%0 
%0 Conference Proceedings
%A Braun, Simone; Schmidt, Andreas; Walter, Andreas; Nagypal, Gabor & Zacharias, Valentin
%D 2007
%T Ontology Maturing: a Collaborative Web 2.0 Approach to Ontology Engineering 
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%B Workshop on Social and Collaborative Construction of Structured Knowledge (CKC 2007) at WWW 2007
%C Banff, Canada
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%K collaborative engineering folksonomy ontology tagging web2.0 
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%U http://www2007.org/workshops/paper_14.pdf
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%0 Book
%A Gendarmi, Domenico & Lanubile, Filippo
%D 2006
%T Community-Driven Ontology Evolution Based on Folksonomies
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%K cominf community democratization evolution folksonomy knowledge-sharing tagging 
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%U http://dx.doi.org/10.1007/11915034_41
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%0 Journal Article
%A Golder, Scott A. & Huberman, Bernardo A.
%D 2006
%T Usage patterns of collaborative tagging systems
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%K collaborative collaborative-tagging folksonomy pattern tagging 
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%A Golder, Scott A. & Huberman, Bernardo A.
%D 2006
%T Usage patterns of collaborative tagging systems
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%B J. Inf. Sci.
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%I Sage Publications, Inc.
%V 32
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%N 2
%P 198--208
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%K citation golder huberman must problems tagging 
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%U http://portal.acm.org/citation.cfm?id=1119738.1119747
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%0 
%0 Conference Proceedings
%A Millen, David R.; Feinberg, Jonathan & Kerr, Bernard
%D 2006
%T Dogear: Social bookmarking in the enterprise
%E 
%B CHI '06: Proceedings of the SIGCHI conference on Human Factors in computing systems
%C New York, NY, USA
%I ACM Press
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%P 111--120
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%F citeulike:617156
%K enterprise folksonomy tagging 
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%U http://portal.acm.org/citation.cfm?id=1124792
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%0 
%0 Conference Proceedings
%A Sen, Shilad; Lam, Shyong K.; Rashid, Al Mamunur; Cosley, Dan; Frankowski, Dan; Osterhouse, Jeremy; Harper, F. Maxwell & Riedl, John
%D 2006
%T tagging, communities, vocabulary, evolution
%E 
%B CSCW '06: Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work
%C New York, NY, USA
%I ACM Press
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%P 181--190
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%F 1180904
%K collaborative-tagging community evolution experiment tagging vocabulary 
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%0 Conference Proceedings
%A Sereno, Bertrand; shum, Simon Buckingham & Motta, Enrico
%D 2007
%T Formalization, User Strategy And Interaction Design: Users’ Behaviour With Discourse Tagging Semantics
%E Alani, Harith; Noy, Natasha; Stumme, Gerd; Mika, Peter; Sure, York & Vrandecic, Denny
%B Workshop on Social and Collaborative Construction of Structured Knowledge (CKC 2007) at WWW 2007
%C Banff, Canada
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%K discourse semantic tagging 
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%U http://www2007.org/workshops/paper_30.pdf
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%0 
%0 Generic
%A Speller, Edith
%D 2007
%T Library Student Journal: Collaborative tagging, folksonomies, distributed classification or ethnoclassification: a literature review.
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%F citeulike:1115448
%K basilar folksonomy review survey tagging 
%X Tagging, folksonomy, distributed classification, ethnoclassification—however it is labelled, the concept of users creating and aggregating their own metadata is gaining ground on the internet. This literature review briefly defines the topic at hand, looking at current implementations and summarizing key advantages and disadvantages of distributed classification systems with reference to prominent folksonomy commentators.
After considering whether distributed classification can replace expert catalogers entirely, it concludes that distributed classification can make an important contribution to digital information organisation, but that it may need to be integrated with more traditional organisation tools to overcome its current weaknesses.
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%U http://informatics.buffalo.edu/org/lsj/articles/speller_2007_2_collaborative.php
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%0 Journal Article
%A Tsai, Chih-Fong; Mcgarry, Ken & Tait, John
%D 2006
%T Qualitative evaluation of automatic assignment of keywords to images
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%B Information Processing \& Management
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%V 42
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%N 1
%P 136--154
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%K images tagging theory 
%X In image retrieval, most systems lack user-centred evaluation since they are assessed by some chosen ground truth dataset. The results reported through precision and recall assessed against the ground truth are thought of as being an acceptable surrogate for the judgment of real users. Much current research focuses on automatically assigning keywords to images for enhancing retrieval effectiveness. However, evaluation methods are usually based on system-level assessment, e.g. classification accuracy based on some chosen ground truth dataset. In this paper, we present a qualitative evaluation methodology for automatic image indexing systems. The automatic indexing task is formulated as one of image annotation, or automatic metadata generation for images. The evaluation is composed of two individual methods. First, the automatic indexing annotation results are assessed by human subjects. Second, the subjects are asked to annotate some chosen images as the test set whose annotations are used as ground truth. Then, the system is tested by the test set whose annotation results are judged against the ground truth. Only one of these methods is reported for most systems on which user-centred evaluation are conducted. We believe that both methods need to be considered for full evaluation. We also provide an example evaluation of our system based on this methodology. According to this study, our proposed evaluation methodology is able to provide deeper understanding of the system's performance.
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%U http://www.sciencedirect.com/science/article/B6VC8-4F0GBHT-2/2/ccb893b1f3a4afbd7154d4827a70e009
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%0 Journal Article
%A Xu, Z.; Fu, Y.; Mao, J. & Su, D.
%D 2006
%T Towards the semantic web: Collaborative tag suggestions
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%B Collaborative Web Tagging Workshop at WWW2006, Edinburgh, Scotland, May
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%K collaborative folksonomy recommender suggestions tagging web2.0 www2006 
%X Content organization over the Internet went through several interesting phases of evolution: from structured directories to unstructured Web search engines and more recently, to tagging as a way for aggregating information, a step towards the semantic web vision. Tagging allows ranking and data organization to directly utilize inputs from end users, enabling machine processing of Web content. Since tags are created by individual users in a free form, one important problem facing tagging is to identify most appropriate tags, while eliminating noise and spam. For this purpose, we define a set of general criteria for a good tagging system. These criteria include high coverage of multiple facets to ensure good recall, least effort to reduce the cost involved in browsing, and high popularity to ensure tag quality. We propose a collaborative tag suggestion algorithm using these criteria to spot high-quality tags. The proposed algorithm employs a goodness measure for tags derived from collective user authorities to combat spam. The goodness measure is iteratively adjusted by a reward-penalty algorithm, which also incorporates other sources of tags, e.g., content-based auto-generated tags. Our experiments based on My Web 2.0 show that the algorithm is effective.
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