Ontology-based approach to enhance medical web information extraction
| Autor: | Pierre-Jean Charrel, Malik Si-Mohammed, Catherine Comparot, Nassim Abdeldjallal Otmani |
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| Přispěvatelé: | University of Tizi-Ouzou, Université Mouloud Mammeri [Tizi Ouzou] (UMMTO), MEthodes et ingénierie des Langues, des Ontologies et du DIscours (IRIT-MELODI), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Université Toulouse - Jean Jaurès (UT2J), Smart Modeling for softw@re Research and Technology (IRIT-SM@RT) |
| Rok vydání: | 2019 |
| Předmět: |
Vocabulary
Information retrieval Computer Networks and Communications Computer science media_common.quotation_subject Knowledge engineering Metadata and ontologies Online patient-doctor conversation WordNet Context (language use) 02 engineering and technology Ontology (information science) computer.software_genre Terminology Domain (software engineering) Information extraction Web search and information extraction 020204 information systems 0202 electrical engineering electronic engineering information engineering [INFO]Computer Science [cs] 020201 artificial intelligence & image processing computer Information Systems media_common |
| Zdroj: | International Journal of Web Information Systems International Journal of Web Information Systems, Emerald Publishing Limited, 2018, 15 (3), pp.402--422. ⟨10.1108/IJWIS-03-2018-0017⟩ |
| ISSN: | 1744-0084 |
| DOI: | 10.1108/ijwis-03-2018-0017 |
| Popis: | Purpose The purpose of this study is to propose a framework for extracting medical information from the Web using domain ontologies. 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. Design/methodology/approach 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. To assess the potential of the approach, an information extraction prototype based on syntactical patterns is implemented. Findings 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. Originality/value 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. |
| Databáze: | OpenAIRE |
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