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.

翻译：在现实世界任务中，通常存在大量未标记数据和已标记数据。结合二者进行学习的任务被称为半监督学习。专家可利用逻辑规则为未标记数据添加标签，但这一操作成本高昂。感知与推理的结合在处理此类包含领域知识的半监督任务中具有良好效果。然而，领域知识的获取以及规则的修正、约简与生成仍是待解决的复杂问题。粗糙集理论是解决信息系统中知识处理问题的重要方法。本文提出一种基于粗糙集的规则归纳式溯因学习方法（RS-ABL）。通过将目标概念和规则的子概念转化为信息表，利用粗糙集理论以较低成本解决领域知识获取及规则的修正、约简与生成问题。该框架还能生成更广泛的负向规则以增强知识库的广度。与传统半监督学习方法相比，RS-ABL在处理半监督任务时具有更高的准确率。