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Forage des données et formalisation des connaissances sur un accident : Le cas Deepwater Horizon

Abstract : Data drilling, the method and means developed in this thesis, redefines the process of data extraction, the formalization of knowledge and its enrichment, particularly in the context of the elucidation of events that have not or only slightly been documented. The Deepwater Horizon disaster, the drilling platform operated for BP in the Gulf of Mexico that suffered a blowout on April 20, 2010, will be our case study for the implementation of our proof of concept for data drilling. This accident is the result of an unprecedented discrepancy between the state of the art of drilling engineers' heuristics and that of pollution response engineers. The loss of control of the MC 252-1 well is therefore an engineering failure and it will take the response party eighty-seven days to regain control of the wild well and halt the pollution. Deepwater Horizon is in this sense a case of engineering facing extreme situation, as defined by Guarnieri and Travadel.First, we propose to return to the overall concept of accident by means of an in-depth linguistic analysis presenting the semantic spaces in which the accident takes place. This makes it possible to enrich its "core meaning" and broaden the shared acceptance of its definition.Then, we bring that the literature review must be systematically supported by algorithmic assistance to process the data taking into account the available volume, the heterogeneity of the sources and the requirements of quality and relevance standards. In fact, more than eight hundred scientific articles mentioning this accident have been published to date and some twenty investigation reports, constituting our research material, have been produced. Our method demonstrates the limitations of accident models when dealing with a case like Deepwater Horizon and the urgent need to look for an appropriate way to formalize knowledge.As a result, the use of upper-level ontologies should be encouraged. The DOLCE ontology has shown its great interest in formalizing knowledge about this accident and especially in elucidating very accurately a decision-making process at a critical moment of the intervention. The population, the creation of instances, is the heart of the exploitation of ontology and its main interest, but the process is still largely manual and not without mistakes. This thesis proposes a partial answer to this problem by an original NER algorithm for the automatic population of an ontology.Finally, the study of accidents involves determining the causes and examining "socially constructed facts". This thesis presents the original plans of a "semantic pipeline" built with a series of algorithms that extract the expressed causality in a document and produce a graph that represents the "causal path" underlying the document. It is significant for scientific or industrial research to highlight the reasoning behind the findings of the investigation team. To do this, this work leverages developments in Machine Learning and Question Answering and especially the Natural Language Processing tools.As a conclusion, this thesis is a work of a fitter, an architect, which offers both a prime insight into the Deepwater Horizon case and proposes the data drilling, an original method and means to address an event, in order to uncover answers from the research material for questions that had previously escaped understanding.
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Submitted on : Tuesday, July 23, 2019 - 3:33:19 PM
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Thibaut Eude. Forage des données et formalisation des connaissances sur un accident : Le cas Deepwater Horizon. Risques. Université Paris sciences et lettres, 2018. Français. ⟨NNT : 2018PSLEM079⟩. ⟨tel-02191668⟩



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