scholarly journals Analyzing a Knowledge Graph of Industry 4.0 Standards

2021 ◽  
Author(s):  
Irlán Grangel-González ◽  
Maria Esther Vidal
Keyword(s):  
Industry 4.0 ◽  
Knowledge Graph

scholarly journals The roles of data management and analytics in industry 4.0 ecosystems

2020 ◽  
Author(s):  
Hendro Wicaksono
Keyword(s):  
Artificial Intelligence ◽  
Industry 4.0 ◽  
Linked Data ◽  
Data Analytics ◽  
Industrial Revolution ◽  
Data Driven ◽  
Knowledge Graph ◽  
Fourth Industrial Revolution ◽  
Global Access ◽  
Integration Data

The presentation introduces the technologies associated with the fourth industrial revolution which rely on the concept of artificial intelligence. Data is the basis of functioning artificial intelligence technologies. The presentation also explains how data can revolutionize the business by providing global access to physical products through an industry 4.0 ecosystem. The ecosystem contains four pillars: smart product, smart process, smart resources (smart PPR), and data-driven services. Through these four pillars, the industry 4.0 can be implemented in different sectors. The presentation also provides some insights on the roles of linked data (knowledge graph) for data integration, data analytics, and machine learning in industry 4.0 ecosystem. Project examples in smart city, healthcare, and agriculture sectors are also described. Finally, the presentation discusses the implications of the introduced concepts on the Indonesian context.

scholarly journals A Knowledge Graph for Industry 4.0

2020 ◽  
pp. 465-480
Author(s):  
Sebastian R. Bader ◽  
Irlan Grangel-Gonzalez ◽  
Priyanka Nanjappa ◽  
Maria-Esther Vidal ◽  
Maria Maleshkova
Keyword(s):  
Industry 4.0 ◽  
Knowledge Graph

scholarly journals Data-Driven Construction Safety Information Sharing System Based on Linked Data, Ontologies, and Knowledge Graph Technologies

2022 ◽  
Vol 19 (2) ◽  
pp. 794
Author(s):  
Akeem Pedro ◽  
Anh-Tuan Pham-Hang ◽  
Phong Thanh Nguyen ◽  
Hai Chien Pham
Keyword(s):  
Information Sharing ◽  
Industry 4.0 ◽  
Linked Data ◽  
Data Science ◽  
Safety Information ◽  
Construction Safety ◽  
Lessons Learned ◽  
Data Driven ◽  
Knowledge Graph ◽  
Ontological Approach

Accident, injury, and fatality rates remain disproportionately high in the construction industry. Information from past mishaps provides an opportunity to acquire insights, gather lessons learned, and systematically improve safety outcomes. Advances in data science and industry 4.0 present new unprecedented opportunities for the industry to leverage, share, and reuse safety information more efficiently. However, potential benefits of information sharing are missed due to accident data being inconsistently formatted, non-machine-readable, and inaccessible. Hence, learning opportunities and insights cannot be captured and disseminated to proactively prevent accidents. To address these issues, a novel information sharing system is proposed utilizing linked data, ontologies, and knowledge graph technologies. An ontological approach is developed to semantically model safety information and formalize knowledge pertaining to accident cases. A multi-algorithmic approach is developed for automatically processing and converting accident case data to a resource description framework (RDF), and the SPARQL protocol is deployed to enable query functionalities. Trials and test scenarios utilizing a dataset of 200 real accident cases confirm the effectiveness and efficiency of the system in improving information access, retrieval, and reusability. The proposed development facilitates a new “open” information sharing paradigm with major implications for industry 4.0 and data-driven applications in construction safety management.

Discover Relations in the Industry 4.0 Standards Via Unsupervised Learning on Knowledge Graph Embeddings

2021 ◽  
Vol 2 (3) ◽  
pp. 336-347
Author(s):  
Ariam Rivas ◽  
Irlan Grangel-Gonzalez ◽  
Diego Collarana ◽  
Jens Lehmann ◽  
Maria-esther Vidal
Keyword(s):  
Data Model ◽  
Industry 4.0 ◽  
Knowledge Graph ◽  
Similarity Metrics ◽  
Graph Embeddings ◽  
Graph Data ◽  
Detection Algorithms ◽  
Unsupervised Approach ◽  
Main Components ◽  
Smart Factories

Industry 4.0 (I4.0) standards and standardization frameworks provide a unified way to describe smart factories. Standards specify the main components, systems, and processes inside a smart factory and the interaction among all of them. Furthermore, standardization frameworks classify standards according to their functions into layers and dimensions. Albeit informative, frameworks can categorize similar standards differently. As a result, interoperability conflicts are generated whenever smart factories are described with miss-classified standards. Approaches like ontologies and knowledge graphs enable the integration of standards and frameworks in a structured way. They also encode the meaning of the standards, known relations among them, as well as their classification according to existing frameworks. This structured modeling of the I4.0 landscape using a graph data model provides the basis for graph-based analytical methods to uncover alignments among standards. This paper contributes to analyzing the relatedness among standards and frameworks; it presents an unsupervised approach for discovering links among standards. The proposed method resorts to knowledge graph embeddings to determine relatedness among standards-based on similarity metrics. The proposed method is agnostic to the technique followed to create the embeddings and to the similarity measure. Building on the similarity values, community detection algorithms can automatically create communities of highly similar standards. Our approach follows the homophily principle, and assumes that related standards are together in a community. Thus, alignments across standards are predicted and interoperability issues across them are solved. We empirically evaluate our approach on a knowledge graph of 249 I4.0 standards using the Trans$^*$ family of embedding models for knowledge graph entities. Our results are promising and suggest that relations among standards can be detected accurately.

scholarly journals Unveiling Relations in the Industry 4.0 Standards Landscape Based on Knowledge Graph Embeddings

2020 ◽  
pp. 179-194
Author(s):  
Ariam Rivas ◽  
Irlán Grangel-González ◽  
Diego Collarana ◽  
Jens Lehmann ◽  
Maria-Esther Vidal
Keyword(s):  
Industry 4.0 ◽  
Knowledge Graph ◽  
Graph Embeddings

Marktkopplung und Ablaufdeterminismus

2017 ◽  
Vol 47 (187) ◽  
pp. 213-228
Author(s):  
Gaus Jobst ◽  
Knop Christopher ◽  
Wandjo David
Keyword(s):  
Industry 4.0 ◽  
Ongoing Debate ◽  
Centralized Planning

Through the ongoing debate different positions support the hypothesis that Industry 4.0 evokes decentralization in everyday works. In this article we argue that the technological premises of Industry 4.0 lead to the contrary: centralized planning ensuing from optimized adaptation to the imperatives of the market. We exemplify this pattern, that we named ‘determinated procedure’, through exemplary cases from different industrial branches. Furthermore, we argue that (indeed) existing decentral moments neither amount to structural decentralization nor to humanizing and empowering concessions to employees, but rather primarily serve to their integration into the enterprise and mobilization of their production intelligence.

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