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Knowledge Acquisition Through Ontologies from Medical Natural Language Texts

Knowledge Acquisition Through Ontologies from Medical Natural Language Texts

José Medina-Moreira, Katty Lagos-Ortiz, Harry Luna-Aveiga, Oscar Apolinario-Arzube, María del Pilar Salas-Zárate, Rafael Valencia-García
Copyright: © 2017 |Volume: 10 |Issue: 4 |Pages: 14
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781522511908|DOI: 10.4018/JITR.2017100104
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MLA

Medina-Moreira, José, et al. "Knowledge Acquisition Through Ontologies from Medical Natural Language Texts." JITR vol.10, no.4 2017: pp.56-69. http://doi.org/10.4018/JITR.2017100104

APA

Medina-Moreira, J., Lagos-Ortiz, K., Luna-Aveiga, H., Apolinario-Arzube, O., Salas-Zárate, M. D., & Valencia-García, R. (2017). Knowledge Acquisition Through Ontologies from Medical Natural Language Texts. Journal of Information Technology Research (JITR), 10(4), 56-69. http://doi.org/10.4018/JITR.2017100104

Chicago

Medina-Moreira, José, et al. "Knowledge Acquisition Through Ontologies from Medical Natural Language Texts," Journal of Information Technology Research (JITR) 10, no.4: 56-69. http://doi.org/10.4018/JITR.2017100104

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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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