Link Ontology Analytics: Representing COVID-19 Data as Network Links

The COVID-19 pandemic has significantly impacted the global population, and understanding the spread and impact of the disease is crucial to managing the crisis. In this paper, we propose the Link Ontology Analytics approach for creating an RDF (Resource Description Framework) dataset of COVID-19 data to facilitate knowledge discovery and improve the decision-making process. The dataset includes confirmed cases, deaths, recoveries, and testing data from various sources. First step pre-processes the collected big data, using Natural Language Processing (NLP) techniques to extract relevant information from unstructured data sources. Second step uses rule mining to retrieve HeathCare Rules and ontologies to represent the big data in a consistent format and to provide a common vocabulary for querying the big data. Third, our resulting dataset provides a comprehensive and structured representation of COVID-19 data that can be used to analyze HeathCare Rules and relationships related to the spread and impact of the disease. We evaluate the dataset by performing various queries and analyzing the results to demonstrate its usefulness for knowledge discovery and decision-making.