Usage patterns of collaborative tagging systems
Journal of Information Science
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Classifying tags using open content resources
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Collaborative Semantic Tagging of Web Resources on the Basis of Individual Knowledge Networks
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Automatically Identifying Tag Types
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Automatic classification of social tags
ECDL'10 Proceedings of the 14th European conference on Research and advanced technology for digital libraries
Evaluating tag-based information access in image collections
Proceedings of the 23rd ACM conference on Hypertext and social media
FReSET: an evaluation framework for folksonomy-based recommender systems
Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web
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When users use tags they often have a rich semantic structure in mind, which can not be fully explicated using existing tagging systems. However, a tagging system needs to be simple in order to be successful, otherwise it will not be accepted by users. In our ELWMS.KOM system for the support of self-regulated Resource-Based Learning users can assign specific semantic types to the tags they use in order to manage their web-based learning resources. However studies have shown that most users would appreciate an automatic identification of tag types. In this paper we present a knowledge-based approach for the automatic identification of the tag types used in the ELWMS.KOM system. Evaluations conducted on different corpora show that the algorithm works with an overall accuracy of up to 84%.