Survey on ontology learning from Web and open issues

Abstract : With the continual increase of the volume of available information on the Web, information access and knowledge management become challenging. Thus, adding a semantic dimension to the Web, by the deployment of ontologies, contributes to solve many problems. In the context of the semantic Web, ontologies improve the exploitation of Web resources by adding a consensual field of knowledge. The need for using domain ontology for information retrieval (IR) has been explored by some approaches to better answer users' queries. However, ontology in IR system requires a regular updating, especially the addition of new concepts and relationships. In fact, IR systems are generally based on few number of domain ontology that cannot be extended. This paper proposes a survey of main several approaches of ontology learning from Web. In a previous work, we have proposed an incremental approach for ontology learning using an ontological representation called "Metaontology". In this paper, we describe a how the processes of semantic search and ontology learning from texts can collaborate for learning of multilayer ontology warehouse.
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Nesrine Ben Mustapha, Hajer Baazaoui, Marie-Aude Aufaure, Henda Ben Ghezala. Survey on ontology learning from Web and open issues. ISIICT'09: Third international conference on Innovation and Information and Communication Technology, Jun 2009, Amman, Jordan. pp.1-1. ⟨hal-00831675⟩

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