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.
Highlights
Adding a semantic dimension to the Web [1], by the deployment of ontologies, contributes to solve many problems: knowledge sharing, semantic access to Web resources and information retrieval
We have developed an online information retrieval based on this ontology to collect and classify the results selected by users
This approach is based on the metaontology which is based on extraction of all textual elements from Web documents which are imported by a search engine
Summary
Adding a semantic dimension to the Web [1], by the deployment of ontologies, contributes to solve many problems: knowledge sharing, semantic access to Web resources and information retrieval. In this paper, knowing that a unique data source cannot cover all concepts of a target domain of knowledge and that Web is a rich textual source, we have chosen to consider the Web as a learning corpus from which domain ontologies are extracted. These ontologies will be used in semantic search systems. We conclude and give some perspectives for this research work
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