Avatar semantic search: a database approach to information retrieval
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Efficiently linking text documents with relevant structured information
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Optimisation methods for ranking functions with multiple parameters
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
SQAK: doing more with keywords
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Introduction to Information Retrieval
Introduction to Information Retrieval
Combining keyword search and forms for ad hoc querying of databases
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Keyword querying and ranking in databases
Proceedings of the VLDB Endowment
Detecting and exploiting stability in evolving heterogeneous information spaces
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
SODA: generating SQL for business users
Proceedings of the VLDB Endowment
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A number of existing approaches attempt to reduce ambiguity of user's keyword queries by translating them to structured database queries. This disambiguation process relies on a proper assessment of whether a structured query represents the intent behind the keyword query. In this paper we systematically analyze a number of intuitive statistical measures that can potentially be used in this disambiguation process. We evaluate the impact of these measures through experiments on real-world data.