Search (47 results, page 1 of 3)

0.11286168 = product of:
  0.16929251 = sum of:
    0.03561326 = weight(_text_:based in 2889) [ClassicSimilarity], result of:
      0.03561326 = score(doc=2889,freq=2.0), product of:
        0.15283063 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.050723847 = queryNorm
        0.23302436 = fieldWeight in 2889, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2889)
    0.13367924 = sum of:
      0.08557258 = weight(_text_:training in 2889) [ClassicSimilarity], result of:
        0.08557258 = score(doc=2889,freq=2.0), product of:
          0.23690371 = queryWeight, product of:
            4.67046 = idf(docFreq=1125, maxDocs=44218)
            0.050723847 = queryNorm
          0.3612125 = fieldWeight in 2889, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            4.67046 = idf(docFreq=1125, maxDocs=44218)
            0.0546875 = fieldNorm(doc=2889)
      0.048106667 = weight(_text_:22 in 2889) [ClassicSimilarity], result of:
        0.048106667 = score(doc=2889,freq=2.0), product of:
          0.17762627 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.050723847 = 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)
  0.6666667 = coord(2/3)

Date

21. 6.2009 12:22:44

Theme

Computer Based Training

0.028412666 = product of:
  0.042618997 = sum of:
    0.025438042 = weight(_text_:based in 515) [ClassicSimilarity], result of:
      0.025438042 = score(doc=515,freq=2.0), product of:
        0.15283063 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.050723847 = queryNorm
        0.16644597 = fieldWeight in 515, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.0390625 = fieldNorm(doc=515)
    0.017180953 = product of:
      0.034361906 = sum of:
        0.034361906 = weight(_text_:22 in 515) [ClassicSimilarity], result of:
          0.034361906 = score(doc=515,freq=2.0), product of:
            0.17762627 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050723847 = 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)
  0.6666667 = coord(2/3)

Abstract

A new collaborative approach in information organization and sharing has recently arisen, known as collaborative tagging or social indexing. A key element of collaborative tagging is the concept of collective intelligence (CI), which is a shared intelligence among all participants. This research investigates the phenomenon of social tagging in the context of CI with the aim to serve as a stepping-stone towards the mining of truly valuable social tags for web resources. This study focuses on assessing and evaluating the degree of CI embedded in social tagging over time in terms of two-parameter values, number of participants, and top frequency ranking window. Five different metrics were adopted and utilized for assessing the similarity between ranking lists: overlapList, overlapRank, Footrule, Fagin's measure, and the Inverse Rank measure. The result of this study demonstrates that a substantial degree of CI is most likely to be achieved when somewhere between the first 200 and 400 people have participated in tagging, and that a target degree of CI can be projected by controlling the two factors along with the selection of a similarity metric. The study also tests some experimental conditions for detecting social tags with high CI degree. The results of this study can be applicable to the study of filtering social tags based on CI; filtered social tags may be utilized for the metadata creation of tagged resources and possibly for the retrieval of tagged resources.

Date

25.12.2012 15:22:37

0.022434246 = product of:
  0.06730273 = sum of:
    0.06730273 = weight(_text_:based in 3347) [ClassicSimilarity], result of:
      0.06730273 = score(doc=3347,freq=14.0), product of:
        0.15283063 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.050723847 = queryNorm
        0.44037464 = fieldWeight in 3347, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3347)
  0.33333334 = coord(1/3)

Abstract

The cultural heritage sector has embraced social tagging as a way to increase both access to online content and to engage users with their digital collections. In this article, we build on two current lines of research. (a) We use Waisda?, an existing labeling game, to add time-based annotations to content. (b) In this context, we investigate the role of experts in human-based computation (nichesourcing). We report on a small-scale experiment in which we applied Waisda? to content from film archives. We study the differences in the type of time-based tags between experts and novices for film clips in a crowdsourcing setting. The findings show high similarity in the number and type of tags (mostly factual). In the less frequent tags, however, experts used more domain-specific terms. We conclude that competitive games are not suited to elicit real expert-level descriptions. We also confirm that providing guidelines, based on conceptual frameworks that are more suited to moving images in a time-based fashion, could result in increasing the quality of the tags, thus allowing for creating more tag-based innovative services for online audiovisual heritage.

0.018960398 = product of:
  0.056881193 = sum of:
    0.056881193 = weight(_text_:based in 2341) [ClassicSimilarity], result of:
      0.056881193 = score(doc=2341,freq=10.0), product of:
        0.15283063 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.050723847 = queryNorm
        0.37218451 = fieldWeight in 2341, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.0129938 = idf(docFreq=5906, 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.

0.017623993 = product of:
  0.052871976 = sum of:
    0.052871976 = weight(_text_:based in 4748) [ClassicSimilarity], result of:
      0.052871976 = score(doc=4748,freq=6.0), product of:
        0.15283063 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.050723847 = queryNorm
        0.34595144 = fieldWeight in 4748, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.046875 = fieldNorm(doc=4748)
  0.33333334 = coord(1/3)

Abstract

Social tags have become an important tool for improving access to online resources, particularly non-text media. With the dramatic growth of user-generated content, the importance of tags is likely to grow. However, while tagging behavior is well studied, the relationship between tagging behavior and features of the media being tagged is not well understood. In this paper, we examine the relationship between tagging behavior and image type. Through a lab-based study with 51 subjects and an analysis of an online dataset of image tags, we show that there are significant differences in the number, order, and type of tags that users assign based on their past experience with an image, the type of image being tagged, and other image features. We present these results and discuss the significant implications this work has for tag-based search algorithms, tag recommendation systems, and other interface issues.

0.016958695 = product of:
  0.050876085 = sum of:
    0.050876085 = weight(_text_:based in 3452) [ClassicSimilarity], result of:
      0.050876085 = score(doc=3452,freq=8.0), product of:
        0.15283063 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.050723847 = queryNorm
        0.33289194 = fieldWeight in 3452, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3452)
  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.

0.014686662 = product of:
  0.044059984 = sum of:
    0.044059984 = weight(_text_:based in 3707) [ClassicSimilarity], result of:
      0.044059984 = score(doc=3707,freq=6.0), product of:
        0.15283063 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.050723847 = queryNorm
        0.28829288 = fieldWeight in 3707, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3707)
  0.33333334 = coord(1/3)

Abstract

Social tagging or collaborative tagging has become a new trend in the organization, management, and discovery of digital information. The rapid growth of shared information mostly controlled by social tags poses a new challenge for social tag-based information organization and retrieval. A plausible approach for this challenge is linking social tags to a controlled vocabulary. As an introductory step for this approach, this study investigates ways of predicting relevant subject headings for resources from social tags assigned to the resources. The prediction of subject headings was measured by five different similarity measures: tf-idf, cosine-based similarity (CoS), Jaccard similarity (or Jaccard coefficient; JS), Mutual information (MI), and information radius (IRad). Their results were compared to those by professionals. The results show that a CoS measure based on top five social tags was most effective. Inclusions of more social tags only aggravate the performance. The performance of JS is comparable to the performance of CoS while tf-idf is comparable with up to 70% less than the best performance. MI and IRad have inferior performance compared to the other methods. This study demonstrates the application of the similarity measuring techniques to the prediction of correct Library of Congress subject headings.

0.014686662 = product of:
  0.044059984 = sum of:
    0.044059984 = weight(_text_:based in 4938) [ClassicSimilarity], result of:
      0.044059984 = score(doc=4938,freq=6.0), product of:
        0.15283063 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.050723847 = queryNorm
        0.28829288 = fieldWeight in 4938, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4938)
  0.33333334 = coord(1/3)

Abstract

Tags associated with social images are valuable information source for superior image search and retrieval experiences. Although various heuristics are valuable to boost tag-based search for images, there is a lack of general framework to study the impact of these heuristics. Specifically, the task of ranking images matching a given tag query based on their associated tags in descending order of relevance has not been well studied. In this article, we take the first step to propose a generic, flexible, and extensible framework for this task and exploit it for a systematic and comprehensive empirical evaluation of various methods for ranking images. To this end, we identified five orthogonal dimensions to quantify the matching score between a tagged image and a tag query. These five dimensions are: (i) tag relatedness to measure the degree of effectiveness of a tag describing the tagged image; (ii) tag discrimination to quantify the degree of discrimination of a tag with respect to the entire tagged image collection; (iii) tag length normalization analogous to document length normalization in web search; (iv) tag-query matching model for the matching score computation between an image tag and a query tag; and (v) query model for tag query rewriting. For each dimension, we identify a few implementations and evaluate their impact on NUS-WIDE dataset, the largest human-annotated dataset consisting of more than 269K tagged images from Flickr. We evaluated 81 single-tag queries and 443 multi-tag queries over 288 search methods and systematically compare their performances using standard metrics including Precision at top-K, Mean Average Precision (MAP), Recall, and Normalized Discounted Cumulative Gain (NDCG).

0.01438993 = product of:
  0.04316979 = sum of:
    0.04316979 = weight(_text_:based in 752) [ClassicSimilarity], result of:
      0.04316979 = score(doc=752,freq=4.0), product of:
        0.15283063 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.050723847 = queryNorm
        0.28246817 = fieldWeight in 752, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.046875 = fieldNorm(doc=752)
  0.33333334 = coord(1/3)

Abstract

Purpose - The purpose of this paper is two-fold: to examine three models of subject indexing (i.e. expert-led indexing, author-generated indexing, and user-orientated indexing); and to compare and contrast two user-orientated indexing approaches (i.e. the theoretically-based Democratic Indexing project, and Flickr, a working system for describing photographs). Design/methodology/approach - The approach to examining Flickr and Democratic Indexing is evaluative. The limitations of Flickr are described and examples are provided. The Democratic Indexing approach, which the authors believe offers a method of marshalling a "free" user-indexed archive to provide useful retrieval functions, is described. Findings - The examination of both Flickr and the Democratic Indexing approach suggests that, despite Shirky's claim of philosophical paradigm shifting for social tagging, there is a residing doubt amongst information professionals that self-organising systems can work without there being some element of control and some form of "representative authority". Originality/value - This paper contributes to the literature of user-based indexing and social tagging.

0.01438993 = product of:
  0.04316979 = sum of:
    0.04316979 = weight(_text_:based in 873) [ClassicSimilarity], result of:
      0.04316979 = score(doc=873,freq=4.0), product of:
        0.15283063 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.050723847 = queryNorm
        0.28246817 = fieldWeight in 873, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.046875 = fieldNorm(doc=873)
  0.33333334 = coord(1/3)

Abstract

Social or collaborative tagging enables users to organize and label resources on the web. Libraries and other information environments hope that tagging can complement professional subject access with user-created terms. But who are the taggers, and does their language represent that of the user population? Some language theorists believe that inherent variables, such as gender or race, can be responsible for language use, whereas other researchers endorse more multiply-influenced practice-based approaches, where interactions with others affect language use more than a single variable. To explore whether linguistic variation in tagging is influenced more by gender or context, in this exploratory study, I will analyze the content and quantity of tags used on LibraryThing. This study seeks to dismantle stereotypical views of women's language use and to suggest a community of practice-based approach to analyzing social tags.

0.01438993 = product of:
  0.04316979 = sum of:
    0.04316979 = weight(_text_:based in 1370) [ClassicSimilarity], result of:
      0.04316979 = score(doc=1370,freq=4.0), product of:
        0.15283063 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.050723847 = queryNorm
        0.28246817 = fieldWeight in 1370, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.046875 = fieldNorm(doc=1370)
  0.33333334 = coord(1/3)

Abstract

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

0.01438993 = product of:
  0.04316979 = sum of:
    0.04316979 = weight(_text_:based in 1730) [ClassicSimilarity], result of:
      0.04316979 = score(doc=1730,freq=4.0), product of:
        0.15283063 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.050723847 = queryNorm
        0.28246817 = fieldWeight in 1730, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.046875 = fieldNorm(doc=1730)
  0.33333334 = coord(1/3)

Abstract

This paper presents a methodological discussion of a study of tagging quality in subject indexing. The data analysis in the study was divided into 3 phases: analysis of indexing consistency, analysis of tagging effectiveness, and analysis of the semantic values of tags. To analyze indexing consistency, this study employed the vector space model-based indexing consistency measures. An analysis of tagging effectiveness with tagging exhaustivity and tag specificity was conducted to ameliorate the drawbacks of consistency analysis based on only the quantitative measures of vocabulary matching. To further investigate the semantic values of tags at various levels of specificity, a latent semantic analysis (LSA) was conducted. To test statistical significance for the relation between tag specificity and semantic quality, correlation analysis was conducted. This research demonstrates the potential of tags for web document indexing with a complete assessment of tagging quality and provides a basis for further study of the strengths and limitations of tagging.

0.01438993 = product of:
  0.04316979 = sum of:
    0.04316979 = weight(_text_:based in 2732) [ClassicSimilarity], result of:
      0.04316979 = score(doc=2732,freq=4.0), product of:
        0.15283063 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.050723847 = queryNorm
        0.28246817 = fieldWeight in 2732, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.046875 = fieldNorm(doc=2732)
  0.33333334 = coord(1/3)

Abstract

Social tagging systems enable users to assign arbitrary tags to various digital resources. However, they face vague-meaning problems when users retrieve or present resources with the keyword-based tags. In order to solve these problems, this study takes advantage of Semantic Web technology and the topological characteristics of knowledge maps to develop a system that comprises a semantic tagging mechanism and triple-pattern and visual searching mechanisms. A field experiment was conducted to evaluate the effectiveness and user acceptance of these mechanisms in a knowledge sharing context. The results show that the semantic social tagging system is more effective than a keyword-based system. The visualized knowledge map helps users capture an overview of the knowledge domain, reduce cognitive effort for the search, and obtain more enjoyment. Traditional keyword tagging with a keyword search still has the advantage of ease of use and the users had higher intention to use it. This study also proposes directions for future development of semantic social tagging systems.

0.011991608 = product of:
  0.035974823 = sum of:
    0.035974823 = weight(_text_:based in 1265) [ClassicSimilarity], result of:
      0.035974823 = score(doc=1265,freq=4.0), product of:
        0.15283063 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.050723847 = queryNorm
        0.23539014 = fieldWeight in 1265, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0129938 = idf(docFreq=5906, 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.

Footnote

This piece is based on two talks I gave in the spring of 2005 -- one at the O'Reilly ETech conference in March, entitled "Ontology Is Overrated", and one at the IMCExpo in April entitled "Folksonomies & Tags: The rise of user-developed classification." The written version is a heavily edited concatenation of those two talks.

0.011991608 = product of:
  0.035974823 = sum of:
    0.035974823 = weight(_text_:based in 4220) [ClassicSimilarity], result of:
      0.035974823 = score(doc=4220,freq=4.0), product of:
        0.15283063 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.050723847 = queryNorm
        0.23539014 = fieldWeight in 4220, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4220)
  0.33333334 = coord(1/3)

Abstract

Taxonomy construction is a resource-demanding, top-down, and time consuming effort. It does not always cater for the prevailing context of the captured information. This paper proposes a novel approach to automatically convert tags into a hierarchical taxonomy. Folksonomy describes the process by which many users add metadata in the form of keywords or tags to shared content. Using folksonomy as a knowledge source for nominating tags, the proposed method first converts the tags into a hierarchy. This serves to harness a core set of taxonomy terms; the generated hierarchical structure facilitates users' information navigation behavior and permits personalizations. Newly acquired tags are then progressively integrated into a taxonomy in a largely automated way to complete the taxonomy creation process. Common taxonomy construction techniques are based on 3 main approaches: clustering, lexico-syntactic pattern matching, and automatic acquisition from machine-readable dictionaries. In contrast to these prevailing approaches, this paper proposes a taxonomy construction analysis based on heuristic rules and deep syntactic analysis. The proposed method requires only a relatively small corpus to create a preliminary taxonomy. The approach has been evaluated using an expert-defined taxonomy in the environmental protection domain and encouraging results were yielded.

0.011871087 = product of:
  0.03561326 = sum of:
    0.03561326 = weight(_text_:based in 119) [ClassicSimilarity], result of:
      0.03561326 = score(doc=119,freq=2.0), product of:
        0.15283063 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.050723847 = queryNorm
        0.23302436 = fieldWeight in 119, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.0546875 = fieldNorm(doc=119)
  0.33333334 = coord(1/3)

Abstract

In this paper, we consider three models of subject indexing, and compare and contrast two indexing approaches, the theoretically based democratic indexing project, and Flickr, a working system for describing photographs. We argue that, despite Shirky's (2005) claim of philosophical paradigm shifting for social tagging, there is a residing doubt amongst information professionals that self-organising systems can work without there being some element of control and some form of 'representative authority'.

0.011871087 = product of:
  0.03561326 = sum of:
    0.03561326 = weight(_text_:based in 4647) [ClassicSimilarity], result of:
      0.03561326 = score(doc=4647,freq=2.0), product of:
        0.15283063 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.050723847 = queryNorm
        0.23302436 = fieldWeight in 4647, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0129938 = idf(docFreq=5906, 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.

0.010187211 = product of:
  0.030561633 = sum of:
    0.030561633 = product of:
      0.061123267 = sum of:
        0.061123267 = weight(_text_:training in 4171) [ClassicSimilarity], result of:
          0.061123267 = score(doc=4171,freq=2.0), product of:
            0.23690371 = queryWeight, product of:
              4.67046 = idf(docFreq=1125, maxDocs=44218)
              0.050723847 = queryNorm
            0.2580089 = fieldWeight in 4171, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.67046 = idf(docFreq=1125, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4171)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

Purpose - Traditional subject indexing and classification are considered infeasible in many digital collections. This paper seeks to investigate ways of enhancing social tagging via knowledge organization systems, with a view to improving the quality of tags for increased information discovery and retrieval performance. Design/methodology/approach - Enhanced tagging interfaces were developed for exemplar online repositories, and trials were undertaken with author and reader groups to evaluate the effectiveness of tagging augmented with control vocabulary for subject indexing of papers in online repositories. Findings - The results showed that using a knowledge organisation system to augment tagging does appear to increase the effectiveness of non-specialist users (that is, without information science training) in subject indexing. Research limitations/implications - While limited by the size and scope of the trials undertaken, these results do point to the usefulness of a mixed approach in supporting the subject indexing of online resources. Originality/value - The value of this work is as a guide to future developments in the practical support for resource indexing in online repositories.

0.010175217 = product of:
  0.03052565 = sum of:
    0.03052565 = weight(_text_:based in 3113) [ClassicSimilarity], result of:
      0.03052565 = score(doc=3113,freq=2.0), product of:
        0.15283063 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.050723847 = queryNorm
        0.19973516 = fieldWeight in 3113, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.046875 = fieldNorm(doc=3113)
  0.33333334 = coord(1/3)

Abstract

A longstanding area of study in indexing is the identification of factors affecting vocabulary usage and consistency. This topic has seen a recent resurgence with a focus on social tagging. Tagging data for scholarly articles made available by the social bookmarking Website CiteULike (www.citeulike.org) were used to test the use of inter-indexer/tagger consistency density values, based on a method developed by the authors by comparing calculations for highly tagged documents representing three subject areas (Science, Social Science, Social Software). The analysis revealed that the developed method is viable for a large dataset. The findings also indicated that there were no significant differences in tagging consistency among the three topic areas, demonstrating that vocabulary usage in a relatively new subject area like social software is no more inconsistent than the more established subject areas investigated. The implications of the method used and the findings are discussed.

0.010175217 = product of:
  0.03052565 = sum of:
    0.03052565 = weight(_text_:based in 3290) [ClassicSimilarity], result of:
      0.03052565 = score(doc=3290,freq=2.0), product of:
        0.15283063 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.050723847 = queryNorm
        0.19973516 = fieldWeight in 3290, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.046875 = fieldNorm(doc=3290)
  0.33333334 = coord(1/3)

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.