Ontology Matching
Ranking very many typed entities on wikipedia
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
SKOS core: simple knowledge organisation for the web
DCMI '05 Proceedings of the 2005 international conference on Dublin Core and metadata applications: vocabularies in practice
A declarative framework for semantic link discovery over relational data
Proceedings of the 18th international conference on World wide web
DBpedia: a nucleus for a web of open data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
A survey of schema-based matching approaches
Journal on Data Semantics IV
Post-based collaborative filtering for personalized tag recommendation
Proceedings of the 2011 iConference
Mining tags using social endorsement networks
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Tag recommendation for large-scale ontology-based information systems
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part II
Folksonomy link prediction based on a tripartite graph for tag recommendation
Journal of Intelligent Information Systems
Exploiting user comments for audio-visual content indexing and retrieval
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
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We consider the problem of tag prediction in collaborative tagging systems where users share and annotate resources on the Web. We put forward HAMLET, a novel approach to automatically propagate tags along the edges of a graph which relates similar documents. We identify the core principles underlying tag propagation for which we derive suitable scoring models combined in one overall ranking formula. Leveraging these scores, we present an efficient top-k tag selection algorithm that infers additional tags by carefully inspecting neighbors in the document graph. Experiments using real-world data demonstrate the viability of our approach in large-scale environments where tags are scarce.