Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Keyword Searching and Browsing in Databases using BANKS
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Bidirectional expansion for keyword search on graph databases
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Finding and approximating top-k answers in keyword proximity search
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Effective keyword search in relational databases
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Spark: top-k keyword query in relational databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
BLINKS: ranked keyword searches on graphs
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Towards keyword-driven analytical processing
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Discover: keyword search in relational databases
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Efficient IR-style keyword search over relational databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Objectrank: authority-based keyword search in databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Summarizing relational databases
Proceedings of the VLDB Endowment
Using structural information in XML keyword search effectively
ACM Transactions on Database Systems (TODS)
Keyword search over relational databases: a metadata approach
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Answering Top-k Keyword Queries on Relational Databases
International Journal of Information Retrieval Research
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Keyword search has become an effective information retrieval method for structured data. Existing works in relational database keyword search have addressed the problems of finding and evaluating candidate results. However, given that keyword queries are inherently ambiguous, it is often the case that candidate results do not match users' search intention. In this paper, we analyze the limitations of current keyword search techniques and introduce the problem of generating and ranking keyword query interpretations. We propose a novel 3-phase keyword search paradigm which consists of: (1) the ability to predict query interpretations; (2) incorporate user feedback to to remove keyword ambiguities; (3) a ranking model to evaluate a query interpretation.