Search (39 results, page 1 of 2)

  • × theme_ss:"Folksonomies"
  1. Catarino, M.E.; Baptista, A.A.: Relating folksonomies with Dublin Core (2008) 0.03
    0.031174384 = product of:
      0.046761576 = sum of:
        0.024946647 = weight(_text_:to in 2652) [ClassicSimilarity], result of:
          0.024946647 = score(doc=2652,freq=18.0), product of:
            0.08279609 = queryWeight, product of:
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.045541126 = queryNorm
            0.30130222 = fieldWeight in 2652, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2652)
        0.02181493 = product of:
          0.04362986 = sum of:
            0.04362986 = weight(_text_:22 in 2652) [ClassicSimilarity], result of:
              0.04362986 = score(doc=2652,freq=4.0), product of:
                0.15947726 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.045541126 = 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)
      0.6666667 = coord(2/3)
    
    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. Morrison, P.J.: Tagging and searching : search retrieval effectiveness of folksonomies on the World Wide Web (2008) 0.03
    0.025645267 = product of:
      0.0384679 = sum of:
        0.019957317 = weight(_text_:to in 2109) [ClassicSimilarity], result of:
          0.019957317 = score(doc=2109,freq=8.0), product of:
            0.08279609 = queryWeight, product of:
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.045541126 = queryNorm
            0.24104178 = fieldWeight in 2109, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.046875 = fieldNorm(doc=2109)
        0.018510582 = product of:
          0.037021164 = sum of:
            0.037021164 = weight(_text_:22 in 2109) [ClassicSimilarity], result of:
              0.037021164 = score(doc=2109,freq=2.0), product of:
                0.15947726 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.045541126 = queryNorm
                0.23214069 = fieldWeight in 2109, 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=2109)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    Abstract
    Many Web sites have begun allowing users to submit items to a collection and tag them with keywords. The folksonomies built from these tags are an interesting topic that has seen little empirical research. This study compared the search information retrieval (IR) performance of folksonomies from social bookmarking Web sites against search engines and subject directories. Thirty-four participants created 103 queries for various information needs. Results from each IR system were collected and participants judged relevance. Folksonomy search results overlapped with those from the other systems, and documents found by both search engines and folksonomies were significantly more likely to be judged relevant than those returned by any single IR system type. The search engines in the study had the highest precision and recall, but the folksonomies fared surprisingly well. Del.icio.us was statistically indistinguishable from the directories in many cases. Overall the directories were more precise than the folksonomies but they had similar recall scores. Better query handling may enhance folksonomy IR performance further. The folksonomies studied were promising, and may be able to improve Web search performance.
    Date
    1. 8.2008 12:39:22
  3. Wesch, M.: Information R/evolution (2006) 0.02
    0.022158299 = product of:
      0.033237446 = sum of:
        0.011641769 = weight(_text_:to in 1267) [ClassicSimilarity], result of:
          0.011641769 = score(doc=1267,freq=2.0), product of:
            0.08279609 = queryWeight, product of:
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.045541126 = queryNorm
            0.14060771 = fieldWeight in 1267, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1267)
        0.021595677 = product of:
          0.043191355 = sum of:
            0.043191355 = weight(_text_:22 in 1267) [ClassicSimilarity], result of:
              0.043191355 = score(doc=1267,freq=2.0), product of:
                0.15947726 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.045541126 = queryNorm
                0.2708308 = fieldWeight in 1267, 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=1267)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    Abstract
    This video explores the changes in the way we find, store, create, critique, and share information. This video was created as a conversation starter, and works especially well when brainstorming with people about the near future and the skills needed in order to harness, evaluate, and create information effectively. Ein sehr schöner Kurzfilm von Michael Wesch, dem wir auch den Beitrag zu Web 2.0 (The Machine is Us/ing Us) verdanken (vor einiger Zeit hier besprochen), thematisiert die Veränderung der Handhabung von Information (insbesondere die Strukturierung und Ordnung, aber auch die Generierung und Speicherung), die auf ihre digitale Gestalt zurückzuführen ist. Kernaussage: Da die Informationen keine physikalischen Beschränkungen mehr unterworfen sind, wird die Ordnung der Informationen vielfältiger, flexibler und für jedermann einfacher zugänglich.
    Date
    5. 1.2008 19:22:48
  4. 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) 0.02
    0.018123632 = product of:
      0.027185448 = sum of:
        0.011759962 = weight(_text_:to in 2650) [ClassicSimilarity], result of:
          0.011759962 = score(doc=2650,freq=4.0), product of:
            0.08279609 = queryWeight, product of:
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.045541126 = queryNorm
            0.14203523 = fieldWeight in 2650, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2650)
        0.015425485 = product of:
          0.03085097 = sum of:
            0.03085097 = weight(_text_:22 in 2650) [ClassicSimilarity], result of:
              0.03085097 = score(doc=2650,freq=2.0), product of:
                0.15947726 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.045541126 = queryNorm
                0.19345059 = fieldWeight in 2650, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=2650)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    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
  5. Broughton, V.: Automatic metadata generation : Digital resource description without human intervention (2007) 0.01
    0.012340388 = product of:
      0.037021164 = sum of:
        0.037021164 = product of:
          0.07404233 = sum of:
            0.07404233 = weight(_text_:22 in 6048) [ClassicSimilarity], result of:
              0.07404233 = score(doc=6048,freq=2.0), product of:
                0.15947726 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.045541126 = queryNorm
                0.46428138 = fieldWeight in 6048, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.09375 = fieldNorm(doc=6048)
          0.5 = coord(1/2)
      0.33333334 = coord(1/3)
    
    Date
    22. 9.2007 15:41:14
  6. Noruzi, A.: Folksonomies : (un)controlled vocabulary? (2006) 0.01
    0.009505464 = product of:
      0.028516391 = sum of:
        0.028516391 = weight(_text_:to in 404) [ClassicSimilarity], result of:
          0.028516391 = score(doc=404,freq=12.0), product of:
            0.08279609 = queryWeight, product of:
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.045541126 = queryNorm
            0.34441712 = fieldWeight in 404, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.0546875 = fieldNorm(doc=404)
      0.33333334 = coord(1/3)
    
    Abstract
    Folksonomy, a free-form tagging, is a user-generated classification system of web contents that allows users to tag their favorite web resources with their chosen words or phrases selected from natural language. These tags (also called concepts, categories, facets or entities) can be used to classify web resources and to express users' preferences. Folksonomy-based systems allow users to classify web resources through tagging bookmarks, photos or other web resources and saving them to a public web site like Del.icio.us. Thus information about web resources and online articles can be shared in an easy way. The purpose of this study is to provide an overview of the folksonomy tagging phenomenon (also called social tagging and social bookmarking) and explore some of the reasons why we need controlled vocabularies, discussing the problems associated with folksonomy.
  7. Watters, C.; Nizam, N.: Knowledge organization on the Web : the emergent role of social classification (2012) 0.01
    0.009505464 = product of:
      0.028516391 = sum of:
        0.028516391 = weight(_text_:to in 828) [ClassicSimilarity], result of:
          0.028516391 = score(doc=828,freq=12.0), product of:
            0.08279609 = queryWeight, product of:
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.045541126 = queryNorm
            0.34441712 = fieldWeight in 828, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.0546875 = fieldNorm(doc=828)
      0.33333334 = coord(1/3)
    
    Abstract
    There are close to a billion websites on the Internet with approximately 400 million users worldwide [www.internetworldstats.com]. People go to websites for a wide variety of different information tasks, from finding a restaurant to serious research. Many of the difficulties with searching the Web, as it is structured currently, can be attributed to increases to scale. The content of the Web is now so large that we only have a rough estimate of the number of sites and the range of information is extremely diverse, from blogs and photos to research articles and news videos.
  8. Shirky, C.: Ontology is overrated : categories, links, and tags (2005) 0.01
    0.0087653585 = product of:
      0.026296074 = sum of:
        0.026296074 = weight(_text_:to in 1265) [ClassicSimilarity], result of:
          0.026296074 = score(doc=1265,freq=20.0), product of:
            0.08279609 = queryWeight, product of:
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.045541126 = queryNorm
            0.31760043 = fieldWeight in 1265, product of:
              4.472136 = tf(freq=20.0), with freq of:
                20.0 = termFreq=20.0
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1265)
      0.33333334 = coord(1/3)
    
    Abstract
    Today I want to talk about categorization, and I want to convince you that a lot of what we think we know about categorization is wrong. In particular, I want to convince you that many of the ways we're attempting to apply categorization to the electronic world are actually a bad fit, because we've adopted habits of mind that are left over from earlier strategies. I also want to convince you that what we're seeing when we see the Web is actually a radical break with previous categorization strategies, rather than an extension of them. The second part of the talk is more speculative, because it is often the case that old systems get broken before people know what's going to take their place. (Anyone watching the music industry can see this at work today.) That's what I think is happening with categorization. What I think is coming instead are much more organic ways of organizing information than our current categorization schemes allow, based on two units -- the link, which can point to anything, and the tag, which is a way of attaching labels to links. The strategy of tagging -- free-form labeling, without regard to categorical constraints -- seems like a recipe for disaster, but as the Web has shown us, you can extract a surprising amount of value from big messy data sets.
  9. Trant, J.: Exploring the potential for social tagging and folksonomy in art museums : proof of concept (2006) 0.01
    0.008588262 = product of:
      0.025764786 = sum of:
        0.025764786 = weight(_text_:to in 5900) [ClassicSimilarity], result of:
          0.025764786 = score(doc=5900,freq=30.0), product of:
            0.08279609 = queryWeight, product of:
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.045541126 = queryNorm
            0.3111836 = fieldWeight in 5900, product of:
              5.477226 = tf(freq=30.0), with freq of:
                30.0 = termFreq=30.0
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.03125 = fieldNorm(doc=5900)
      0.33333334 = coord(1/3)
    
    Abstract
    Documentation of art museum collections has been traditionally written by and for art historians. To make art museum collections broadly accessible, and to enable art museums to engage their communities, means of access need to reflect the perspectives of other groups and communities. Social Tagging (the collective assignment of keywords to resources) and its resulting Folksonomy (the assemblage of concepts expressed in such a cooperatively developed system of classification) offer ways for art museums to engage with their communities and to understand what users of online museum collections see as important. Proof of Concept studies at The Metropolitan Museum of Art compared terms assigned by trained cataloguers and untrained cataloguers to existing museum documentation, and explored the potential for social tagging to improve access to museum collections. These preliminary studies, the results of which are reported here, have shown the potential of social tagging and folksonomy to open museum collections to new, more personal meanings. Untrained cataloguers identified content elements not described in formal museum documentation. Results from these tests - the first in the domain - provided validation for exploring social tagging and folksonomy as an access strategy within The Metropolitan Museum, motivation to proceed with a broader inter-institutional collaboration, and input into the development of a multi-institutional collaboration exploring tagging in art museums. Tags assigned by users might help bridge the semantic gap between the professional discourse of the curator and the popular language of the museum visitor. The steve collaboration (http://www.steve.museum) is building on these early studies to develop shared tools and research methods that enable social tagging of art museum collections and explore the utility of folksonomy for providing enhanced access to collections.
  10. Macgregor, G.; McCulloch, E.: Collaborative tagging as a knowledge organisation and resource discovery tool (2006) 0.01
    0.007839975 = product of:
      0.023519924 = sum of:
        0.023519924 = weight(_text_:to in 764) [ClassicSimilarity], result of:
          0.023519924 = score(doc=764,freq=16.0), product of:
            0.08279609 = queryWeight, product of:
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.045541126 = queryNorm
            0.28407046 = fieldWeight in 764, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.0390625 = fieldNorm(doc=764)
      0.33333334 = coord(1/3)
    
    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.
  11. Chopin, K.: Finding communities : alternative viewpoints through weblogs and tagging (2008) 0.01
    0.007839975 = product of:
      0.023519924 = sum of:
        0.023519924 = weight(_text_:to in 2341) [ClassicSimilarity], result of:
          0.023519924 = score(doc=2341,freq=16.0), product of:
            0.08279609 = queryWeight, product of:
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.045541126 = queryNorm
            0.28407046 = fieldWeight in 2341, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2341)
      0.33333334 = coord(1/3)
    
    Abstract
    Purpose - This paper aims to discuss and test the claim that user-based tagging allows for access to a wider variety of viewpoints than is found using other forms of online searching. Design/methodology/approach - A general overview of the nature of weblogs and user-based tagging is given, along with other relevant concepts. A case is then analyzed where viewpoints towards a specific issue are searched for using both tag searching (Technorati) and general search engine searching (Google and Google Blog Search). Findings - The claim to greater accessibility through user-based tagging is not overtly supported with these experiments. Further results for both general and tag-specific searching goes against some common assumptions about the types of content found on weblogs as opposed to more general web sites. Research limitations/implications - User-based tagging is still not widespread enough to give conclusive data for analysis. As this changes, further research in this area, using a variety of search subjects, is warranted. Originality/value - Although proponents of user-based tagging attribute many qualities to the practice, these qualities have not been properly documented or demonstrated. This paper partially rectifies this gap by testing one of the claims made, that of accessibility to alternate views, thus adding to the discussion on tagging for both researchers and other interested parties.
  12. Pera, M.S.; Lund, W.; Ng, Y.-K.: ¬A sophisticated library search strategy using folksonomies and similarity matching (2009) 0.01
    0.007839975 = product of:
      0.023519924 = sum of:
        0.023519924 = weight(_text_:to in 2939) [ClassicSimilarity], result of:
          0.023519924 = score(doc=2939,freq=16.0), product of:
            0.08279609 = queryWeight, product of:
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.045541126 = queryNorm
            0.28407046 = fieldWeight in 2939, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2939)
      0.33333334 = coord(1/3)
    
    Abstract
    Libraries, private and public, offer valuable resources to library patrons. As of today, the only way to locate information archived exclusively in libraries is through their catalogs. Library patrons, however, often find it difficult to formulate a proper query, which requires using specific keywords assigned to different fields of desired library catalog records, to obtain relevant results. These improperly formulated queries often yield irrelevant results or no results at all. This negative experience in dealing with existing library systems turns library patrons away from directly querying library catalogs; instead, they rely on Web search engines to perform their searches first, and upon obtaining the initial information (e.g., titles, subject headings, or authors) on the desired library materials, they query library catalogs. This searching strategy is an evidence of failure of today's library systems. In solving this problem, we propose an enhanced library system, which allows partial, similarity matching of (a) tags defined by ordinary users at a folksonomy site that describe the content of books and (b) unrestricted keywords specified by an ordinary library patron in a query to search for relevant library catalog records. The proposed library system allows patrons posting a query Q using commonly used words and ranks the retrieved results according to their degrees of resemblance with Q while maintaining the query processing time comparable with that achieved by current library search engines.
  13. Rafferty, P.: Tagging (2018) 0.01
    0.0077611795 = product of:
      0.023283537 = sum of:
        0.023283537 = weight(_text_:to in 4647) [ClassicSimilarity], result of:
          0.023283537 = score(doc=4647,freq=8.0), product of:
            0.08279609 = queryWeight, product of:
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.045541126 = queryNorm
            0.28121543 = fieldWeight in 4647, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4647)
      0.33333334 = coord(1/3)
    
    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.
  14. Bullard, J.: Curated Folksonomies : three implementations of structure through human judgment (2018) 0.01
    0.0077611795 = product of:
      0.023283537 = sum of:
        0.023283537 = weight(_text_:to in 5002) [ClassicSimilarity], result of:
          0.023283537 = score(doc=5002,freq=8.0), product of:
            0.08279609 = queryWeight, product of:
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.045541126 = queryNorm
            0.28121543 = fieldWeight in 5002, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.0546875 = fieldNorm(doc=5002)
      0.33333334 = coord(1/3)
    
    Abstract
    Traditional knowledge organization approaches struggle to make large user-generated collections navigable, especially when these collections are quickly growing, in which currency is of particular concern, for which professional classification design is too costly. Many of these collections use folksonomies for labelling and organization as a low-cost but flawed knowledge organization approach. While several computational approaches offer ways to ameliorate the worst flaws of folksonomies, some user-generated collections have implemented a human judgment-centered alternative to produce structured folksonomies. An analysis of three such implementations reveals design differences within the space. This approach, termed "curated folksonomy," presents a new object of study for knowledge organization and represents one answer to the tension between scalability and the value of human judgment.
  15. Voss, J.: Collaborative thesaurus tagging the Wikipedia way (2006) 0.01
    0.0076815756 = product of:
      0.023044726 = sum of:
        0.023044726 = weight(_text_:to in 620) [ClassicSimilarity], result of:
          0.023044726 = score(doc=620,freq=6.0), product of:
            0.08279609 = queryWeight, product of:
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.045541126 = queryNorm
            0.2783311 = fieldWeight in 620, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.0625 = fieldNorm(doc=620)
      0.33333334 = coord(1/3)
    
    Abstract
    This paper explores the system of categories that is used to classify articles in Wikipedia. It is compared to collaborative tagging systems like del.icio.us and to hierarchical classification like the Dewey Decimal Classification (DDC). Specifics and commonalities of these systems of subject indexing are exposed. Analysis of structural and statistical properties (descriptors per record, records per descriptor, descriptor levels) shows that the category system of Wikimedia is a thesaurus that combines collaborative tagging and hierarchical subject indexing in a special way.
  16. Furner, J.: Folksonomies (2009) 0.01
    0.0076815756 = product of:
      0.023044726 = sum of:
        0.023044726 = weight(_text_:to in 3857) [ClassicSimilarity], result of:
          0.023044726 = score(doc=3857,freq=6.0), product of:
            0.08279609 = queryWeight, product of:
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.045541126 = queryNorm
            0.2783311 = fieldWeight in 3857, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.0625 = fieldNorm(doc=3857)
      0.33333334 = coord(1/3)
    
    Abstract
    Folksonomies are indexing languages that emerge from the distributed resource-description activity of multiple agents who make use of online tagging services to assign tags (i.e., category labels) to the resources in collections. Although individuals' motivations for engaging in tagging activity vary widely, folksonomy-based retrieval systems can be evaluated by measuring the degree to which taggers and searchers agree on tag-resource pairings.
  17. Xie, H.; Li, X.; Wang, T.; Lau, R.Y.K.; Wong, T.-L.; Chen, L.; Wang, F.L.; Li, Q.: Incorporating sentiment into tag-based user profiles and resource profiles for personalized search in folksonomy (2016) 0.01
    0.007354548 = product of:
      0.022063645 = sum of:
        0.022063645 = weight(_text_:to in 2671) [ClassicSimilarity], result of:
          0.022063645 = score(doc=2671,freq=22.0), product of:
            0.08279609 = queryWeight, product of:
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.045541126 = queryNorm
            0.26648173 = fieldWeight in 2671, product of:
              4.690416 = tf(freq=22.0), with freq of:
                22.0 = termFreq=22.0
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.03125 = fieldNorm(doc=2671)
      0.33333334 = coord(1/3)
    
    Abstract
    In recent years, there has been a rapid growth of user-generated data in collaborative tagging (a.k.a. folksonomy-based) systems due to the prevailing of Web 2.0 communities. To effectively assist users to find their desired resources, it is critical to understand user behaviors and preferences. Tag-based profile techniques, which model users and resources by a vector of relevant tags, are widely employed in folksonomy-based systems. This is mainly because that personalized search and recommendations can be facilitated by measuring relevance between user profiles and resource profiles. However, conventional measurements neglect the sentiment aspect of user-generated tags. In fact, tags can be very emotional and subjective, as users usually express their perceptions and feelings about the resources by tags. Therefore, it is necessary to take sentiment relevance into account into measurements. In this paper, we present a novel generic framework SenticRank to incorporate various sentiment information to various sentiment-based information for personalized search by user profiles and resource profiles. In this framework, content-based sentiment ranking and collaborative sentiment ranking methods are proposed to obtain sentiment-based personalized ranking. To the best of our knowledge, this is the first work of integrating sentiment information to address the problem of the personalized tag-based search in collaborative tagging systems. Moreover, we compare the proposed sentiment-based personalized search with baselines in the experiments, the results of which have verified the effectiveness of the proposed framework. In addition, we study the influences by popular sentiment dictionaries, and SenticNet is the most prominent knowledge base to boost the performance of personalized search in folksonomy.
  18. Park, H.: ¬A conceptual framework to study folksonomic interaction (2011) 0.01
    0.0073336246 = product of:
      0.022000873 = sum of:
        0.022000873 = weight(_text_:to in 4852) [ClassicSimilarity], result of:
          0.022000873 = score(doc=4852,freq=14.0), product of:
            0.08279609 = queryWeight, product of:
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.045541126 = queryNorm
            0.2657236 = fieldWeight in 4852, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4852)
      0.33333334 = coord(1/3)
    
    Abstract
    This paper proposes a conceptual framework to recast a folksonomy as a Web classification and to use this to explore the ways in which people work with it in assessing, sharing, and navigating Web resources. The author uses information scent and foraging theory as a context to discuss how folksonomy is constructed through interactions among users, a folksonomic system, and a given domain that consists of a group of users who share the same interest or goals. The discussion centers on two dimensions of folksonomies: (1) folksonomy as a Web classification which puts like information together in a Web context; and (2) folksonomy as information scent which helps users to find related resources and users, and obtain desired information. This paper aims to integrate these two dimensions with a conceptual framework that addresses the structure of a folksonomy shaped by users' interactions. A proposed framework consists of three components of users' interactions with a folksonomy: (a) tagging - cognitive categorization of Web accessible resources by an individual user; (b) navigation - exploration and discovery of Web accessible resources in the folksonomic system; and (c) knowledge sharing - representation and communication of knowledge within a domain. This understanding will help us motivate possible future directions of research in folksonomy. This initial framework will frame a number of research questions and help lay the groundwork for future empirical research which focuses on qualitative analysis of a folksonomy and users' tagging behaviors.
  19. Moreiro-González, J.-A.; Bolaños-Mejías, C.: Folksonomy indexing from the assignment of free tags to setup subject : a search analysis into the domain of legal history (2018) 0.01
    0.0073336246 = product of:
      0.022000873 = sum of:
        0.022000873 = weight(_text_:to in 4640) [ClassicSimilarity], result of:
          0.022000873 = score(doc=4640,freq=14.0), product of:
            0.08279609 = queryWeight, product of:
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.045541126 = queryNorm
            0.2657236 = fieldWeight in 4640, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4640)
      0.33333334 = coord(1/3)
    
    Abstract
    The behaviour and lexical quality of the folksonomies is examined by comparing two online social networks: Library-Thing (for books) and Flickr (for photos). We presented a case study that combines quantitative and qualitative elements, singularized by the lexical and functional framework. Our query was made by "Legal History" and by the synonyms "Law History" and "History of Law." We then examined the relevance, consistency and precision of the tags attached to the retrieved documents, in addition to their lexical composition. We identified the difficulties caused by free tagging and some of the folksonomy solutions that have been found to solve them. The results are presented in comparative tables, giving special attention to related tags within each retrieved document. Although the number of ambiguous or inconsistent tags is not very large, these do nevertheless represent the most obvious problem to search and retrieval in folksonomies. Relevance is high when the terms are assigned by especially competent taggers. Even with less expert taggers, ambiguity is often successfully corrected by contextualizing the concepts within related tags. A propinquity to associative and taxonomic lexical semantic knowledge is reached via contextual relationships.
  20. Bar-Ilan, J.; Belous, Y.: Children as architects of Web directories : an exploratory study (2007) 0.01
    0.006789617 = product of:
      0.02036885 = sum of:
        0.02036885 = weight(_text_:to in 289) [ClassicSimilarity], result of:
          0.02036885 = score(doc=289,freq=12.0), product of:
            0.08279609 = queryWeight, product of:
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.045541126 = queryNorm
            0.24601223 = fieldWeight in 289, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.818051 = idf(docFreq=19512, maxDocs=44218)
              0.0390625 = fieldNorm(doc=289)
      0.33333334 = coord(1/3)
    
    Abstract
    Children are increasingly using the Web. Cognitive theory tells us that directory structures are especially suited for information retrieval by children; however, empirical results show that they prefer keyword searching. One of the reasons for these findings could be that the directory structures and terminology are created by grown-ups. Using a card-sorting method and an enveloping system, we simulated the structure of a directory. Our goal was to try to understand what browsable, hierarchical subject categories children create when suggested terms are supplied and they are free to add or delete terms. Twelve groups of four children each (fourth and fifth graders) participated in our exploratory study. The initial terminology presented to the children was based on names of categories used in popular directories, in the sections on Arts, Television, Music, Cinema, and Celebrities. The children were allowed to introduce additional cards and change the terms appearing on the 61 cards. Findings show that the different groups reached reasonable consensus; the majority of the category names used by existing directories were acceptable by them and only a small minority of the terms caused confusion. Our recommendation is to include children in the design process of directories, not only in designing the interface but also in designing the content structure as well.