Partitioning sparse matrices with eigenvectors of graphs
SIAM Journal on Matrix Analysis and Applications
Optimizing web search using social annotations
Proceedings of the 16th international conference on World Wide Web
Integrating Folksonomies with the Semantic Web
ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
Pattern Matching Techniques to Identify Syntactic Variations of Tags in Folksonomies
WSKS '08 Proceedings of the 1st world summit on The Knowledge Society: Emerging Technologies and Information Systems for the Knowledge Society
Ranking in folksonomy systems: can context help?
Proceedings of the 17th ACM conference on Information and knowledge management
Searching and Browsing Tag Spaces Using the Semantic Tag Clustering Search Framework
ICSC '10 Proceedings of the 2010 IEEE Fourth International Conference on Semantic Computing
Reorganizing clouds: A study on tag clustering and evaluation
Expert Systems with Applications: An International Journal
Semantic disambiguation and contextualisation of social tags
UMAP'11 Proceedings of the 19th international conference on Advances in User Modeling
A semantic-based approach for searching and browsing tag spaces
Decision Support Systems
Multimedia information retrieval on the social web
Proceedings of the 22nd international conference on World Wide Web companion
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Many of the existing cloud tagging systems are unable to cope with the syntactic and semantic tag variations during user search and browse activities. As a solution to this problem, in this paper, we propose the Semantic Tag Clustering Search, a framework able to cope with these needs. The framework consists of three parts: removing syntactic variations, creating semantic clusters, and utilizing the obtained clusters to improve search and exploration of tag spaces. For removing syntactic variations, we use the normalized Levenshtein distance, and the cosine similarity measure based on tag co-occurrences. For creating semantic clusters, we improve an existing non-hierarchical clustering technique. Using our framework, we are able to find more clusters and achieve a higher precision than the original method. The advantages of a cluster-based approach for searching and browsing through tag spaces have been exploited in Xplore-Flickr.com, the implementation of our framework.