Query expansion using lexical-semantic relations
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
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Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
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Journal of the American Society for Information Science and Technology
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Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
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CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Text Classification by Boosting Weak Learners based on Terms and Concepts
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
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WWW '05 Proceedings of the 14th international conference on World Wide Web
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ACM Transactions on Information Systems (TOIS)
Introduction to Information Retrieval
Introduction to Information Retrieval
Learning concept hierarchies from text corpora using formal concept analysis
Journal of Artificial Intelligence Research
What you seek is what you get: extraction of class attributes from query logs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Mining term association patterns from search logs for effective query reformulation
Proceedings of the 17th ACM conference on Information and knowledge management
Learning latent semantic relations from clickthrough data for query suggestion
Proceedings of the 17th ACM conference on Information and knowledge management
Mining translations of web queries from web click-through data
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Beyond hyperlinks: organizing information footprints in search logs to support effective browsing
Proceedings of the 18th ACM conference on Information and knowledge management
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Proceedings of the 2nd ACM workshop on Social web search and mining
Mining large query induced graphs towards a hierarchical query folksonomy
SPIRE'10 Proceedings of the 17th international conference on String processing and information retrieval
Implicit association via crowd-sourced coselection
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
The role of query sessions in extracting instance attributes from web search queries
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Automatic taxonomy construction from keywords
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining query log graphs towards a query folksonomy
Concurrency and Computation: Practice & Experience
Mining long-lasting exploratory user interests from search history
Proceedings of the 21st ACM international conference on Information and knowledge management
Evaluating implicit judgments from image search clickthrough data
Journal of the American Society for Information Science and Technology
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In this paper, we propose to mine query hierarchies from clickthrough data, which is within the larger area of automatic acquisition of knowledge from the Web. When a user submits a query to a search engine and clicks on the returned Web pages, the user's understanding of the query as well as its relation to the Web pages is encoded in the clickthrough data. With millions of queries being submitted to search engines every day, it is both important and beneficial to mine the knowledge hidden in the queries and their intended Web pages. We can use this information in various ways, such as providing query suggestions and organizing the queries. In this paper, we plan to exploit the knowledge hidden in clickthrough logs by constructing query hierarchies, which can reflect the relationship among queries. Our proposed method consists of two stages: generating candidate queries and determining "generalization/specialization" relatinns between these queries in a hierarchy. We test our method on some labeled data sets and illustrate the effectiveness of our proposed solution empirically.