Towards Generalized Welding Ontology in Line with ISO and Knowledge Graph Construction

Yahya, Muhammad; Zhou, Baifan; Zheng, Zhuoxun; Zhou, Dongzhuoran; Breslin, John G.; Ali, Mohammad; Kharlamov, Evgeny

doi:10.1007/978-3-031-11609-4_16

Cited by 11 publications

(6 citation statements)

References 18 publications

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“…We have created new concepts such as WeldingMachine as Machine, WeldingRobot, WeldingGun, and Electrode as Tool, WeldingOperation and MaintenanceOperation as Operation, and Workpiece as Material. This developed welding ontology demonstrates the adaptability of RGOM [38]. Adopting a similar mechanism will help digital transformation for those who have not set up a Linked Data-based production line.…”

Section: Extension Of the Benchmark To Other Use Casesmentioning

confidence: 92%

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

Yahya

Ali²,

Mehmood

et al. 2024

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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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Section: Extension Of the Benchmark To Other Use Casesmentioning

confidence: 92%

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

Yahya

Ali²,

Mehmood

et al. 2024

Self Cite

View full text Add to dashboard Cite

show abstract

“…In modern manufacturing industries, machine learning (ML) technologies attracts substantial yet increasing attention thanks to its strong modelling capability without the need of explicit programming . Take the Resistance Spot Welding at Bosch as an example, which is a type of fully automated and impactful manufacturing process widely applied in automotive industry, accounting for the production of millions of cars globally every year [1]. To ensure the welding quality, traditional quality monitoring approaches often require tearing the welded car bodies apart , which is extremely costly and produces much waste .…”

Section: Talk Descriptionmentioning

confidence: 99%

Executable Knowledge Graph for Transparent Machine Learning in Welding Monitoring at Bosch

Zheng

Zhou

et al. 2022

Proceedings of the 31st ACM International Conference on Information &Amp; Knowledge Management

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With the development of Industry 4.0 technology, modern industries such as Bosch's welding monitoring witnessed the rapid widespread of machine learning (ML) based data analytical applications, which in the case of welding monitoring has led to more efficient and accurate welding monitoring quality. However, industrial ML is affected by the low transparency of ML towards non-ML experts needs. The lack of understanding by domain experts of ML methods hampers the application of ML methods in industry and the reuse of developed ML pipelines, as ML methods are often developed in an ad hoc manner for specific problems. To address these challenges, we propose the concept and a system of executable Knowledge Graph (KG), which formally encode ML knowledge and solutions in KGs, which serve as common language between ML experts and non-ML experts, thus facilitate their communication and increase the transparency of ML methods. We evaluated our system extensively with an industrial use case at Bosch, showing promising results.Speaker's Bio: Zhuoxun Zheng is a Data Scientist and Doctoral Researcher at Bosch Center for Artificial Intelligence. He obtained his Master's degree in Mechanical Engineering at Karlsruhe Institute of Technology. Currently, he is working on knowledge enhanced machine learning and neuro-symbolic reasoning to increase transparency, trust, and performance of ML.Relevancy to CIKM: Knowledge enhanced ML in the industry.

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“…However, poor design of ontologies or high degree of mismatch between them and industrial data, e.g., when the ontology is designed to reflect the general domain of knowledge or exported, rather than data particularities [7] naturally lead to KGs of low quality that impede the adoption and scalability of industrial analytics. Indeed, such KGs often have deep structure or have many blank nodes thus they are sparse and consume a high volume of storage for redundant information, and are hard to maintain and update.…”

Section: Introductionmentioning

confidence: 99%

Query-Based Industrial Analytics over Knowledge Graphs with Ontology Reshaping

Zheng

Zhou

et al. 2022

The Semantic Web: ESWC 2022 Satellite Events

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Industrial analytics that includes among others equipment diagnosis and anomaly detection heavily relies on integration of heterogeneous production data. Knowledge Graphs (KGs) as the data format and ontologies as the unified data schemata are a prominent solution that offers high quality data integration and a convenient and standardised way to exchange data and to layer analytical applications over it. However, poor design of ontologies of high degree of mismatch between them and industrial data naturally lead to KGs of low quality that impede the adoption and scalability of industrial analytics. Indeed, such KGs substantially increase the training time of writing queries for users, consume high volume of storage for redundant information, and are hard to maintain and update. To address this problem we propose an ontology reshaping approach to transform ontologies into KG schemata that better reflect the underlying data and thus help to construct better KGs. In this poster we present a preliminary discussion of our on-going research, evaluate our approach with a rich set of SPARQL queries on real-world industry data at Bosch and discuss our findings.

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Towards Generalized Welding Ontology in Line with ISO and Knowledge Graph Construction

Cited by 11 publications

References 18 publications

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

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

Executable Knowledge Graph for Transparent Machine Learning in Welding Monitoring at Bosch

Query-Based Industrial Analytics over Knowledge Graphs with Ontology Reshaping

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