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
An information-theoretic approach to automatic query expansion
ACM Transactions on Information Systems (TOIS)
Query Expansion with Long-Span Collocates
Information Retrieval
ACM SIGIR Forum
Automatic categorization of query results
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Data & Knowledge Engineering
Personalized query expansion for the web
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Query biased snippet generation in XML search
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Selecting good expansion terms for pseudo-relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Mining search engine query logs via suggestion sampling
Proceedings of the VLDB Endowment
Data clouds: summarizing keyword search results over structured data
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Finding frequent co-occurring terms in relational keyword search
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Query dependent pseudo-relevance feedback based on wikipedia
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Enhancing cluster labeling using wikipedia
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Measure-driven keyword-query expansion
Proceedings of the VLDB Endowment
Using trees to depict a forest
Proceedings of the VLDB Endowment
Structured search result differentiation
Proceedings of the VLDB Endowment
Facetedpedia: dynamic generation of query-dependent faceted interfaces for wikipedia
Proceedings of the 19th international conference on World wide web
Analysis of structural relationships for hierarchical cluster labeling
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
FACeTOR: cost-driven exploration of faceted query results
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Conceptual indexing based on document content representation
CoLIS'05 Proceedings of the 5th international conference on Context: conceptions of Library and Information Sciences
Ontology-Based spatial query expansion in information retrieval
OTM'05 Proceedings of the 2005 OTM Confederated international conference on On the Move to Meaningful Internet Systems: CoopIS, COA, and ODBASE - Volume Part II
Evaluating the effectiveness of a collaborative querying environment
ICADL'05 Proceedings of the 8th international conference on Asian Digital Libraries: implementing strategies and sharing experiences
Exploiting and Maintaining Materialized Views for XML Keyword Queries
ACM Transactions on Internet Technology (TOIT)
Using Google™ facets as implicit feedback for query expansion in database searching
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
Summarizing answer graphs induced by keyword queries
Proceedings of the VLDB Endowment
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Query expansion is a functionality of search engines that suggests a set of related queries for a user-issued keyword query. Typical corpus-driven keyword query expansion approaches return popular words in the results as expanded queries. Using these approaches, the expanded queries may correspond to a subset of possible query semantics, and thus miss relevant results. To handle ambiguous queries and exploratory queries, whose result relevance is difficult to judge, we propose a new framework for keyword query expansion: we start with clustering the results according to user specified granularity, and then generate expanded queries, such that one expanded query is generated for each cluster whose result set should ideally be the corresponding cluster. We formalize this problem and show its APX-hardness. Then we propose two efficient algorithms named iterative single-keyword refinement and partial elimination based convergence, respectively, which effectively generate a set of expanded queries from clustered results that provides a classification of the original query results. We believe our study of generating an optimal query based on the ground truth of the query results not only has applications in query expansion, but has significance for studying keyword search quality in general.