score(doc=764,freq=24.0), product of:
            0.2979515 = queryWeight, product of:
              5.9038734 = idf(docFreq=327, maxDocs=44218)
              0.05046712 = queryNorm
            1.129803 = fieldWeight in 764, product of:
              4.8989797 = tf(freq=24.0), with freq of:
                24.0 = termFreq=24.0
              5.9038734 = idf(docFreq=327, maxDocs=44218)
              0.0390625 = fieldNorm(doc=764)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Purpose - The purpose of the paper is to provide an overview of the collaborative tagging phenomenon and explore some of the reasons for its emergence. Design/methodology/approach - The paper reviews the related literature and discusses some of the problems associated with, and the potential of, collaborative tagging approaches for knowledge organisation and general resource discovery. A definition of controlled vocabularies is proposed and used to assess the efficacy of collaborative tagging. An exposition of the collaborative tagging model is provided and a review of the major contributions to the tagging literature is presented. Findings - There are numerous difficulties with collaborative tagging systems (e.g. low precision, lack of collocation, etc.) that originate from the absence of properties that characterise controlled vocabularies. However, such systems can not be dismissed. Librarians and information professionals have lessons to learn from the interactive and social aspects exemplified by collaborative tagging systems, as well as their success in engaging users with information management. The future co-existence of controlled vocabularies and collaborative tagging is predicted, with each appropriate for use within distinct information contexts: formal and informal. Research limitations/implications - Librarians and information professional researchers should be playing a leading role in research aimed at assessing the efficacy of collaborative tagging in relation to information storage, organisation, and retrieval, and to influence the future development of collaborative tagging systems. Practical implications - The paper indicates clear areas where digital libraries and repositories could innovate in order to better engage users with information. Originality/value - At time of writing there were no literature reviews summarising the main contributions to the collaborative tagging research or debate.