Enhancing Knowledge Graph Generation with Ontology Reshaping – Bosch Case

Zhou, Dongzhuoran; Zhou, Baifan; Zheng, Zhuoxun; Kostylev, Egor V.; Cheng, Gong; Jiménez-Ruiz, Ernesto; Soylu, Ahmet; Kharlamov, Evgeny

doi:10.1007/978-3-031-11609-4_45

Cited by 9 publications

(6 citation statements)

References 21 publications

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“…A knowledge graph can be regarded as a knowledge database based on an instantiation of the relevant ontology, showcasing entities and their relationships in a graphical form [39,40]. In this section, entity extraction methods relevant to fire evacuation are introduced.…”

Section: Construction Of Knowledge Graph For Evacuationmentioning

confidence: 99%

A Semantic Approach to Dynamic Path Planning for Fire Evacuation through BIM and IoT Data Integration

Pang,

Shi,

Jiang

et al. 2024

Advances in Civil Engineering

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Fire evacuation path planning involves multiple data sources. In order to develop a dynamic planning, a comprehensive knowledge of the environment involving building information and fire development is required. This article presents a semantic approach that integrates Building Information Modeling (BIM) and Internet of Things (IoT) information to provide a data foundation for dynamic path planning. First, a fire evacuation (FE) ontology is introduced to fuse both knowledge and information relevant to dynamic path planning. Next, a dynamic knowledge graph that evolves according to the development of fire situation is instantiated based on the relevant FE ontology. Finally, to validate the feasibility of the semantic approach based on the ontology and knowledge graph, an example of application is conducted using a specific building as an example. This study provides a data foundation for more intelligent and precise decision-making in fire evacuation scenarios and offers a new approach for safety design and management in the field of construction.

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Section: Construction Of Knowledge Graph For Evacuationmentioning

confidence: 99%

A Semantic Approach to Dynamic Path Planning for Fire Evacuation through BIM and IoT Data Integration

Pang,

Shi,

Jiang

et al. 2024

Advances in Civil Engineering

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“…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 . In contrast, ML-based methods will help reduce the waste and contribute to more economical and sustainable manufacturing industry [7].…”

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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“…Industrial analytics heavily relies on integration of heterogeneous production data. Knowledge Graphs (KGs) as the data format for integration and ontologies as the unified data schemata are a prominent solution that offers not only a high quality data integration [4] but also a convenient and standardised way to exchange data and to layer analytical applications over it [5,6].…”

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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Enhancing Knowledge Graph Generation with Ontology Reshaping – Bosch Case

Cited by 9 publications

References 21 publications

A Semantic Approach to Dynamic Path Planning for Fire Evacuation through BIM and IoT Data Integration

A Semantic Approach to Dynamic Path Planning for Fire Evacuation through BIM and IoT Data Integration

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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