AutoTag: a collaborative approach to automated tag assignment for weblog posts
Proceedings of the 15th international conference on World Wide Web
An Ontological Approach for Memetic Optimization in Personalised E-Learning Scenarios
ICCIT '08 Proceedings of the 2008 Third International Conference on Convergence and Hybrid Information Technology - Volume 02
Domain-specific keyphrase extraction
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
An integrated approach to discover tag semantics
Proceedings of the 2011 ACM Symposium on Applied Computing
Ontology Learning from User Tagging for Tag Recommendation Making
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Personalization in tag ontology learning for recommendation making
Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services
Content-based and collaborative techniques for tag recommendation: an empirical evaluation
Journal of Intelligent Information Systems
Learning personalized tag ontology from user tagging information
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
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Tagging is a representative activity of social Web, useful for organizing information into knowledge. This activity presents some open issues, due in the majority to the manual insertion of tags. On the other hand, domain ontology is a specification of the conceptualization of a domain in terms of concepts, attributes and relations. Domain ontologies have a good potential to improve information organization, management and understanding. In this paper, we propose an automated approach for recommending new tags for Web resources by using domain ontologies and key-phrases. The proposed approach is implemented in the PIRATES framework, a prototype system for personalized content retrieval, annotation, and classification. Our approach is then explained with a simple use-case scenario.