The Data Value Quest: A Holistic Semantic Approach at Bosch

Zhou, Baifan; Zheng, Zhuoxun; Zhou, Dongzhuoran; Cheng, Gong; Jiménez-Ruiz, Ernesto; Tran, Trung-Kien; Stepanova, Daria; Gad-Elrab, Mohamed H.; Nikolov, Nikolay; Soylu, Ahmet; Kharlamov, Evgeny

doi:10.1007/978-3-031-11609-4_42

Cited by 11 publications

(5 citation statements)

References 21 publications

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“…We have preliminary results that show the benefit of our approach for industrial analytics. Our work falls into the big picture of KG-based industrial applications [18]. As future work we will investigate transforming queries across different KG schemata, and study more query properties.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…ML projects in modern industries often involve an interdisciplinary team of experts with distinct background. The transparency of ML (C1) to non-ML experts is usually challenging, since the latter didn't receive excessive training of ML that is often required to understand the sophisticated ML methods and interpret the ML results [5]. The non-ML experts need to understand ML and trust that ML applied in manufacturing robots operating with high electricity can ensure product quality and personnel safety.…”

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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“…Take the cutting force for example, additional sensor systems like rotating measuring platforms and other advanced sensory solutions exist mainly in demonstration machines and laboratory environments, while in industrial environments, the current-based force model is widely adopted instead of installing external sensors or expensive advanced sensory systems. Since the data has direct influences on the model findings, processing procedures are required to contextualize and link such various data types, while considering the multi-fidelity nature of industrial data that differs in amounts, formats, features, and coverage of manufacturing scenarios arising from separate environments [5,22,23].…”

Section: Introductionmentioning

confidence: 99%