Publikation

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

Václav Jirkovský, Ondřej Šebek, Petr Kadera, Pavel Burget, Sönke Knoch, Tilman Becker

In: Maria Indrawan-Santiago, Eric Pardede, Ivan Luiz Salvadori, Matthias Steinbauer, Ismail Khalil, Gabriele Anderst-Kotsis (Hrsg.). Proceedings of the 21st International Conference on Information Integration and Web-Based Applications & Services. International Conference on Information Integration and Web-based Applications & Services (iiWAS-2019) December 2-4 Munich Germany Seiten 649-653 ISBN 9781450371797 Association for Computing Machinery New York, NY, USA 12/2019.

Abstrakt

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.

Projekte

Weitere Links

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence