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Knowledge Acquisition Through Ontologies from Medical Natural Language Texts
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Author(s): José Medina-Moreira (University of Guayaquil, Ecuador), Katty Lagos-Ortiz (University of Guayaquil, Ecuador), Harry Luna-Aveiga (University of Guayaquil, Ecuador), Oscar Apolinario-Arzube (University of Guayaquil, Ecuador), María del Pilar Salas-Zárate (University of Murcia, Spain)and Rafael Valencia-García (University of Murcia, Spain)
Copyright: 2020
Pages: 15
Source title:
Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-1204-3.ch053
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Abstract
Ontologies are used to represent knowledge and they have become very important in the Semantic Web era. Ontologies evolve continuously during their life cycle to adapt to new requirements and needs, especially in the biomedical field, where the number of ontologies and their complexity have increased during the last years. On the other hand, a vast amount of clinical knowledge resides in natural language texts. For these reasons, building and maintaining biomedical ontologies from natural language texts is a relevant and challenging issue. In order to provide a general solution and to minimize the experts' participation during the ontology enriching process, a methodology for extracting terms and relations from natural language texts is proposed in this work. This framework is based on linguistic and statistical methods and semantic role labeling technologies, having been validated in the domain of diabetes, where they have obtained encouraging results with an F-measure of 82.1% and 79.9% for concepts and relations, respectively.
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