Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
A framework for selective query expansion
Proceedings of the thirteenth ACM international conference on Information and knowledge management
An Approach for Defining Relevance in the Ontology-Based Information Retrieval
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
A study of results overlap and uniqueness among major web search engines
Information Processing and Management: an International Journal
An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval
IEEE Transactions on Knowledge and Data Engineering
Improving query precision using semantic expansion
Information Processing and Management: an International Journal
Mining Fuzzy Ontology for a Web-Based Granular Information Retrieval System
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
An information retrieval driven by ontology from query to document expansion
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
An ontology-based information retrieval model
ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
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This paper presents a vector space model approach to representing documents and queries, which considers concepts instead of terms and uses WordNet as a light ontology. This representation reduces information redundancy with respect to conventional semantic expansion techniques. Experiments carried out on the MuchMore benchmark and on the TREC-7 and TREC-8 Ad-hoc collections demonstrate the effectiveness of the proposed approach.