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Stud Health Technol Inform. 2017;243:197-201.

Optimizing a Query by Transformation and Expansion.

Author information

1
Division of Medical Informatics for Translational Oncology, Deutsches Krebsforschungszentrum, Heidelberg, Germany.
2
National Center for Tumor Diseases (NCT), Heidelberg, Germany.

Abstract

In the biomedical sector not only the amount of information produced and uploaded into the web is enormous, but also the number of sources where these data can be found. Clinicians and researchers spend huge amounts of time on trying to access this information and to filter the most important answers to a given question. As the formulation of these queries is crucial, automated query expansion is an effective tool to optimize a query and receive the best possible results. In this paper we introduce the concept of a workflow for an optimization of queries in the medical and biological sector by using a series of tools for expansion and transformation of the query. After the definition of attributes by the user, the query string is compared to previous queries in order to add semantic co-occurring terms to the query. Additionally, the query is enlarged by an inclusion of synonyms. The translation into database specific ontologies ensures the optimal query formulation for the chosen database(s). As this process can be performed in various databases at once, the results are ranked and normalized in order to achieve a comparable list of answers for a question.

KEYWORDS:

Data Mining; Information Management; Information Storage and Retrieval; Medical Informatics

PMID:
28883200
[Indexed for MEDLINE]

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