A survey on the use of relevance feedback for information access systems
The Knowledge Engineering Review
Examining the effectiveness of real-time query expansion
Information Processing and Management: an International Journal
A review of ontology based query expansion
Information Processing and Management: an International Journal
Exploiting underrepresented query aspects for automatic query expansion
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A knowledge-based search engine powered by wikipedia
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Introduction to Information Retrieval
Introduction to Information Retrieval
Learning to link with wikipedia
Proceedings of the 17th ACM conference on Information and knowledge management
Stratified analysis of AOL query log
Information Sciences: an International Journal
Query dependent pseudo-relevance feedback based on wikipedia
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Supervised query modeling using wikipedia
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Wikipedia as sense inventory to improve diversity in Web search results
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Improving AbraQ: An Automatic Query Expansion Algorithm
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Query expansion based on pseudo relevance feedback from definition clusters
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Query expansion based on a semantic graph model
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
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This research introduces a new query expansion method that uses Wikipedia and its hyperlink structure to find related terms for reformulating a query. Queries are first understood better by splitting into query aspects. Further understanding is gained through measuring how well each aspect is represented in the original search results. Poorly represented aspects are found to be an excellent source of query improvement. Our main contribution is the way of using Wikipedia to identify aspects and underrepresented aspects, and to weight the expansion terms. Results have shown that our approach improves the original query and search results, and outperforms two existing query expansion methods.