A benchmark dataset with Knowledge Graph generation for Industry 4.0 production lines

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Semantic Web Pub Date : 2023-06-13 DOI:10.3233/sw-233431

Muhammad Yahya, Aabid Ali, Qaiser Mehmood, Lan Yang, J. Breslin, M. Ali

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引用次数: 0

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 https://w3id.org/rgom 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 https://github.com/MuhammadYahta/ManufacturingProductionLineDataSetGeneration-Football,.33 https://zenodo.org/record/7779522 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.

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具有工业4.0生产线知识图谱生成的基准数据集

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、工业物联网等工业4.0支柱提升了制造业生产线的性能和效率。近年来，人们提出了表示制造领域知识的语义模型，其中一种模型是参考广义本体论模型(RGOM)。11 https://w3id.org/rgom但由于缺乏制造数据，不能保证其与其他模型一样的适应性。在本文中，我们的目标是为工业4.0生产线中的知识图生成开发一个基准数据集，并展示使用数据本体和语义注释的好处，以展示工业4.0如何从KGs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 https://github.com/MuhammadYahta/ManufacturingProductionLineDataSetGeneration-Football，.33 https://zenodo.org/record/7779522知识图(KGs)或知识图(KG)已经成为存储领域实体语义的重要技术。KGs已用于各种行业，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的自动化解决方案，将数据映射并填充到RGOM类和关系中，生成I4.0 KG。它包含超过250万个公理和大约100万个实例。这个KG使我们能够演示RGOM的适应性和有用性。我们的研究通过利用嵌入在KG中的信息来帮助生产线员工及时做出决策。与此相关的是，RGOM的适应性在一个用例场景的帮助下进行了演示，以发现所需的信息，如特定时间的当前温度、电机状态、机器上部署的工具等。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Semantic Web COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS

CiteScore

8.30

自引率

6.70%

发文量

期刊介绍： The journal Semantic Web – Interoperability, Usability, Applicability brings together researchers from various fields which share the vision and need for more effective and meaningful ways to share information across agents and services on the future internet and elsewhere. As such, Semantic Web technologies shall support the seamless integration of data, on-the-fly composition and interoperation of Web services, as well as more intuitive search engines. The semantics – or meaning – of information, however, cannot be defined without a context, which makes personalization, trust, and provenance core topics for Semantic Web research. New retrieval paradigms, user interfaces, and visualization techniques have to unleash the power of the Semantic Web and at the same time hide its complexity from the user. Based on this vision, the journal welcomes contributions ranging from theoretical and foundational research over methods and tools to descriptions of concrete ontologies and applications in all areas. We especially welcome papers which add a social, spatial, and temporal dimension to Semantic Web research, as well as application-oriented papers making use of formal semantics.