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A knowledge-based approach to organizing retrieved documents
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IEEE Intelligent Systems
Faceted metadata for image search and browsing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
EZ.WordNet: Principles for Automatic Generation of a Coarse Grained WordNet
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
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Automatic construction of multifaceted browsing interfaces
Proceedings of the 14th ACM international conference on Information and knowledge management
Being Literate with Large Document Collections: Observational Studies and Cost Structure Tradeoffs
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 03
Clustering versus faceted categories for information exploration
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Automatic Extraction of Useful Facet Hierarchies from Text Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
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Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
The optimum clustering framework: implementing the cluster hypothesis
Information Retrieval
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Hierarchical faceted metadata is a proven and popular approach to organizing information for navigation of information collections. More recently, digital libraries have begun to adopt faceted navigation for collections of scholarly holdings. A key impediment to further adoption is the need for the creation of subject-oriented faceted metadata. The Castanet algorithm was developed for the purpose of (semi) automated creation of such structures. This paper describes the application of Castanet to journal title content, and presents an evaluation suggesting its efficacy. This is followed by a discussion of areas for future work.