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Towards a natural language-based interface for querying hospital data

Published: 18 May 2018 Publication History

Abstract

There is a growing necessity in various domains for non-programmers to be able to retrieve information gathered about the operation of the organization and stored in its databases. This information could hugely benefit the decision making process of the managers of the institution, but it is not often exploited due to the complexity of extracting the information from the existing data. In this paper we sketch a way how that information could be managed by the domain experts themselves by the means of a natural language-based query language that works upon data stored in the ontology. Our experiments show that the proposed approach is indeed easy-to-use by our target end-users - managers and physicians of hospitals -, because lacking technical details the query language is very intuitive to use.

References

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Aspin, A. 2014. Self-Service Business Intelligence. High Impact Data Visualization with Power View, Power Map, and Power BI, 1--18, Apress.
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Kogalovsky, M.R. 2012. Ontology-Based Data Access Systems. Programming and Computer Software. 38.4.
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Optique: Scalable End-user Access to Big Data, http://optique-project.eu
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Fei, L., and Jagadish, H.V. 2014. NaLIR: An interactive natural language interface for querying relational databases. Proceedings of the ACM SIGMOD International Conference on Management of Data.
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Gao, T., Dontcheva, M., Adar, E., Liu, Z., and Karahalios, K.G. 2015. DataTone: Managing Ambiguity in Natural Language Interfaces for Data Visualization. Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology (UIST '15). ACM, New York, NY, USA, 489--500.
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Barzdins, J., Grasmanis, M., Rencis, E., Sostaks, A., and Barzdins, J. 2016. Self-service Ad-hoc Querying Using Controlled Natural Language. G. Arnicans et al. (Eds.) Proc. of the 12th International Baltic Conference, Baltic DB&IS, 18--34, CCIS 615.
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Barzdins, J., Grasmanis, M., Rencis, E., Sostaks, A., Barzdins, J. 2016. Ad-hoc Querying of Semistar Data Ontologies Using Controlled Natural Language. Frontiers in Artificial Intelligence and Applications, Vol. 291, Databases and Information Systems IX, IOS Press, 3--16.
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Barzdins, J., Grasmanis, M., Rencis, E., Sostaks, A., and Steinsbekk, A. 2016. Towards a more effective hospital: helping health professionals to learn from their own practice by developing an easy to use clinical processes querying language. International Conference on Health and Social Care Information Systems and Technologies, Procedia Computer Science Journal, 100, 498--506.

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  • (2020)Knowledge Extraction from Healthcare Data Using User-Adaptable Keywords-Based Query LanguageProceedings of the 2020 the 4th International Conference on Information System and Data Mining10.1145/3404663.3406876(128-131)Online publication date: 15-May-2020

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cover image ACM Other conferences
ICBDT '18: Proceedings of the 1st International Conference on Big Data Technologies
May 2018
144 pages
ISBN:9781450364270
DOI:10.1145/3226116
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 May 2018

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Author Tags

  1. hospital management
  2. natural language processing
  3. query language
  4. query translation

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  • (2020)Knowledge Extraction from Healthcare Data Using User-Adaptable Keywords-Based Query LanguageProceedings of the 2020 the 4th International Conference on Information System and Data Mining10.1145/3404663.3406876(128-131)Online publication date: 15-May-2020

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