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Optimizing a Query by Transformation and Expansion

机译:通过转换和扩展优化查询

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摘要

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.
机译:在生物医学部门中,不仅可以生成并上传到Web的信息量是巨大的,而且可以找到这些数据的源代码数。临床医生和研究人员花费大量的时间试图访问这些信息,并过滤给定问题的最重要答案。随着这些查询的制定至关重要,自动查询扩展是优化查询并获得最佳结果的有效工具。在本文中,我们通过使用一系列用于查询的扩展和转换的工具来介绍医疗和生物扇区中查询的工作流程的概念。在用户定义属性之后,将查询字符串与先前查询进行比较,以便向查询添加语义共同术语。此外,查询通过包含同义词而放大。转换到数据库特定的本体中,可确保所选数据库的最佳查询配方。由于此过程一次可以在各种数据库中执行,结果排序和标准化,以便实现问题的可比答案列表。

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