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Journal Articles International Journal of Web Information Systems Year : 2018

Ontology-based approach to enhance medical web information extraction

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Patient-Doctor conversations have become prevalent on the Web. For instance, solutions like HealthTap or AskTheDoctors allow patients to ask doctors health-related questions. However, most online health care consumers still struggle to express their questions efficiently due mainly to the expert/layman language and knowledge discrepancy. Extracting information from these layman descriptions, which typically lack expert terminology, is challenging. This hinders the efficiency of the underlying applications such as information retrieval. Herein, an ontology-driven approach is proposed, which aims at extracting information from such sparse descriptions using a meta-model. A meta-model is designed to bridge the gap between the vocabulary of the medical experts and the consumers of the health services. The meta-model is mapped with SNOMED-CT to access the comprehensive medical vocabulary as well as with WordNet to improve the coverage of layman terms during information extraction. In order to assess the potential of the approach, an information extraction prototype based on syntactical patterns is implemented. The evaluation of the approach on the gold standard corpus defined in Task1 of ShARe CLEF 2013 showed promising results, an F-score of 0.79 for recognizing medical concepts in real-life medical documents. The originality of the proposed approach lies in the way information is extracted. The context defined through a meta-model proved to be efficient for the task of information extraction, especially from layman descriptions.
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Dates and versions

hal-02982982 , version 1 (29-10-2020)



Nassim Otmani, Malik Si-Mohammed, Catherine Comparot, Pierre-Jean Charrel. Ontology-based approach to enhance medical web information extraction. International Journal of Web Information Systems, 2018, 15 (3), pp.402--422. ⟨10.1108/IJWIS-03-2018-0017⟩. ⟨hal-02982982⟩
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