Abstract
Designing new processes for bio-based and biodegradable food packaging is an environmental and economic challenge. Due to the multiplicity of the parameters, such an issue requires an approach that proposes both (1) to integrate heterogeneous data sources and (2) to allow causal reasoning. In this article, we present POND (Process and observation ONtology Discovery), a workflow dedicated to answering expert queries on domains modeled by the Process and Observation Ontology (PO\(^2\)). The presentation is illustrated with a real-world application on bio-composites for food packaging to solve a reverse engineering problem, using a novel dataset composed of data from different projects.
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Notes
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We use the classical statistical method Greedy Hill Climbing with a BIC Score.
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For the rest of the article, all attributes represented in the model are bolded.
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Acknowledgement
We would like to thank Claire Mayer (PhyProDiv Team, INRAE IATE) who provided data for the biomass discovery aspect. Our work has been partially financed by the French national research agency ANR in the framework of D2KAB (ANR-18-CE23-0017) and DataSusFood (ANR-19-DATA-0016) projects.
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Münch, M., Buche, P., Manfredotti, C., Wuillemin, PH., Angellier-Coussy, H. (2022). A Process Reverse Engineering Approach Using Process and Observation Ontology and Probabilistic Relational Models: Application to Processing of Bio-composites for Food Packaging. In: Garoufallou, E., Ovalle-Perandones, MA., Vlachidis, A. (eds) Metadata and Semantic Research. MTSR 2021. Communications in Computer and Information Science, vol 1537. Springer, Cham. https://doi.org/10.1007/978-3-030-98876-0_1
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