Abstract
All manufacturing companies need to be able to closely monitor the processes, labor, tooling, parts and throughput on the assembly plant floor. This might be a challenging task because of a large number of plant floor applications that operate using different hardware and software tools. In many cases, there are a large number of devices that need to be monitored and from which critical data must be extracted and analyzed. This situation calls for the use of an architecture that can support data from heterogeneous sources and support the analysis of data and communication with these devices. Ontologies can be developed to facilitate a proper understanding of the problem domain, and subsequently, knowledge from external sources can be shared through linked open data or directly integrated (mapped) using an ontology matching approach. In this paper, we demonstrate how ontological data description may facilitate interoperability between a company data model and new data sources as well as an update of stored data via ontology matching. The MAPSOM system (system for semi-automatic ontology matching) is introduced and described in this paper, and subsequently, an example of new data model integration is demonstrated using the MAPSOM system.
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Notes
- 1.
Web Ontology Language - https://www.w3.org/OWL.
- 2.
Knowledge Interchange Format - http://www-ksl.stanford.edu/knowledge-sharing/kif.
- 3.
Microsoft Excel https://products.office.com/en-us/excel.
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Acknowledgment
This work is supported through the Ford Motor Company University Research Proposal (URP) program and by institutional resources for research by the Czech Technical University in Prague, Czech Republic.
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Jirkovský, V., Kadera, P., Rychtyckyj, N. (2017). Semi-automatic Ontology Matching Approach for Integration of Various Data Models in Automotive. In: Mařík, V., Wahlster, W., Strasser, T., Kadera, P. (eds) Industrial Applications of Holonic and Multi-Agent Systems. HoloMAS 2017. Lecture Notes in Computer Science(), vol 10444. Springer, Cham. https://doi.org/10.1007/978-3-319-64635-0_5
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