Query-Based Industrial Analytics over Knowledge Graphs with Ontology Reshaping

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

et al. 2022

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.

Section: Talk Descriptionmentioning

confidence: 99%

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

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

et al. 2022

“…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%

ScheRe: Schema Reshaping for Enhancing Knowledge Graph Construction

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

et al. 2022

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.

“…Our system offers KG construction that represent executable data pipelines via GUI-based system for creation, modification, integration and visualisation of KGs. We name our system executable KGs (ExeKG), because our KGs can be translated to modularised and executable ML scripts that can be modified and reused for new data and new questions, besides, these KGs can also be used in other industrial applications such as pipeline verification and selection based query answering [17]. In particular, we focus on three important activities of data analytics practice: (1) visual analytics using various plotting methods to visualise data for intuitive data understanding; (2) statistical analytics with statistical methods to extract insights from data; (3) ML analytics relying on classic ML methods as well as neural networks for classification or regression.…”

Section: Introductionmentioning

confidence: 99%

ExeKG: Executable Knowledge Graph System for User-friendly Data Analytics

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

et al. 2022

Data analytics including machine learning (ML) is essential to extract insights from production data in modern industries. However, industrial ML is affected by: the low transparency of ML towards non-ML experts; poor and non-unified descriptions of ML practices for reviewing or comprehension; ad-hoc fashion of ML solutions tailored to specific applications, which affects their re-usability. To address these challenges, we propose the concept and a system of executable knowledge graph (KG), which represent KGs that rely on semantic technologies to formally encode ML knowledge and solutions. These KGs can be translated to executable scripts in a reusable and modularised fashion. CCS CONCEPTS• Information systems → Information systems applications.