Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Deriving concept hierarchies from text
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A knowledge-based approach to organizing retrieved documents
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Does organisation by similarity assist image browsing?
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Modern Information Retrieval
XML Topic Maps: Creating and Using Topic Maps for the Web
XML Topic Maps: Creating and Using Topic Maps for the Web
SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Clustering versus faceted categories for information exploration
Communications of the ACM - Supporting exploratory search
Demonstrating the semantic growbag: automatically creating topic facets for faceteddblp
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Multi-Layered browsing and visualisation for digital libraries
ECDL'06 Proceedings of the 10th European conference on Research and Advanced Technology for Digital Libraries
Text2Onto: a framework for ontology learning and data-driven change discovery
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
SWDB'04 Proceedings of the Second international conference on Semantic Web and Databases
YAGO: A Large Ontology from Wikipedia and WordNet
Web Semantics: Science, Services and Agents on the World Wide Web
Automated Educational Course Metadata Generation Based on Semantics Discovery
EC-TEL '09 Proceedings of the 4th European Conference on Technology Enhanced Learning: Learning in the Synergy of Multiple Disciplines
Using semantic technologies in digital libraries: a roadmap to quality evaluation
ECDL'09 Proceedings of the 13th European conference on Research and advanced technology for digital libraries
FACeTOR: cost-driven exploration of faceted query results
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Uncovering hidden qualities - benefits of quality measures for automatically generated metadata
ECDL'10 Proceedings of the 14th European conference on Research and advanced technology for digital libraries
CFinder: An intelligent key concept finder from text for ontology development
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
Using keyword search to find relevant objects in digital libraries often results in way too large result sets. Based on the metadata associated with such objects, the faceted search paradigm allows users to structure and filter the result set, for example, using a publication type facet to show only books or videos. These facets usually focus on clear-cut characteristics of digital items, however it is very difficult to also organize the actual semantic content information into such a facet. The Semantic GrowBag approach, presented in this paper, uses the keywords provided by many authors of digital objects to automatically create light-weight topic categorization systems as a basis for a meaningful and dynamically adaptable topic facet. Using such emergent semantics enables an alternative way to filter large result sets according to the objects' content without the need to manually classify all objects with respect to a pre-specified vocabulary. We present the details of our algorithm using the DBLP collection of computer science documents and show some experimental evidence about the quality of the achieved results.