DBXplorer: A System for Keyword-Based Search over Relational Databases
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Inverted files for text search engines
ACM Computing Surveys (CSUR)
Discover: keyword search in relational databases
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
BANKS: browsing and keyword searching in relational databases
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
SQAK: doing more with keywords
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
From keywords to semantic queries-Incremental query construction on the semantic web
Web Semantics: Science, Services and Agents on the World Wide Web
Keyword Search in Databases
Keymantic: semantic keyword-based searching in data integration systems
Proceedings of the VLDB Endowment
Keyword search over relational databases: a metadata approach
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
The list Viterbi training algorithm and its application to keyword search over databases
Proceedings of the 20th ACM international conference on Information and knowledge management
From network mining to large scale business networks
Proceedings of the 21st international conference companion on World Wide Web
A query answering system for data with evolution relationships
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
QUEST: a keyword search system for relational data based on semantic and machine learning techniques
Proceedings of the VLDB Endowment
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We present a novel method for translating keyword queries over relational databases into SQL queries with the same intended semantic meaning. In contrast to the majority of the existing keyword-based techniques, our approach does not require any a-priori knowledge of the data instance. It follows a probabilistic approach based on a Hidden Markov Model for computing the top-K best mappings of the query keywords into the database terms, i.e., tables, attributes and values. The mappings are then used to generate the SQL queries that are executed to produce the answer to the keyword query. The method has been implemented into a system called KEYRY (from KEYword to queRY).