Manufacturing activities in cloud-based environments strongly rely on the online integration and description of the capability of machining resources. In the authors’ previous work, STEP-NC schema was applied to construct an ontology model to support the integration and reasoning of machine tool information and capability. For the maintenance and update of the model, in this paper, a self-learning method is proposed to explore correlations from STEP-NC process planning documents to obtain machining knowledge to improve the comprehensiveness of the model. In this method, a Map/Reduce-based Apriori algorithm is developed incombination with the built ontological model. First, a dataset is extracted from the document according to the importance analysis results of the model. Then, a mining procedure that combinesApriori algorithm and Map/Reduce framework is developed. Finally, two representation modes are adopted to embed the mined results into the model. According to the outcomes of a preliminary experiment with standard STEP-NC documents, this method effectively enables the ontology learning mechanism from the aspects of time consumption and mined associations, which improves the suitability of the enriched ontology model to handle information integration and industrial applications.

基于云的环境中的制造活动强烈依赖于加工资源能力的在线集成和描述。在作者之前的工作中，STEP-NC 模式被应用于构建本体模型，以支持机床信息和能力的集成和推理。针对模型的维护和更新，本文提出了一种自学习的方法，从STEP-NC工艺规划文档中挖掘相关性，获取加工知识，提高模型的综合性。在该方法中，结合构建的本体模型开发了基于 Map/Reduce 的 Apriori 算法。首先，根据模型的重要性分析结果从文档中提取数据集。然后，开发了结合Apriori算法和Map/Reduce框架的挖掘程序。最后，采用两种表示方式将挖掘结果嵌入到模型中。根据标准STEP-NC文档的初步实验结果，该方法从时间消耗和挖掘关联方面有效地启用了本体学习机制，提高了丰富的本体模型处理信息集成和工业应用的适用性。