Executable Knowledge Graphs for Machine Learning: A Bosch Case of Welding Monitoring

Zheng, Zhuoxun; Zhou, Baifan; Zhou, Dongzhuoran; Zheng, Xianda; Cheng, Gong; Soylu, Ahmet; Kharlamov, Evgeny

doi:10.1007/978-3-031-19433-7_45

Cited by 11 publications

(10 citation statements)

References 27 publications

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“…Semantic interoperability integration (Grangel-Gonz alez et al, 2016) Ontological approach (Mayer et al, 2017) OSF framework (Yahya et al, 2021) RGOM (Bader et al, 2020) I40KG (Nilssonet al, 2019) Translation model (Terziyan and Vitko, 2022) Explainable AI model (Zhou et al, 2021) KG combined with ML model (Zheng et al, 2022) KG combined with ML model Source: Created by the authors machine-understandable format. This lays the foundation for automated reasoning and decision-making based on knowledge models, allowing semantic applications to use both domain-specific and general knowledge models.…”

Section: Referencementioning

confidence: 99%

“…In a recent study based on the previous study, Zheng et al (2022) explain how executable knowledge graphs can be used for machine learning, using a Bosch welding monitoring case study as an example. The authors describe how they created a framework called executable knowledge graph that incorporated domain-specific knowledge and enabled automated reasoning for machine learning.…”

Section: Referencementioning

confidence: 99%

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Semantic web and machine learning techniques addressing semantic interoperability in Industry 4.0

Hafidi,

Djezzar,

Hemam

et al. 2023

IJWIS

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Purpose This paper aims to offer a comprehensive examination of the various solutions currently accessible for addressing the challenge of semantic interoperability in cyber physical systems (CPS). CPS is a new generation of systems composed of physical assets with computation capabilities, connected with software systems in a network, exchanging data collected from the physical asset, models (physics-based, data-driven, . . .) and services (reconfiguration, monitoring, . . .). The physical asset and its software system are connected, and they exchange data to be interpreted in a certain context. The heterogeneous nature of the collected data together with different types of models rise interoperability problems. Modeling the digital space of the CPS and integrating information models that support cyber physical interoperability together are required. Design/methodology/approach This paper aims to identify the most relevant points in the development of semantic models and machine learning solutions to the interoperability problem, and how these solutions are implemented in CPS. The research analyzes recent papers related to the topic of semantic interoperability in Industry 4.0 (I4.0) systems. Findings Semantic models are key enabler technologies that provide a common understanding of data, and they can be used to solve interoperability problems in Industry by using a common vocabulary when defining these models. Originality/value This paper provides an overview of the different available solutions to the semantic interoperability problem in CPS.

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Section: Referencementioning

confidence: 99%

Section: Referencementioning

confidence: 99%

Semantic web and machine learning techniques addressing semantic interoperability in Industry 4.0

Hafidi,

Djezzar,

Hemam

et al. 2023

IJWIS

View full text Add to dashboard Cite

show abstract

“…Challenge. However, there exist still challenges of ML practice in industry [2]. ML projects in modern industries often involve an interdisciplinary team of experts with distinct background.…”

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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“…Resistance Spot Welding (RSW) is an automated manufacturing process widely applied for joining metal materials in these industries [2]. Worldwide RSW accounts for the production of over million of cars every year [3]. In the RSW process, a high current flows through metal worksheets, generating a large amount of heat and melting down the metal materials to form welding spots.…”

Section: Introductionmentioning

confidence: 99%

Semantic Modeling, Development and Evaluation for the Resistance Spot Welding Industry

et al. 2023

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The ongoing industrial revolution termed Industry 4.0 (I4.0) has borne witness to a series of profound changes towards increasing smart automation, particularly in the industrial sectors of automotive, aerospace, manufacturing, etc. Automatic welding, a widely applied manufacturing process in these domains, is not an exception of these changes. One type of automatic welding, Resistance Spot Welding (RSW), lies in the center of this work. Large volumes and varieties of RSW data are being generated, thanks to the technologies behind I4.0. To address the associated data challenges, ontologies are essential in various aspects: integrating data sources, enhancing interoperability, and unifying knowledge etc. However, there have been limited studies around the semantic modelling of Resistance Spot Welding: Existing ontologies have overlooked some crucial concepts, such as an operation-centric view, welding software, and welding electrodes, which are essential for the monitoring of sensor measurements as well as the status of machine components (e.g., electrode wear). Additionally, current ontologies are not publicly available (to the best of our knowledge), and therefore cannot be accessed by other users. Such a lack of availability often requires that users build their own ontologies from scratch. In this paper, we propose our RSW ontology (RSWO) a to formalize knowledge in the RSW domain. It combines three sources of knowledge: extensive discussions with Bosch welding experts; reusing terminologies following ISO-14327 and ISO-14373 standards; and existing established ontologies. We have evaluated RSWO on real-world data from monitoring welding quality at Bosch in Germany, using Competency Questions, FAIR principles, OOPS!, and OntoMetrics.

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Executable Knowledge Graphs for Machine Learning: A Bosch Case of Welding Monitoring

Cited by 11 publications

References 27 publications

Semantic web and machine learning techniques addressing semantic interoperability in Industry 4.0

Semantic web and machine learning techniques addressing semantic interoperability in Industry 4.0

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

Semantic Modeling, Development and Evaluation for the Resistance Spot Welding Industry

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