An intelligent automatic query generation interface for relational databases using deep learning technique

In the emerging information retrieval world, the selection of keywords and the generation of queries are very critical for the efficient retrieval. The most new-generations database applications request keen interface to upgrade productive connections among various databases and the clients. Majority of the accessible interfaces for databases should be intelligent and also should understand natural language expressions. The paper deals with, the mapping of natural language queries which is in spoken form, and converting into words forming the foundation of SQL. We give a general framework for a smart database interface which could be linked to any database. One of the most striking features of this interface is domain-independence. The smart interface employs speech recognition techniques to convert spoken language input into text. Then a semantic matching technique is employed in converting natural language query to SQL words, complemented by using dictionary and a set of production rules. The dictionary consists of semantics sets for columns and tables. The identified type of query, query-word tuple is memorized and sent through a convolution neural network for better construction. This work tries to use natural language processing techniques, parsing and POS tagging to identify semantic structures of inputs. The natural language techniques are used to increase the production of unambiguous queries and speed in detecting and identifying the important parts in a query.

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