Optimizing web search using social annotations
Proceedings of the 16th international conference on World Wide Web
GroupMe! - where semantic web meets web 2.0
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Personalized social search based on the user's social network
Proceedings of the 18th ACM conference on Information and knowledge management
Exploiting User Feedback to Improve Semantic Web Service Discovery
ISWC '09 Proceedings of the 8th International Semantic Web Conference
A semantic clustering-based approach for searching and browsing tag spaces
Proceedings of the 2011 ACM Symposium on Applied Computing
A semantic-based approach for searching and browsing tag spaces
Decision Support Systems
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Folksonomy systems have shown to contribute to the quality of Web search ranking strategies. In this paper, we analyze and compare different graph-based ranking algorithms, namely FolkRank, SocialPageRank, and SocialSimRank. We enhance these algorithms by exploiting the context of tag assignmets, and evaluate the results on the GroupMe! dataset. In GroupMe!, users can organize and maintain arbitrary Web resources in self-defined groups. When users annotate resources in GroupMe!, this can be interpreted in context of a certain group. The grouping activity delivers valuable semantic information about resources and their context. We show how to use this information to improve the detection of relevant search results, and compare different strategies for ranking result lists in folksonomy systems.