Formalising contextual expert knowledge for causal discovery in linked knowledge graphs about transformation processes: application to processing of bio-composites for food packaging - Archive ouverte HAL Access content directly
Journal Articles International Journal of Metadata, Semantics and Ontologies Year : 2022

Formalising contextual expert knowledge for causal discovery in linked knowledge graphs about transformation processes: application to processing of bio-composites for food packaging

Abstract

With numerous parameters and criteria to take into account, transformation processes are a challenge to model and reason about. This work can be eased thanks to knowledge graphs, which are a widespread practice for formalising knowledge associated with structured and specialised vocabulary about a given domain. They allow to draw semantic relations between concepts, and thus offer numerous tools for reasoning over complex queries. Yet, some of these queries in transformation processes might rely on an additional layer hard to transcribe: uncertainty. In this article, we present how knowledge graphs and probabilistic models can benefit each other for reasoning over transformation processes and address the necessity of formalising contextual expert knowledge for this combination. We then show how this can be used for (1) reverse engineering approaches and (2) linking knowledge bases, through a detailed example on the process of bio-composites for food packaging.
Fichier principal
Vignette du fichier
IJMSO___MTSR_extended_version.pdf (986.88 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-04115029 , version 1 (02-06-2023)

Identifiers

Cite

Mélanie Munch, Patrice Buche, Helene Angellier-Coussy, Cristina Manfredotti, Pierre-Henri Wuillemin. Formalising contextual expert knowledge for causal discovery in linked knowledge graphs about transformation processes: application to processing of bio-composites for food packaging. International Journal of Metadata, Semantics and Ontologies, 2022, 16 (1), pp.1-15. ⟨10.1504/IJMSO.2022.131129⟩. ⟨hal-04115029⟩
98 View
43 Download

Altmetric

Share

Gmail Facebook X LinkedIn More