Ontology Reshaping for Knowledge Graph Construction: Applied on Bosch Welding Case

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

doi:10.1007/978-3-031-19433-7_44

Cited by 8 publications

(9 citation statements)

References 45 publications

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“…Based on the ontologies, users can generated executable KGs semi-automatically through a GUI-based software system by adding information in the instance level into the ontologies [6]. In particular, the system constructs a KG that represents an executable data analytical pipeline with a series of tasks.…”

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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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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“…And common Queries are selected by the users. The KGs in the KG Data Layer then can be used for applications like Query-Based Analytics [22] and ML Analytics [19,21,23,26] in the Application Layer.…”

Section: Schere: Schemata Reshaping System 21 Architectural Overviewmentioning

confidence: 99%

“…Edges that connect entities and their data values, represent relations between entities and data properties of entities. In context of Industry 4.0 [10] and Internet of Things [8], KGs are gaining popularity in a wide range of industries [14,16,17,26] and applications such as data integration [20,25,27], question answering [18], production monitoring in automatic manufacturing, pose estimation in robotics [1,13] and semantic search [5,24]. Due to complexity and variety of industrial data (e.g., relational tables), it is very desired to facilitate automation of KG construction.…”

Section: Introductionmentioning

confidence: 99%

“…Our ScheRe System. To this end, we propose our ScheRe system that enhances KG construction with a schema reshaping algorithm to "reshape" a given domain KG schema to data-oriented KG schema [29,30], which is free of blank nodes and still partially incorporates knowledge in domain schema. ScheRe has four layers that organise raw data, schema reshaping algorithm, and KG construction module for enhanced KG construction that generates KGs free of blank nodes.…”

Section: Introductionmentioning

confidence: 99%

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ScheRe: Schema Reshaping for Enhancing Knowledge Graph Construction

Zhou

Zheng

et al. 2022

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

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Automatic knowledge graph (KG) construction is widely used for e.g. data integration, question answering and semantic search. There are many approaches of automatic KG construction. Among which, an important approach is to map the raw data to a given domain KG schema, e.g., domain ontology or conceptual graph, and construct the entities and properties according to the domain KG schema. However, the existing approaches to construct KGs are not always efficient enough and the resulting KGs are not sufficiently user-friendly. The main challenge arises from the trade-off: the domain KG schema should be knowledge-oriented, to reflect the general domain knowledge; while a KG schema should be dataoriented, to cover all data features. If the former is directly used for KG construction, this can cause issues like a high load of blank nodes, which are technical nodes in the KGs that represent unknown entities. To this end, we propose our ScheRe system in the demo, which relies on a schema reshaping algorithm and other two semantic modules for enhancing KG construction. The demo attendees will use ScheRe to reshape a domain KG schema to data specific KG schema, build KGs with industrial data, and experience more user-friendly querying. CCS CONCEPTS• Information systems → Database utilities and tools.

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“…Ontologies play a fundamental role in many fields such as natural language understanding, semantic information extraction, and intelligent information integration to provide a formal representation of interested knowledge [1][2][3][4][5][6]. In particular, with the rapid development of knowledge graphs [7], ontologies became more essential for integrating, querying, and maintaining knowledge graphs [8][9][10]. High-quality ontologies are particularly critical.…”

Section: Introductionmentioning

confidence: 99%

An Embedding-Based Approach to Repairing OWL Ontologies

Yang

et al. 2022

Applied Sciences

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High-quality ontologies are critical to ontology-based applications, such as natural language understanding and information extraction, but logical conflicts naturally occur in the lifecycle of ontology development. To deal with such conflicts, conflict detection and ontology repair become two critical tasks, and we focus on repairing ontologies. Most existing approaches for ontology repair rely on the syntax of axioms or logical consequences but ignore the semantics of axioms. In this paper, we propose an embedding-based approach by considering sentence embeddings of axioms, which translates axioms into semantic vectors and provides facilities to compute semantic similarities among axioms. A threshold-based algorithm and a signature-based algorithm are designed to repair ontologies with the help of detected conflicts and axiom embeddings. In the experiments, our proposed algorithms are compared with existing ones over 20 real-life incoherent ontologies. The threshold-based algorithm with different distance metrics is further evaluated with 10 distinct thresholds and 3 pre-trained models. The experimental results show that the embedding-based algorithms could achieve promising performances.

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Ontology Reshaping for Knowledge Graph Construction: Applied on Bosch Welding Case

Cited by 8 publications

References 45 publications

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

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

ScheRe: Schema Reshaping for Enhancing Knowledge Graph Construction

An Embedding-Based Approach to Repairing OWL Ontologies

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