Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Personalized query expansion for the web
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
TREC: Continuing information retrieval's tradition of experimentation
Communications of the ACM
A survey of Web clustering engines
ACM Computing Surveys (CSUR)
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
WikiRelate! computing semantic relatedness using wikipedia
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Topic identification using Wikipedia graph centrality
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Taxonomy induction based on a collaboratively built knowledge repository
Artificial Intelligence
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The query formulation has always been a challenge for the users. In this paper, we propose a novel interactive query expansion methodology that identifies and presents the potential directions generalised concepts for the given query enabling the user to explore the interested topic further. The methodology proposed is concept-based direction CoD finder which relies on the external knowledge repository for finding the directions. Wikipedia, the most important non-profit crowdsourcing project, is considered as the external knowledge repository for CoD finder methodology. CoD finder identifies the concepts for the given query and derives the generalised direction for each of the concepts, based on the content of the Wikipedia article and the categories it belongs to. The CoD finder methodology has been evaluated in the crowdsourcing marketplace - Amazon's Mechanical Turk - for measuring the quality of the identified potential directions. The evaluation result shows that the potential directions identified by the CoD finder methodology produces better precision and recall for the given queries.