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.

通过使用 COVID-19 感染风险本体论（CIRO）的资源描述框架（RDF）和 SPARQL（SPARQL 协议和 RDF 查询语言）查询，本研究试图通过自动评估每个人的感染风险来减轻公共卫生官员的负担，并建立的知识图表明了其能够推断政府制定的风险，此外，还进行了推理实验以分析计算效率，发现了部署问题。