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Asking Data in a Controlled Way with Ask Data Anything NQL

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. Supplementary Information: Supplementary materials a

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Details

Category:
Conference activity
Type:
materiały konferencyjne indeksowane w Web of Science
Title of issue:
Controlled Natural Language strony 58 - 68
ISSN:
0302-9743
Language:
English
Publication year:
2016
Bibliographic description:
Seganti A., Kapłański P., Campo J., Cieśliński K., Koziołkiewicz J., Zarzycki P..: Asking Data in a Controlled Way with Ask Data Anything NQL, W: Controlled Natural Language, 2016, Springer,.
DOI:
Digital Object Identifier (open in new tab) 10.1007/978-3-319-41498-0_6
Verified by:
Gdańsk University of Technology

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