Catherine Comparot

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This paper deals with an approach for an automated evaluation of the learners’ state of knowledge when learning by doing. This approach is called ODALA for “Ontology-Driven Auto-evaluation for e-Learning Approach”. It takes place in the context of Computer Based Human Learning Environment (CBHLE) in a self-learning by doing mode. ODALA is based on the(More)
We present in this paper ODALA approach (Ontology-Driven Auto-evaluation for e-Learning Approach) for an automated evaluation of the learners state of knowledge. The context considered is Computer Based Human Learning Environnement (CBHLE) in a self-learning by doing mode. This approach, that we put in work and test in the setting of a self-learning system(More)
Many collections of structured documents are available on the web. The collection generally describes the characteristics of entities from a single type, where each page describes one entity. These documents are adequate knowledge sources for building ontologies. As they benefit from a strong and shared layout, they contain less well written text than plain(More)
Résumé. Nous présentons une approche pour enrichir automatiquement une ontologie à partir d’un ensemble de pages web structurées. Cette approche s’appuie sur un noyau d’ontologie initial. Son originalité est d’exploiter conjointement la structure des documents et des annotations sémantiques produites à l’aide du noyau d’ontologie pour identifier de nouveaux(More)
We propose an approach to semantically enrich metadata records of satellite imagery with external data. As a result we are able the identify relevant images using a larger set of matching criteria. Conventional methods for annotating data sets are usually based on metadata records (with attributes such as title, provider, access mode, and spatiotemporal(More)