การพัฒนาออนโทโลยีโควิด-19 จากแหล่งข้อมูลที่หลากหลายโดยใช้การวิเคราะห์ข้อความ

Abstract

Gathering information from multiple data sources takes a long time to collect, analyze and classify. Furthermore, if the data sources have different data structures, the merged data structure must be able to support such heterogeneity. In addition, semantics must also be considered. This paper proposes automated knowledge integration from heterogeneous data sources, using ontology engineering combined with text analytics. Text stemming is used to preprocess data. Part-of-speech (POS) tagging, Universal Dependencies (UD), and text similarity measurement called cosine similarity are used to analyze and integrate data. The knowledge scopes focus on five perspectives of COVID-19 information: COVID-19, Coronavirus, disease, pandemic, and vaccine. For evaluation, six ontologies were constructed using cosine similarity measurement ranged from 0.5 to 1.0. The data used in each ontology construction contain data related and unrelated to COVID-19 in a ratio of 70 to 30. The six constructed ontologies were evaluated for consistency with the original data. Using cosine similarity with 0.6, precision, recall, and F1-score are 0.80, 0.70, and 0.75, respectively, and the constructed ontology is optimal containing the highest amount of relevant COVID-19 information for this case study.