Methodes et outils d'acquisition des connaissances

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Comparison of Conceptual Graphs for Modelling Knowledge of Multiple Experts : Application to Traffic Accident Analysis

When modelling knowledge of multiple experts, it is interesting to build a common expertise model corresponding to the kernel of knowledge common to the experts. Therefore the expertise conflicts between the expertise models of the different experts must be tackled. The domain level of an expertise model can be described through concepts linked by relations, and represented through Sowa's conceptual graph formalism. This report presents a method for conflict management during knowledge modelling from multiple experts: this method relies on the comparison and integration of several conceptual graphs corresponding to different viewpoints, the integration being guided by different integration strategies. For the comparison of the conceptual graphs, we define and exploit possible relations that can link such graphs. The appendix presents a base of conceptual graphs obtained by modelling the knowledge of several experts in traffic accident analysis

Cokace: A Centaur-based environment for CommonKADS Conceptual Modelling Language

International audienceIn order to help the knowledge engineer during knowledge modelling phase, we developed an environment for the conceptual modelling language CML offered by the CommonKADS methodology. This environment, called Cokace, was designed using Centaur, a programming environment generator, that was usually exploited for building environments dedicated to software engineering languages. Thanks to Centaur, Cokace provides the knowledge engineer with structured edition, static validation and dynamic interpretation of CML expertise models. Cokace allows the knowledge engineer to simulate a reasoning on CML expertise models, and enables verification and evaluation of such expertise models before implementation of the final knowledge-based system. This work illustrates an example of the benefits knowledge engineering can get from well established techniques and tools available in software engineering

Proximité Conceptuelle et Distances de Graphes

National audienceDans cet article nous donnons un aperçu des applications et des travaux que nous menons autour de la notion de proximité ou distance sémantique

Annotations sémantiques pour le domaine Biopuces

Après avoir souligné l'intérêt du Web sémantique dans le domaine biomédical et de l'apport des annotations sémantiques dans la recherche d'information nous présentons une méthode pour la génération semi-automatique des annotations sémantiques décrivant des articles dans le domaine des biopuces et ce en se basant sur les techniques d'extraction d'information

Accidentology: An Example of Problem Solving by Multiple Agents with Multiple Representations

International audienc

A Conceptual Graph Model for W3C Resource Description Framework

International audienceWith the aim of building a "Semantic Web" , the content of the documents must be explicitly represented through metadata in order to enable contents-guided search. Our approach is to exploit a standard language (RDF, recommended by W3C) for expressing such metadata and to interpret these metadata in conceptual graphs (CG) in order to exploit querying and inferencing capabilities enabled by CG formalism. The paper presents our mapping of RDF into CG and its interest in the context of the semantic Web

CoMMA : Une approche distribuée de la mémoire organisationnelle

National audienceLa distribution, au sens large, d’une mémoire organisationnelle est une caractéristique fondamentalement importante pour le développement et le déploiement de systèmes de gestion des connaissances. Cependant nous montrons dans cet article qu'un certain nombre de technologies émergentes peuvent actuellement faciliter la réalisation d'une solution complète etintégrée à l'organisation

Mining Biomedical Texts to Generate Semantic Annotations

This report focuses on text mining in the biomedical domain for the generation of semantic annotations based on a formal model which is ontology. We start by exposing the generic methodology for the generation of annotations from texts. Then, we present a state of the art on different knowledge extraction techniques used on biomedical texts. We propose our approach based on Semantic Web Technologies and Natural Language Processing (NLP): it relies on formal ontologies to generate semantic annotations on scientific articles and on other knowledge sources (databases, experiment sheets). This approach can be extended to other do-mains requiring experiments and massive data analyses. Finally, we conclude with a discussion about our work and we present some learnt lessons