Analysis of the Impact of COVID-19 Ontologies

The COVID-19 pandemic slowed down or even often stopped the world’s activities in economy, education, society and other areas of our daily life, but was a huge trigger for research. Several COVID-19 related ontologies are considered and often published together with large instance data forming a knowledge graph, which serves as the basis for many applications related to COVID-19. All the outspread COVID-19 associated data and technologies can be organized and integrated using knowledge graphs. Numerous ontologies have been developed to represent data and knowledge about the COVID-19 pandemic. We have analyzed the properties of and validated ontologies and knowledge graphs dealing with the COVID-19 virus and pandemic. We devised a first-step approach to measure the impact of ontologies. Our approach is based on ontologies’ direct and indirect reuses on other projects or ontologies. The results of this analysis about the impact of COVID-19 ontologies might help data scientists to choose those ontologies promising the best interoperability with other applications and tools. Furthermore, we have reviewed a couple of COVID-19 knowledge graphs and datasets to explore the impact of COVID-19 ontologies. This tutorial will present:

1. Important COVID-19 ontologies
2. Validating COVID-19 Ontologies and Knowledge Graphs
3. Measuring Impact of Ontologies
4. COVID-19 Datasets