Analyzing and Predicting COVID-19 Effect using Semantic Web RDF Data
In the present times, Corona virus (COVID-19) has affected the entire world which is of prime significance and needs immediate attention. The need is to analyze and predict about the huge COVID datasets scattered on web which may be in unstructured or semi-structured format. Semantic web has the potential to facilitate it by making this data machine understandable by its RDF technology. In this paper, in regard to this context, COVID-19 RDF data has been generated and analyzed. Further a comparative analysis has been made of different prediction models like linear regression, polynomial regression and Holts Models (Time Series forecasting). The prediction models proposed have considerable significance when it works with RDF dataset which is more flexible and extensible with context to semantic web. For the above, Semantic web technologies, SPARQL and RDF have been used along with python language for implementation. The prediction models have been compared and illustrated in context to COVID spread around the globe as well as in India.