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。

CIRO: COVID-19 感染风险本体

CIRO: COVID-19 infection risk ontology

In real-world tasks, there is usually a large amount of unlabeled data and
labeled data. The task of combining the two to learn is known as
semi-supervised learning. Experts can use logical rules to label unlabeled
data, but this operation is costly. The combination of perception and reasoning
has a good effect in processing such semi-supervised tasks with domain
knowledge. However, acquiring domain knowledge and the correction, reduction
and generation of rules remain complex problems to be solved. Rough set theory
is an important method for solving knowledge processing in information systems.
In this paper, we propose a rule general abductive learning by rough set
(RS-ABL). By transforming the target concept and sub-concepts of rules into
information tables, rough set theory is used to solve the acquisition of domain
knowledge and the correction, reduction and generation of rules at a lower
cost. This framework can also generate more extensive negative rules to enhance
the breadth of the knowledge base. Compared with the traditional
semi-supervised learning method, RS-ABL has higher accuracy in dealing with
semi-supervised tasks.

本文提出了一种基于粗集理论的规则一般性诱导方法（Rule General Abductive Learning by Rough Set，RS-ABL），用于解决信息系统中知识处理中的知识获取、纠错、减少、生成和协同处理等问题，并在解决半监督任务时具有更高的准确性。

基于粗糙集的归纳推理学习算法

A rule-general abductive learning by rough sets

As an emerging concept cognitive learning model, partial order formal
structure analysis (POFSA) has been widely used in the field of knowledge
processing. In this paper, we propose the method named three-way causal
attribute partial order structure (3WCAPOS) to evolve the POFSA from set
coverage to causal coverage in order to increase the interpretability and
classification performance of the model. First, the concept of causal factor
(CF) is proposed to evaluate the causal correlation between attributes and
decision attributes in the formal decision context. Then, combining CF with
attribute partial order structure, the concept of causal attribute partial
order structure is defined and makes set coverage evolve into causal coverage.
Finally, combined with the idea of three-way decision, 3WCAPOS is formed, which
makes the purity of nodes in the structure clearer and the changes between
levels more obviously. In addition, the experiments are carried out from the
classification ability and the interpretability of the structure through the
six datasets. Through these experiments, it is concluded the accuracy of
3WCAPOS is improved by 1% - 9% compared with classification and regression
tree, and more interpretable and the processing of knowledge is more reasonable
compared with attribute partial order structure.

本研究提出三维因果属性偏序结构 (3WCAPOS) 方法，将偏序形式分析 (POFSA) 从集覆盖扩展到因果覆盖，提高了模型的可解释性和分类性能，实验证明 3WCAPOS 的分类准确率比分类回归树高 1%-9%，知识处理更加合理。