Usage patterns of collaborative tagging systems
Journal of Information Science
HT06, tagging paper, taxonomy, Flickr, academic article, to read
Proceedings of the seventeenth conference on Hypertext and hypermedia
tagging, communities, vocabulary, evolution
CSCW '06 Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work
The complex dynamics of collaborative tagging
Proceedings of the 16th international conference on World Wide Web
Towards automatic extraction of event and place semantics from flickr tags
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Can all tags be used for search?
Proceedings of the 17th ACM conference on Information and knowledge management
Semantic Grounding of Tag Relatedness in Social Bookmarking Systems
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Classifying tags using open content resources
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Automatic identification of tag types in a resource-based learning scenario
EC-TEL'11 Proceedings of the 6th European conference on Technology enhanced learning: towards ubiquitous learning
Evaluating tag-based information access in image collections
Proceedings of the 23rd ACM conference on Hypertext and social media
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Web 2.0 applications such as delicious , flickr or lastfm have recently become extremely popular and as a result, a large amount of semantically rich metadata produced by users becomes available and exploitable. Tag information can be used for many purposes (e.g. user profiling, recommendations, clustering etc ), though the benefit of tags for search is by far the most discussed usage. Tag types differ largely across systems and previous studies showed that, while some tag type categories might be useful for some particular users when searching, they may not bring any benefit to others. The present paper proposes an approach which utilizes rule-based as well as model-based methods, in order to automatically identify exactly these different types of tags. We compare the automatic tag classification produced by our algorithms against a ground truth data set, consisting of manual tag type assignments produced by human raters. Experimental results show that our methods can identify tag types with high accuracy, thus enabling further improvement of systems making use of social tags.