RDF Knowledge Graphs Designed with Axiomatic Methodology to Enhance Industry 4.0
Industry 4.0 has introduced a data-driven model of production and management of goods and services. This manufacturing paradigm leverages the potential of the Internet of Things (IoT), but finding the information necessary to drive manufacturing processes can be challenging. In this context, the aut...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/13/1/58 |
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| author | Fernando Rolli Chiara Parretti Riccardo Barbieri Alessandro Polidoro Bianca Bindi |
| author_facet | Fernando Rolli Chiara Parretti Riccardo Barbieri Alessandro Polidoro Bianca Bindi |
| author_sort | Fernando Rolli |
| collection | DOAJ |
| description | Industry 4.0 has introduced a data-driven model of production and management of goods and services. This manufacturing paradigm leverages the potential of the Internet of Things (IoT), but finding the information necessary to drive manufacturing processes can be challenging. In this context, the authors propose an innovative approach based on axiomatic design to design RDF knowledge graphs from which to extract the information needed by decision makers. This approach derives from the possibility of providing RDF knowledge graphs with an equivalent matrix representation based on axiomatic design. It allows the selection of the most reliable data sources, thereby optimizing the knowledge graph construction process using matrix algebra, minimizing redundancy and improving the efficiency of query response. The goal of the presented methodology is to address the five critical aspects of Big Data (volume, velocity, variety, value, and veracity) by preordering the knowledge graph according to the information needs of business decision makers, thereby optimizing the use of the immense wealth of information made available by the Web in design. |
| format | Article |
| id | doaj-art-83d215e19df840e58ffda16b463ef8bc |
| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-83d215e19df840e58ffda16b463ef8bc2025-01-24T13:39:18ZengMDPI AGMachines2075-17022025-01-011315810.3390/machines13010058RDF Knowledge Graphs Designed with Axiomatic Methodology to Enhance Industry 4.0Fernando Rolli0Chiara Parretti1Riccardo Barbieri2Alessandro Polidoro3Bianca Bindi4Department of Engineering Science, Guglielmo Marconi University, Via Plinio 44, 00193 Rome, ItalyDepartment of Engineering Science, Guglielmo Marconi University, Via Plinio 44, 00193 Rome, ItalyDipartimento Ingegneria Industriale, Università Degli Studi di Firenze, Via di Santa Marta 3, 50139 Firenze, ItalyDepartment of Engineering Science, Guglielmo Marconi University, Via Plinio 44, 00193 Rome, ItalyDepartment of Engineering Science, Guglielmo Marconi University, Via Plinio 44, 00193 Rome, ItalyIndustry 4.0 has introduced a data-driven model of production and management of goods and services. This manufacturing paradigm leverages the potential of the Internet of Things (IoT), but finding the information necessary to drive manufacturing processes can be challenging. In this context, the authors propose an innovative approach based on axiomatic design to design RDF knowledge graphs from which to extract the information needed by decision makers. This approach derives from the possibility of providing RDF knowledge graphs with an equivalent matrix representation based on axiomatic design. It allows the selection of the most reliable data sources, thereby optimizing the knowledge graph construction process using matrix algebra, minimizing redundancy and improving the efficiency of query response. The goal of the presented methodology is to address the five critical aspects of Big Data (volume, velocity, variety, value, and veracity) by preordering the knowledge graph according to the information needs of business decision makers, thereby optimizing the use of the immense wealth of information made available by the Web in design.https://www.mdpi.com/2075-1702/13/1/58axiomatic designinformation managementIndustry 4.0information reliability in knowledge management |
| spellingShingle | Fernando Rolli Chiara Parretti Riccardo Barbieri Alessandro Polidoro Bianca Bindi RDF Knowledge Graphs Designed with Axiomatic Methodology to Enhance Industry 4.0 Machines axiomatic design information management Industry 4.0 information reliability in knowledge management |
| title | RDF Knowledge Graphs Designed with Axiomatic Methodology to Enhance Industry 4.0 |
| title_full | RDF Knowledge Graphs Designed with Axiomatic Methodology to Enhance Industry 4.0 |
| title_fullStr | RDF Knowledge Graphs Designed with Axiomatic Methodology to Enhance Industry 4.0 |
| title_full_unstemmed | RDF Knowledge Graphs Designed with Axiomatic Methodology to Enhance Industry 4.0 |
| title_short | RDF Knowledge Graphs Designed with Axiomatic Methodology to Enhance Industry 4.0 |
| title_sort | rdf knowledge graphs designed with axiomatic methodology to enhance industry 4 0 |
| topic | axiomatic design information management Industry 4.0 information reliability in knowledge management |
| url | https://www.mdpi.com/2075-1702/13/1/58 |
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