Lobna Karoui

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This paper deals with the automation of ontology building process from HTML pages. Our methodology is based on the complementary use of two approaches. The first approach is based on Aussenac-Gilles methodology and requires feedback from the user to propose and refine concepts and their relationships. The second one exploits Web pages structure and is based(More)
In this paper, we focus on the ontological concept extraction and evaluation process from HTML documents. In order to improve this process, we propose an unsupervised hierarchical clustering algorithm namely " Contextual Concept Discovery " (CCD) which is an incremental use of the partitioning algorithm Kmeans and is guided by a structural context. Our(More)
Relation extraction is a difficult open research problem with important applications in several fields such as knowledge management, web mining, ontology building, intelligent systems, etc. In our research, we focus on extracting relations among the ontological concepts in order to build a domain ontology. In this paper, firstly, we answer some crucial(More)
AbstrAct Research in ontology learning had always separated between ontology building and evaluation tasks. Moreover, it had used for example a sentence, a syntactic structure or a set of words to establish the context of a word. However, this research avoids accounting for the structure of the document and the relation between the contexts. In our work, we(More)
Un des problèmes majeurs dans la gestion des ontologies est son éva-luation. Cet article traite l'évaluation des concepts ontologiques qui sont ex-traits de pages Web. Pour cela, nous avons proposé une méthodologie d'évaluation des concepts basée trois critères révélateurs : « le degré de crédi-bilité »; « le degré de cohésion » et « le degré d'éligibilité(More)