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Query Expansion Using Medical Information Extraction for Improving Information Retrieval in French Medical Domain

Query Expansion Using Medical Information Extraction for Improving Information Retrieval in French Medical Domain

Aicha Ghoulam, Fatiha Barigou, Ghalem Belalem, Farid Meziane
Copyright: © 2018 |Volume: 14 |Issue: 3 |Pages: 17
ISSN: 1548-3657|EISSN: 1548-3665|EISBN13: 9781522542803|DOI: 10.4018/IJIIT.2018070101
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MLA

Ghoulam, Aicha, et al. "Query Expansion Using Medical Information Extraction for Improving Information Retrieval in French Medical Domain." IJIIT vol.14, no.3 2018: pp.1-17. http://doi.org/10.4018/IJIIT.2018070101

APA

Ghoulam, A., Barigou, F., Belalem, G., & Meziane, F. (2018). Query Expansion Using Medical Information Extraction for Improving Information Retrieval in French Medical Domain. International Journal of Intelligent Information Technologies (IJIIT), 14(3), 1-17. http://doi.org/10.4018/IJIIT.2018070101

Chicago

Ghoulam, Aicha, et al. "Query Expansion Using Medical Information Extraction for Improving Information Retrieval in French Medical Domain," International Journal of Intelligent Information Technologies (IJIIT) 14, no.3: 1-17. http://doi.org/10.4018/IJIIT.2018070101

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Abstract

This article describes how many users' queries contain references to named entities, and this is particularly true in the medical field. Doctors express their information needs using medical entities as they are element rich with information that helps better target relevant documents. At the same time, many resources have been recognized as a large container of medical entities and relationships between them such as clinical reports; which are medical texts written by doctors. In this article, the authors present a query expansion method that uses medical entities and their semantic relations in the query context based on an external resource in OWL. The goal of this method is to evaluate the effectiveness of an information retrieval system to support doctors in accessing easily relevant information. Experiments on a collection of real clinical reports show that their approach reveals interesting improvements in precision, recall and MAP in medical information retrieval.

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