  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.
