The increasing digitalization of the manufacturing domain requires adequate knowledge modeling to capture relevant information. Ontologies and Knowledge Graphs provide means to model and relate a wide range of concepts, problems, and configurations. Both can be used to generate new knowledge through deductive inference and identify missing knowledge. While digitalization increases the amount of data available, much data is not labeled and cannot be directly used to train supervised machine learning models. Active learning can be used to identify the most informative data instances for which to obtain users' feedback, reduce friction, and maximize knowledge acquisition. By combining semantic technologies and active learning, multiple use cases in the manufacturing domain can be addressed taking advantage of the available knowledge and data.
翻译:制造领域日益数字化需要适当的知识建模,以获取相关信息; 主题和知识图提供了建模手段,并涉及各种概念、问题和配置; 两者都可用于通过推论推论产生新知识,并查明缺失的知识; 虽然数字化增加了现有数据的数量,但许多数据没有贴上标签,无法直接用于培训受监督的机器学习模式; 积极学习可以用来确定获取用户反馈、减少摩擦和最大限度地获取知识的最丰富数据实例; 通过将语义技术和积极学习相结合,可以利用现有知识和数据处理制造领域的多种使用案例。