SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
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IEEE Intelligent Systems
Clustering short texts using wikipedia
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Harvesting Wiki Consensus: Using Wikipedia Entries as Vocabulary for Knowledge Management
IEEE Internet Computing
The Black Swan: The Impact of the Highly Improbable
The Black Swan: The Impact of the Highly Improbable
Semantic Convergence of Wikipedia Articles
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
TaxaMiner: an experimentation framework for automated taxonomy bootstrapping
International Journal of Web and Grid Services
Deriving a large scale taxonomy from Wikipedia
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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
Spatio-Temporal-Thematic Analysis of Citizen Sensor Data: Challenges and Experiences
WISE '09 Proceedings of the 10th International Conference on Web Information Systems Engineering
Understanding an ontology through divergent exploration
ESWC'11 Proceedings of the 8th extended semantic web conference on The semantic web: research and applications - Volume Part I
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Probase: a probabilistic taxonomy for text understanding
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
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Domain hierarchies are widely used as models underlying information retrieval tasks. Formal ontologies and taxonomies enrich such hierarchies further with properties and relationships but require manual effort; therefore they are costly to maintain, and often stale. Folksonomies and vocabularies lack rich category structure. Classification and extraction require the coverage of vocabularies and the alterability of folksonomies and can largely benefit from category relationships and other properties. With Doozer, a program for building conceptual models of information domains, we want to bridge the gap between the vocabularies and Folksonomies on the one side and the rich, expert-designed ontologies and taxonomies on the other. Doozer mines Wikipedia to produce tight domain hierarchies, starting with simple domain descriptions. It also adds relevancy scores for use in automated classification of information. The output model is described as a hierarchy of domain terms that can be used immediately for classifiers and IR systems or as a basis for manual or semi-automatic creation of formal ontologies.