  Public health authorities perform contact tracing for highly contagious
agents to identify close contacts with the infected cases. However, during the
pandemic caused by coronavirus disease 2019 (COVID-19), this operation was not
employed in countries with high patient volumes. Meanwhile, the Japanese
government conducted this operation, thereby contributing to the control of
infections, at the cost of arduous manual labor by public health officials. To
ease the burden of the officials, this study attempted to automate the
assessment of each person's infection risk through an ontology, called COVID-19
Infection Risk Ontology (CIRO). This ontology expresses infection risks of
COVID-19 formulated by the Japanese government, toward automated assessment of
infection risks of individuals, using Resource Description Framework (RDF) and
SPARQL (SPARQL Protocol and RDF Query Language) queries. For evaluation, we
demonstrated that the knowledge graph built could infer the risks, formulated
by the government. Moreover, we conducted reasoning experiments to analyze the
computational efficiency. The experiments demonstrated usefulness of the
knowledge processing, and identified issues left for deployment.
