Abstract

Industry 4.0 (I4.0) is a new era in the industrial revolution that emphasizes machine connectivity, automation, and data analytics. The I4.0 pillars such as autonomous robots, cloud computing, horizontal and vertical system integration, and the industrial internet of things have increased the performance and efficiency of production lines in the manufacturing industry. Over the past years, efforts have been made to propose semantic models to represent the manufacturing domain knowledge, one such model is Reference Generalized Ontological Model (RGOM).11 However, its adaptability like other models is not ensured due to the lack of manufacturing data. In this paper, we aim to develop a benchmark dataset for knowledge graph generation in Industry 4.0 production lines and to show the benefits of using ontologies and semantic annotations of data to showcase how the I4.0 industry can benefit from KGs and semantic datasets. This work is the result of collaboration with the production line managers, supervisors, and engineers in the football industry to acquire realistic production line data22^,.33 Knowledge Graphs (KGs) or Knowledge Graph (KG) have emerged as a significant technology to store the semantics of the domain entities. KGs have been used in a variety of industries, including banking, the automobile industry, oil and gas, pharmaceutical and health care, publishing, media, etc. The data is mapped and populated to the RGOM classes and relationships using an automated solution based on JenaAPI, producing an I4.0 KG. It contains more than 2.5 million axioms and about 1 million instances. This KG enables us to demonstrate the adaptability and usefulness of the RGOM. Our research helps the production line staff to take timely decisions by exploiting the information embedded in the KG. In relation to this, the RGOM adaptability is demonstrated with the help of a use case scenario to discover required information such as current temperature at a particular time, the status of the motor, tools deployed on the machine, etc.

中文翻译：

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用于工业 4.0 生产线的知识图谱生成基准数据集

摘要

工业 4.0 (I4.0) 是工业革命的新时代，强调机器连接、自动化和数据分析。自主机器人、云计算、横向和纵向系统集成、工业物联网等I4.0支柱提高了制造业生产线的性能和效率。在过去的几年里，人们一直在努力提出语义模型来表示制造领域知识，其中一种模型是参考广义本体模型（RGOM）。 11但是，由于缺乏制造数据，它与其他模型一样的适应性无法保证。在本文中，我们的目标是开发一个用于工业 4.0 生产线中知识图谱生成的基准数据集，并展示使用数据本体和语义注释的好处，以展示 I4.0 行业如何从知识图谱和语义数据集中受益。这项工作是与足球行业的生产线经理、主管和工程师合作的结果，以获取真实的生产线数据22 ^, .33 知识图谱 (KG) 或知识图谱 (KG) 已成为存储生产线数据的重要技术。领域实体的语义。 KG 已用于多种行业，包括银行、汽车工业、石油和天然气、制药和医疗保健、出版、媒体等。使用基于JenaAPI，生产 I4.0 KG。它包含超过 250 万条公理和大约 100 万个实例。该 KG 使我们能够展示 RGOM 的适应性和实用性。我们的研究通过利用知识图谱中嵌入的信息帮助生产线员工及时做出决策。与此相关的是，RGOM 的适应性在用例场景的帮助下得到了证明，以发现所需的信息，例如特定时间的当前温度、电机的状态、机器上部署的工具等。