Smart factories are equipped with machines that can sense their manufacturing environments, interact with each other, and control production processes. Smooth operation of such factories requires that the machines and engineering personnel that conduct their monitoring and diagnostics share a detailed common industrial knowledge about the factory, e.g., in the form of knowledge graphs. Creation and maintenance of such knowledge is expensive and requires automation. In this work we show how machine learning that is specifically tailored towards industrial applications can help in knowledge graph completion. In particular, we show how knowledge completion can benefit from event logs that are common in smart factories. We evaluate this on the knowledge graph from a real world-inspired smart factory with encouraging results.

翻译：智能工厂配备了能够感知其制造环境、彼此互动和控制生产过程的机器。这类工厂的顺利运作要求进行其监测和诊断的机器和工程人员分享有关工厂的详细共同的工业知识,例如以知识图的形式。这种知识的创造和维护是昂贵的,需要自动化。在这项工作中,我们展示了具体针对工业应用的机器学习如何有助于完成知识图的完成。特别是,我们展示了知识的完成如何受益于智能工厂常见的事件日志。我们用一个真正受世界启发的智能工厂的知识图对此进行了评估,并取得了令人鼓舞的成果。