Foundations of statistical natural language processing
Foundations of statistical natural language processing
Topic analysis using a finite mixture model
Information Processing and Management: an International Journal
Ontology Matching
The complex dynamics of collaborative tagging
Proceedings of the 16th international conference on World Wide Web
Authors vs. readers: a comparative study of document metadata and content in the www
Proceedings of the 2007 ACM symposium on Document engineering
DEXA '07 Proceedings of the 18th International Conference on Database and Expert Systems Applications
Topic Detection by Clustering Keywords
DEXA '08 Proceedings of the 2008 19th International Conference on Database and Expert Systems Application
Deriving a large scale taxonomy from Wikipedia
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
FCA-MERGE: bottom-up merging of ontologies
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
An empirical study of instance-based ontology matching
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Automatic classification of social tags
ECDL'10 Proceedings of the 14th European conference on Research and advanced technology for digital libraries
Improving tag-based recommendation by topic diversification
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
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The emergence of web based systems in which users can annotate items, raises the question of the semantic interoperability between vocabularies originating from collaborative annotation processes, often called folksonomies, and keywords assigned in a more traditional way. If collections are annotated according to two systems, e.g. with tags and keywords, the annotated data can be used for instance based mapping between the vocabularies. The basis for this kind of matching is an appropriate similarity measure between concepts, based on their distribution as annotations. In this paper we propose a new similarity measure that can take advantage of some special properties of user generated metadata. We have evaluated this measure with a set of articles from Wikipedia which are both classified according to the topic structure of Wikipedia and annotated by users of the bookmarking service del.icio.us. The results using the new measure are significantly better than those obtained using standard similarity measures proposed for this task in the literature, i.e., it correlates better with human judgments. We argue that the measure also has benefits for instance based mapping of more traditionally developed vocabularies.