Automated Knowledge Integration from Heterogeneous Data Sources Using Text Analytics: A Case Study of COVID-19
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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 support such heterogeneity. In addition, semantic of data 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. Our work focuses on five COVID- 19 knowledge scopes: COVID-19, coronaviruses, diseases, pandemics, and vaccines. For evaluation, six ontologies were constructed with six different cosine similarity values ranging from 0.5 to 1.0. Each constructed ontology has COVID-19 related and non-COVID-19 data 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 0.82, 0.71, and 0.76, respectively, and the constructed ontology is optimal, containing the highest amount of relevant COVID-19 information for this case study.
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