Abstract
This paper proposes the semantic solution for data exchange in dynamically changing supply networks. Because of the dynamic nature of supply networks, there is a necessity to manage their efficiency. The first step in this direction is proper data exchange, which leads to transparency of networks and speeds up interactions of their participants. Despite plenty of data standards, there is a lack of approaches to data modeling allowed the seamless knowledge sharing within supply networks. This paper introduces a new ontology-based data metamodel of supply networks based on organizational ontology, consistent theory for data modeling, and the ontologized standard in logistics domain. At the first part of the paper, selected approaches to data modeling are explained and justified. Then, basic information patterns applicable for modeling of supply networks are explained. Finally, we elaborate how the proposed ontological framework facilitates knowledge integration in logistics.
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Notes
- 1.
OASIS: https://www.oasis-open.org/.
- 2.
UN/CEFACT: http://www.unece.org/CEFACT/.
- 3.
GS1: http://www.gs1.org/.
- 4.
ISO 15926: http://15926.org/.
- 5.
IDEAS: http://ideasgroup.org/.
- 6.
- 7.
OMG FIBO: http://www.omg.org/.
- 8.
Taken from GS1 Logistics Interoperability Model (GS1 LIM), Version 1, Issue 1.0 (2007).
- 9.
Jena Ontology API: http://jena.apache.org/documentation/ontology/.
- 10.
EURIDICE project: http://www.euridice-project.eu.
- 11.
i-Cargo project: http://i-cargo.eu.
- 12.
E-Freight project: http://www.efreightproject.eu.
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Acknowledgements
This research is partially supported by LATNA Laboratory, NRU HSE, RF government grant 11.G34.31.0057, and by the CLASSE (“Les Corridors Logistiques: Application a la Vallee de la Seine et son Environnement”) project of the Grand Research Network of in Upper Normandy (Grand Réseaux de Recherche de Haute-Normandie).
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Andreeva, E., Poletaeva, T., Abdulrab, H., Babkin, E. (2016). A Semantic Solution for Seamless Data Exchange in Supply Networks. In: Kalyagin, V., Koldanov, P., Pardalos, P. (eds) Models, Algorithms and Technologies for Network Analysis. NET 2014. Springer Proceedings in Mathematics & Statistics, vol 156. Springer, Cham. https://doi.org/10.1007/978-3-319-29608-1_17
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