Asking Data in a Controlled Way with Ask Data Anything NQL
Authors: 
Alessandro Seganti, Paweł Kapłański, Jesus David Campo, Krzysztof Cieśliński, Jerzy Koziołkiewicz, Paweł ZarzyckiAuthors Info & Claims
CNL 2016: Proceedings of the 5th International Workshop on Controlled Natural Language - Volume 9767
Pages 58 - 68
Abstract
While to collect data, it is necessary to store it, to understand its structure it is necessary to do data-mining. Business Intelligence BI enables us to make intelligent, data-driven decisions by the mean of a set of tools that allows the creation of a potentially unlimited number of machine-generated, data-driven reports, which are calculated by a machine as a response to queries specified by humans. Natural Query Languages NQLs allow one to dig into data with an intuitive human-machine dialogue. The current NQL-based systems main problems are the required prior learning phase for writing correct queries, understanding the linguistic coverage of the NQL and asking precise questions.
Results: We have developed an NQL as well as an entire Natural Language Interface Database NLIDB that supports the user with BI queries with minimized disadvantages, namely Ask Data Anything. The core part - NQL parser - is a hybrid of CNL and the pattern matching approach with a prior error repair phase. Equipped with reasoning capabilities due to the intensive use of semantic technologies, our hybrid approach allows one to use very simple, keyword-based even erroneous queries as well as complex CNL ones with the support of a predictive editor.
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- Asking Data in a Controlled Way with Ask Data Anything NQL
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Published: 25 July 2016
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