Exploring social annotations for the semantic web
Proceedings of the 15th international conference on World Wide Web
Ontologies are us: A unified model of social networks and semantics
Web Semantics: Science, Services and Agents on the World Wide Web
The complex dynamics of collaborative tagging
Proceedings of the 16th international conference on World Wide Web
Tag Meaning Disambiguation through Analysis of Tripartite Structure of Folksonomies
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Tag-aware recommender systems by fusion of collaborative filtering algorithms
Proceedings of the 2008 ACM symposium on Applied computing
Using the wisdom of the crowds for keyword generation
Proceedings of the 17th international conference on World Wide Web
Introduction to Information Retrieval
Introduction to Information Retrieval
A hybrid approach to item recommendation in folksonomies
Proceedings of the WSDM '09 Workshop on Exploiting Semantic Annotations in Information Retrieval
Emergence of consensus and shared vocabularies in collaborative tagging systems
ACM Transactions on the Web (TWEB)
Advanced Data Mining Techniques
Advanced Data Mining Techniques
Semantic imitation in social tagging
ACM Transactions on Computer-Human Interaction (TOCHI)
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Collaborative tagging as a tripartite network
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part III
Concept modeling by the masses: folksonomy structure and interoperability
ER'06 Proceedings of the 25th international conference on Conceptual Modeling
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In social tagging systems, people can annotate arbitrary tags to online data to categorize and index them. However, the lack of the "a priori" set of words makes it difficult for people to reach consensus about the semantics of tags and how to categorize data. Ontologies based approaches can help reaching such consensus, but they are still facing problems such as inability of model ambiguous and new concepts properly. For tags that axe used very few times, since they can only be used in very specific contexts, their semantics are very clear and detailed. Although people have no consensus on these tags, it is still possible to leverage these detailed semantics to model the other tags. In this paper we introduce a random walk and spreading activation like model to represent the semantics of tags using semantics of unpopular tags. By comparing the proposed model to the classic Latent Semantic Analysis approach in a concept clustering task, we show that the proposed model can properly capture the semantics of tags.