Construction d’une ontologie par transformation de systèmes d’organisation des connaissances et évaluation de la confiance

Abstract

Cet article présente une méthode originale de création d'une base de connaissances (ontologie) à partir de transformations de plusieurs sources de connaissances. Ces sources sont issues des systèmes d'organisation des connaissances (thésaurus, taxonomies, etc.). Les sources sont pondérées par un score de confiance. La méthode s'appuie sur un module défini à l'aide de patrons de conception ontologiques. Nous avons mis en oeuvre cette méthode dans un contexte agronomique. Nous proposons dans cet article plusieurs fonctions de pondération que nous avons évaluées à partir de trois sources : Agrovoc, TaxRef et NCBI. ABSTRACT. This paper presents an original method of knowledge base (ontology) creation from transformations of several sources of knowledge. These sources are derived from the knowledge organization systems (thesauri, taxonomies, etc.). The sources are weighted by a confidence score. The method is based on a module aggregating ontological design patterns. We have implemented this method in an agronomical context. We propose in this paper several weighted functions that we have evaluated from three sources: Agrovoc, TaxRef and NCBI.

Dealing with Incompatibilities During a Knowledge Bases Fusion Process

“…The Discovery of the Optimal Extension allows the generation of an extension representing the maximal subset of compatible candidates validated by an expert (optimal extension). The first three activities have been already presented in [1,2]. In this article, we focus on the discovery of the optimal extension.…”

Knowledge Engineering Method Based on Consensual Knowledge and Trust Computation: The MUSCKA System

“…This paper is an extension of our previous work and presents measures to compute trust for each new type of ontological objects that are extracted by our method. More information about the extraction method are available in french language using a different formalisation [2,3]. These measures are evaluated on a real case study in agriculture and we believe they can be applied in any knowledge base merging process.…”