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Developing an Arabic Infectious Disease Ontology to Include Non-Standard Terminology

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published
Publication date11/05/2020
Host publication12th International Conference on Language Resources and Evaluation: LREC2020
Place of PublicationParis
PublisherEuropean Language Resources Association (ELRA)
Pages4842-4850
ISBN (Print)9791095546344
<mark>Original language</mark>English
EventThe 12th Edition of the Language Resources and Evaluation Conference (LREC2020) - Le Palais du Pharo, Marseille, France
Duration: 11/05/202016/05/2020
https://lrec2020.lrec-conf.org/en/

Conference

ConferenceThe 12th Edition of the Language Resources and Evaluation Conference (LREC2020)
Abbreviated titleLREC'20
Country/TerritoryFrance
CityMarseille
Period11/05/2016/05/20
Internet address

Conference

ConferenceThe 12th Edition of the Language Resources and Evaluation Conference (LREC2020)
Abbreviated titleLREC'20
Country/TerritoryFrance
CityMarseille
Period11/05/2016/05/20
Internet address

Abstract

Building ontologies is a crucial part of the semantic web endeavour. In recent years, research interest has grown rapidly in supporting languages such as Arabic in NLP in general but there has been very little research on medical ontologies for Arabic.
We present a new Arabic ontology in the infectious disease domain to support various important applications including the monitoring of infectious disease spread via social media. This ontology meaningfully integrates the scientific vocabularies of infectious diseases with their informal equivalents. We use ontology learning strategies with manual checking to build the ontology. We applied three statistical methods for term extraction from selected Arabic infectious diseases articles: TF-IDF, C-value, and YAKE. We also conducted a study, by consulting around 100 individuals, to discover the informal terms related to infectious diseases in Arabic. In future work, we will automatically extract the relations for infectious disease concepts but for now these are manually created. We report two complementary experiments to evaluate the ontology. First, a quantitative evaluation of the term extraction results and an additional qualitative evaluation by a domain expert.