Description logics with approximate definitions precise modeling of vague concepts

We extend traditional Description Logics (DL) with a simple mechanism to handle approximate concept definitions in a qualitative way. Often, for example in medical applications, concepts are not definable in a crisp way but can fairly exhaustively be constrained through a particular sub- and a particular super-concept. We introduce such lower and upper approximations based on rough-set semantics, and show that reasoning in these languages can be reduced to standard DL satisfiability. This allows us to apply Rough Description Logics in a study of medical trials about sepsis patients, which is a typical application for precise modeling of vague knowledge. The study shows that Rough DL-based reasoning can be done in a realistic use case and that modeling vague knowledge helps to answer important questions in the design of clinical trials

L.: Description logics with approximate definitions: Precise modeling of vague concepts

We extend traditional Description Logics (DL) with a simple mechanism to handle approximate concept definitions in a qualitative way. Often, for example in medical applications, concepts are not definable in a crisp way but can fairly exhaustively be constrained through a particular sub- and a particular super-concept. We introduce such lower and upper approximations based on rough-set semantics, and show that reasoning in these languages can be reduced to standard DL satisfiability. This allows us to apply Rough Description Logics in a study of medical trials about sepsis patients, which is a typical application for precise modeling of vague knowledge. The study shows that Rough DL-based reasoning can be done in a realistic use case and that modeling vague knowledge helps to answer important questions in the design of clinical trials.

Semantic Decision Support for Information Fusion Applications

La thèse s'inscrit dans le domaine de la représentation des connaissances et la modélisation de l'incertitude dans un contexte de fusion d'informations. L'idée majeure est d'utiliser les outils sémantiques que sont les ontologies, non seulement pour représenter les connaissances générales du domaine et les observations, mais aussi pour représenter les incertitudes que les sources introduisent dans leurs observations. Nous proposons de représenter ces incertitudes au travers d'une méta-ontologie (DS-ontology) fondée sur la théorie des fonctions de croyance. La contribution de ce travail porte sur la définition d'opérateurs d'inclusion et d'intersection sémantique et sur lesquels s'appuie la mise en œuvre de la théorie des fonctions de croyance, et sur le développement d'un outil appelé FusionLab permettant la fusion d'informations sémantiques à partir du développement théorique précédent. Une application de ces travaux a été réalisée dans le cadre d'un projet de surveillance maritime.This thesis is part of the knowledge representation domain and modeling of uncertainty in a context of information fusion. The main idea is to use semantic tools and more specifically ontologies, not only to represent the general domain knowledge and observations, but also to represent the uncertainty that sources may introduce in their own observations. We propose to represent these uncertainties and semantic imprecision trough a metaontology (called DS-Ontology) based on the theory of belief functions. The contribution of this work focuses first on the definition of semantic inclusion and intersection operators for ontologies and on which relies the implementation of the theory of belief functions, and secondly on the development of a tool called FusionLab for merging semantic information within ontologies from the previous theorical development. These works have been applied within a European maritime surveillance project.ROUEN-INSA Madrillet (765752301) / SudocSudocFranceF