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Expertise is an activity carried out by experts that contributes to societal progress, as it helps to elucidate unknown situations. Forexample, accident expertise eases accident understanding, by describing how it happened, and by identifying its causes and conse-quences. As a result, the design of accident expertise in a convenient human-machine structure will enable the querying, reasoning,and reuse of accident knowledge in other tools, such as safety and decision-making systems. However, existing representations ofaccident knowledge, such as documents, relational databases, or accident ontologies, do not fulfill accident expertise expectations.Moreover, these representations are unlikely to provide the appropriate use of accident expertise knowledge. This study presents a base ontology for accident expertise knowledge representation designed with a model-driven methodologyand implemented with semantic web technologies. The study obtained satisfactory results from the evaluation and application ofextension and reuse of this ontology with aircraft accident expertise taken from the French bureau of Enquiries and Analysis (BEA)for civil aviation safety.

This work deals with the customer-supplier relationship and concerns offer definition in Engineer-To-Order situations (ETO), by adopting the supplier point of view. In such cases, when the offer definition relies simply on key design choices without a detailed design, there is a specific risk (ETO-specific risk) that customer expectations cannot be fulfilled. This kind of risk is in addition to the conventional risks (non ETO-specific risk) involved in any delivery process (machine break down, resource not available, scrapped part…). In order to minimize the supplier risk of not being able to complete the offer as accepted and contracted by the customer, a knowledge-based system can be used to assist risk engineering. Consequently, this article proposes two interrelated knowledge modeling contributions. Firstly, a risk knowledge model which, when implemented in a knowledge-based system that supports risk characterization and risk treatment by using knowledge re-use techniques, is proposed and discussed. Secondly, two knowledge typologies for risk characterizations and treatments (both for ETO and non-ETO situations) in order to support risk knowledge, identification and modeling are also proposed and discussed. These contributions are innovative and groundbreaking in terms of both academics and applications: they provide a formal model to structure risk knowledge and a first list of risks and treatments to be taken into account in ETO and non ETO situations. After an introduction that presents the research gap, our objectives and an analysis of related works, our two contributions are described in two sections with respect to ISO31000 recommendations. The first section covers risk identification and evaluation while the second deals with risk treatments.