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\(PO^2\) - A Process and Observation Ontology in Food Science. Application to Dairy Gels

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 672))

Abstract

This paper focuses on the knowledge representation task for an interdisciplinary project called Delicious concerning the production and transformation processes in food science. The originality of this project is to combine data from different disciplines like food composition, food structure, sensorial perception and nutrition. Available data sets are described using different vocabularies and are stored in different formats. Therefore there is a need to define an ontology, called \(PO^2\) (Process and Observation Ontology), as a common and standardized vocabulary for this project. The scenario 6 of the NeON methodology was used for building \(PO^2\) and the core component is implemented in OWL. By making use of \(PO^2\), data from the project were structured and an use case is presented here. \(PO^2\) aims to play a key role as the representation layer of the querying and simulation systems of Delicious project.

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Notes

  1. 1.

    https://www.w3.org/2001/sw/wiki/OWL.

  2. 2.

    http://cmap.ihmc.us.

  3. 3.

    http://www.w3.org/2005/Incubator/ssn/XGR-ssn-20110628.

  4. 4.

    https://bioportal.bioontology.org/ontologies/BFO.

  5. 5.

    https://bioportal.bioontology.org/ontologies/IAO.

  6. 6.

    http://lovinra.inra.fr/2015/12/16/multi-scale-multi-step-ontology/.

References

  1. Boisard, L., Andriot, I., Martin, C., Septier, C., Boissard, V., Salles, C., Guichard, E.: The salt and lipid composition of model cheeses modifies in-mouth flavour release and perception related to the free sodium ion content. Food Chem. 145, 437–444 (2014)

    Article  Google Scholar 

  2. Suárez-Figueroa, M.C., Gómez-Pérez, A., Fernández-López, M.: The NeOn methodology for ontology engineering. In: Suárez-Figueroa, M.C., Gómez-Pérez, A., Motta, E., Motta, A. (eds.) Ontology Engineering in a Networked World, pp. 9–34. Springer, Heidelberg (2012). doi:10.1007/978-3-642-24794-1_2

    Chapter  Google Scholar 

  3. Dibie, J., Dervaux, S., Doriot, E., Ibanescu, L., Pénicaud, C.: \([MS]^2O\) – a multi-scale and multi-step ontology for transformation processes: application to micro-organisms. In: Haemmerlé, O., Stapleton, G., Faron Zucker, C. (eds.) ICCS 2016. LNCS (LNAI), vol. 9717, pp. 163–176. Springer, Heidelberg (2016). doi:10.1007/978-3-319-40985-6_13

    Chapter  Google Scholar 

  4. Doan, A., Halevy, A.Y., Ives, Z.G.: Principles of Data Integration. Morgan Kaufmann (2012)

    Google Scholar 

  5. Feron, G., Ayed, C., Qannari, E.M., Courcoux, P., Laboure, H., Guichard, E.: Understanding aroma release from model cheeses by a statistical multiblock approach on oral processing. PLoS ONE 9(4), 1–15 (2014)

    Article  Google Scholar 

  6. Grubic, T., Fan, I.S.: Supply chain ontology: review, analysis and synthesis. Comput. Ind. 61(8), 776–786 (2010)

    Article  Google Scholar 

  7. Guarino, N., Oberle, D., Staab, S.: What is an ontology? In: Staab, S., Staab, R. (eds.) Handbook on Ontologies. International Handbooks on Information Systems, vol. 2009, pp. 1–17. Springer, Heidelberg (2009). doi:10.1007/978-3-540-92673-3_0

    Chapter  Google Scholar 

  8. Muljarto, A.-R., Salmon, J.-M., Neveu, P., Charnomordic, B., Buche, P.: Ontology-based model for food transformation processes - application to winemaking. In: Closs, S., Studer, R., Garoufallou, E., Sicilia, M.-A. (eds.) MTSR 2014. CCIS, vol. 478, pp. 329–343. Springer, Heidelberg (2014). doi:10.1007/978-3-319-13674-5_30

    Google Scholar 

  9. Rospocher, M., Ghidini, C., Serafini, L.: An ontology for the business process modelling notation. In: Garbacz, P., Kutz, O. (eds.) Formal Ontology in Information Systems - Proceedings of the Eighth International Conference, FOIS , 22–25 September 2014, Rio de Janeiro. Frontiers in Artificial Intelligence and Applications, vol. 267, pp. 133–146. IOS Press (2014)

    Google Scholar 

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Correspondence to Liliana Ibanescu .

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Ibanescu, L., Dibie, J., Dervaux, S., Guichard, E., Raad, J. (2016). \(PO^2\) - A Process and Observation Ontology in Food Science. Application to Dairy Gels. In: Garoufallou, E., Subirats Coll, I., Stellato, A., Greenberg, J. (eds) Metadata and Semantics Research. MTSR 2016. Communications in Computer and Information Science, vol 672. Springer, Cham. https://doi.org/10.1007/978-3-319-49157-8_13

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  • DOI: https://doi.org/10.1007/978-3-319-49157-8_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49156-1

  • Online ISBN: 978-3-319-49157-8

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