Nathalie Aussenac-Gilles

Learn More
In the article, we present Dynamo (an acronym of DYNAMic Ontologies), a tool based on an adaptive multi-agent system to construct and maintain an ontology from a domain specific set of texts. The originality of our proposal is that the adaptative multi-agent system is used both to represent the ontology itself and to produce the ontology. This enables us to(More)
The analysis of current approaches combining links and contents for scientific topics discovery reveals that the two sources of information (i.e. links and contents) are considered to be heterogeneous. Therefore, in this paper, we propose to integrate link and content information by exploiting the links semantics to enrich the textual content of documents.(More)
This paper introduces a new approach to provide users with solutions to explore a domain via an information space. A key point in our approach is that information searching and exploring takes place in a domaindependent semantic context. A given context is described through its vocabulary organised along hierarchies that structure the information space.(More)
This article describes a real-world semantic information retrieval tool for automotive diagnosis. Troubleshooting documents have always been popular within car workshops / manufacturers as a simple and direct way to capitalize knowledge on the one hand and to access repair information on the other hand. However, with more and more complex vehicle(More)
The development of the Semantic Web has provoked an increasing interest in the development of ontologies. There are, however, few mechanisms for guiding users in making informed decisions on which ontology to use under given circumstances. In this paper, we propose a framework for evaluating the quality of ontologies based on the SQuaRE standard for(More)
La construction automatique d’ontologies à partir de textes est généralement basée sur le texte proprement dit, et le domaine décrit est circonscrit au contenu du texte. Afin de concevoir des ontologies sémantiquement plus riches, nous proposons d’étendre les méthodes classiques en matière de construction d’ontologie (1) en prenant en compte le texte du(More)
Manual ontology development and evolution are complex and time-consuming tasks, even when textual documents are used as knowledge sources in addition to human expertise or existing ontologies. Processing natural language in text produces huge amounts of linguistic data that need to be filtered out and structured. To support both of these tasks, we have(More)