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Facilitation of Domain-Specific Data Models Design using Semantic Web Technologies for Manufacturing

Published: 22 February 2020 Publication History

Abstract

Modern manufacturing faces a challenge of integrating data models from various sources/domains which may differ both semantically and technically when particular domain specific data models are designed by different users and stored in different formats. This paper introduces an approach for facilitating the design of domain-specific data models using semantic web technologies. In this approach, all the information required for managing the production (including a description of a product, processes involved in the production, and existing resources and their specifications) is captured in an ontology. The proposed Product, Process, and Resource (PPR) ontology defines fundamental conceptualization of the production that can be easily applied to the arbitrary domain. Application of the PPR ontology is demonstrated in the case of simple truck assembling by means of robots. Capturing the knowledge in the form of ontology provides the advantage of employing supporting tools such as reasoners for consistency checking or query languages for information extraction. The paper demonstrates the utilization of SQWRL for searching resources suitable to manipulate given truck parts on the basis of semantic matching between properties of particular elements.

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Cited By

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  • (2024)Evolving to multi-modal knowledge graphs for engineering design: state-of-the-art and future challengesJournal of Engineering Design10.1080/09544828.2023.2301230(1-40)Online publication date: 6-Jan-2024
  • (2023)An AI Factory Digital Twin Deployed Within a High Performance Edge Architecture2023 IEEE 31st International Conference on Network Protocols (ICNP)10.1109/ICNP59255.2023.10355613(1-6)Online publication date: 10-Oct-2023
  • (2021)Revival of MAS Technologies in IndustryTransactions on Large-Scale Data- and Knowledge-Centered Systems XLVIII10.1007/978-3-662-63519-3_6(131-144)Online publication date: 18-May-2021
  • Show More Cited By

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  1. Facilitation of Domain-Specific Data Models Design using Semantic Web Technologies for Manufacturing

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      cover image ACM Other conferences
      iiWAS2019: Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services
      December 2019
      709 pages
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      • JKU: Johannes Kepler Universität Linz
      • @WAS: International Organization of Information Integration and Web-based Applications and Services

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 22 February 2020

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      Author Tags

      1. Data Model Design
      2. Industrial Automation
      3. Ontology
      4. Semantic Matchmaking

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      View all
      • (2024)Evolving to multi-modal knowledge graphs for engineering design: state-of-the-art and future challengesJournal of Engineering Design10.1080/09544828.2023.2301230(1-40)Online publication date: 6-Jan-2024
      • (2023)An AI Factory Digital Twin Deployed Within a High Performance Edge Architecture2023 IEEE 31st International Conference on Network Protocols (ICNP)10.1109/ICNP59255.2023.10355613(1-6)Online publication date: 10-Oct-2023
      • (2021)Revival of MAS Technologies in IndustryTransactions on Large-Scale Data- and Knowledge-Centered Systems XLVIII10.1007/978-3-662-63519-3_6(131-144)Online publication date: 18-May-2021
      • (2020)Requirements for Information Modelling in ManufacturingTowards Digital Intelligence Society10.1007/978-3-030-63872-6_7(147-159)Online publication date: 22-Dec-2020

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