Optimizing a query by transformation and expansion
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 answer...
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| Main Authors: | , , |
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| Format: | Chapter/Article Conference Paper |
| Language: | English |
| Published: |
2017
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| In: |
German medical data sciences
Year: 2017, Pages: 197-201 |
| DOI: | 10.3233/978-1-61499-808-2-197 |
| Subjects: | |
| Online Access: | Verlag, Volltext: http://dx.doi.org/10.3233/978-1-61499-808-2-197 Verlag, Volltext: http://ebooks.iospress.nl/publication/47536 
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| Author Notes: | Katrin Glocker, Alexander Knurr, Julia Dieter, Friederike Dominick, Melanie Forche, Christian Koch, Analie Pascoe Pérez, Benjamin Roth and Frank Ückert |
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| 520 | |a 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. | ||
| 650 | 4 | |a Algorithms | |
| 650 | 4 | |a Data Mining | |
| 650 | 4 | |a Databases, Factual | |
| 650 | 4 | |a Humans | |
| 650 | 4 | |a Information Management | |
| 650 | 4 | |a Information Storage and Retrieval | |
| 650 | 4 | |a Medical Informatics | |
| 650 | 4 | |a Semantics | |
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