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Comparison of TGN and Flickr User-Defined Geographic Tags 

Comparison of TGN and Flickr User-Defined Geographic Tags 

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As evidenced by the growing popularity of sites which provide tagging and annotation functionality, like del.icio.us (http://del.icio.us), Flickr (http://www.flickr.com/), technorati (http://www.technorati.com/), and CiteULike (http://www.citeulike.org/), which already have combined user bases in the several millions, collaborative cataloging, or t...

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... First one tries to educate learners to improve "tag literacy" (Guy and Tonkin 2006). An important condition for this way of resolving problems is to better examine learner researches about folksonomies (Bar-Ilan et al. 2006;Lin et al. 2006;Winget 2006), concerning the "deep nature" of tags (Veres 2006a), discussing aspects of the folksonomy interoperability (Veres 2006b) and the "semiotic dynamics" of folksonomies in terms of tag co-occurrences (Cattuto et al. 2007). For training the learner's selection of "good" tags it may be useful that the system would suggest some tags (MacLaurin 2010). ...
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Collaborative tagging is technique, highly employed in different domains, which is used for automatic analysis of users’ preferences and recommendations. To improve recommendation quality, metadata such as content information of items has typically been used as additional knowledge. With the increasing reputation of the collaborative tagging systems, tags could be interesting and provide useful information to enhance algorithms for recommender systems. Besides helping user to organize his/her personal collections, a tag also can be regarded as a user’s personal opinion expression, while tagging can be considered as implicit rating or voting on the tagged information resources or items. The overview, presented in this chapter includes descriptions of content-based recommender systems, collaborative filtering systems, hybrid approach, memory-based and model-based algorithms, features of collaborative tagging that are generally attributed to their success and popularity, as well as a model for tagging activities and tag-based recommender systems.
... Gruber, 2007). Typically, folksonomies are considered to be formed from a triple of users annotating resources with tags (Winget, 2006). The bottom-up nature of folksonomies is argued to result from their emergent nature, whereby tags used frequently by many users suggest shared conceptualisations (Hollenstein & Purves, 2010;Winget, 2006). ...
... Typically, folksonomies are considered to be formed from a triple of users annotating resources with tags (Winget, 2006). The bottom-up nature of folksonomies is argued to result from their emergent nature, whereby tags used frequently by many users suggest shared conceptualisations (Hollenstein & Purves, 2010;Winget, 2006). Since, for example, individual resources or users can be associated with weighted vectors of tags, it is also possible to calculate similarity between resources or users using a range of similarity measures (Cantador, Bellogín, & Vallet, 2010). ...
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... An important question herein deals with the accuracy of user-provided textual metadata on Flickr. To this goal, Winget [157] made a study towards the "correct" way of organizing information on the web, with case studies derived from Flickr. Her main conclusions were that users intend on providing accurate textual descriptions, however since these appear rather arbitrary, there is the need of organizing them with structured vocabularies. ...
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... There was a cluster of studies around 2005-6, often collating tags from a large number of users into a folksonomy. These studies were often carried out from a librarian and information science perspective (such as Heckner et al. 2007;Winget 2006) or by the site designers themselves (as in Marlow et al. 2006 whose research was done in association with Yahoo). ...
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This article presents the results of a user evaluation of automatically generated concept keywords and place names (toponyms) for geo-referenced images. Automatically annotating images is becoming indispensable for effective information retrieval, since the number of geo-referenced images available online is growing, yet many images are insufficiently tagged or captioned to be efficiently searchable by standard information retrieval procedures. The Tripod project developed original methods for automatically annotating geo-referenced images by generating representations of the likely visible footprint of a geo-referenced image, and using this footprint to query spatial databases and web resources. These queries return raw lists of potential keywords and toponyms, which are subsequently filtered and ranked. This article reports on user experiments designed to evaluate the quality of the generated annotations. The experiments combined quantitative and qualitative approaches: To retrieve a large number of responses, participants rated the annotations in standardized online questionnaires that showed an image and its corresponding keywords. In addition, several focus groups provided rich qualitative information in open discussions. The results of the evaluation show that currently the annotation method performs better on rural images than on urban ones. Further, for each image at least one suitable keyword could be generated. The integration of heterogeneous data sources resulted in some images having a high level of noise in the form of obviously wrong or spurious keywords. The article discusses the evaluation itself and methods to improve the automatic generation of annotations.