SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Relaxing join and selection queries
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
WebTables: exploring the power of tables on the web
Proceedings of the VLDB Endowment
Relaxation in text search using taxonomies
Proceedings of the VLDB Endowment
Structured annotations of web queries
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Result enrichment in commerce search using browse trails
Proceedings of the fourth ACM international conference on Web search and data mining
Rewriting null e-commerce queries to recommend products
Proceedings of the 21st international conference companion on World Wide Web
Domain dependent query reformulation for web search
Proceedings of the 21st ACM international conference on Information and knowledge management
Structured query reformulations in commerce search
Proceedings of the 21st ACM international conference on Information and knowledge management
Toward whole-session relevance: exploring intrinsic diversity in web search
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
On segmentation of eCommerce queries
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Efficient parsing-based search over structured data
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Entwining structure into web search
Proceedings of the 7th International Workshop on Ranking in Databases
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Web search engines incorporate results from structured data sources to answer semantically rich user queries, i.e. Samsung 50 inch led tv can be answered from a table of television data. However, users are not domain experts and quite often enter values that do not match precisely the underlying data, so a literal execution will return zero results. A search engine would prefer to return at least a minimum number of results as close to the original query as possible while providing a time-bound execution guarantee. In this paper, we formalize these requirements, show the problem is NP-Hard and present approximation algorithms that produce rewrites that work in practice. We empirically validate our algorithms on large-scale data from a major search engine.