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Tag Meaning Disambiguation through Analysis of Tripartite Structure of Folksonomies
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Exploring social annotations for web document classification
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Exploring social annotations for information retrieval
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Exploring folksonomy for personalized search
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Personalized recommendation in social tagging systems using hierarchical clustering
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Tapping on the potential of q&a community by recommending answer providers
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Reasonable tag-based collaborative filtering for social tagging systems
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The benefit of additional semantics in folksonomy systems
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Similarity cross-analysis of tag / co-tag spaces in social classification systems
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Contextualising tags in collaborative tagging systems
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Ontology-Based Service Discovery Front-End Interface for GloServ
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TagClusters: Semantic Aggregation of Collaborative Tags beyond TagClouds
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A fuzzy biclustering algorithm for social annotations
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An automatic translation of tags for multimedia contents using folksonomy networks
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Mutual contextualization in tripartite graphs of folksonomies
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A mixture model for expert finding
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The topic-perspective model for social tagging systems
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Modeling Social Annotation: A Bayesian Approach
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Explore the structure of social tags by subsumption relations
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Exploring social annotation tags to enhance information retrieval performance
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Learning the emergent knowledge from annotated blog postings
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In order to obtain a machine understandable semantics for web resources, research on the Semantic Web tries to annotate web resources with concepts and relations from explicitly defined formal ontologies. This kind of formal annotation is usually done manually or semi-automatically. In this paper, we explore a complement approach that focuses on the "social annotations of the web" which are annotations manually made by normal web users without a pre-defined formal ontology. Compared to the formal annotations, although social annotations are coarse-grained, informal and vague, they are also more accessible to more people and better reflect the web resources' meaning from the users' point of views during their actual usage of the web resources. Using a social bookmark service as an example, we show how emergent semantics [2] can be statistically derived from the social annotations. Furthermore, we apply the derived emergent semantics to discover and search shared web bookmarks. The initial evaluation on our implementation shows that our method can effectively discover semantically related web bookmarks that current social bookmark service can not discover easily.