2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI) 2017
DOI: 10.1109/la-cci.2017.8285698
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Learning database queries via intelligent semiotic machines

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Cited by 3 publications
(1 citation statement)
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“…The clustering phase of our solution is in charge of the labeling, which means many more tuples can be labeled as it is automatic. Other approaches also exist to avoid manual tuple labeling, such as [26], that learns queries to help users in data exploration, using genetic programming algorithms. Some approaches from natural language processing are another alternative to interact with databases, such as in [27], or like [28] that highlights part of a natural language query to explain the non answers in the translated SQL query.…”
Section: Related Workmentioning
confidence: 99%
“…The clustering phase of our solution is in charge of the labeling, which means many more tuples can be labeled as it is automatic. Other approaches also exist to avoid manual tuple labeling, such as [26], that learns queries to help users in data exploration, using genetic programming algorithms. Some approaches from natural language processing are another alternative to interact with databases, such as in [27], or like [28] that highlights part of a natural language query to explain the non answers in the translated SQL query.…”
Section: Related Workmentioning
confidence: 99%