An Overview on Ontology Learning Tasks

Abstract

Ontology Learning (OL) for the Semantic Web has become widely used for knowledge representation. Therefore, the success of the Semantic Web depends strongly on the proliferation of ontologies, which requires fast and sound ontologies engineering learning process in order to provide an efficient knowledge acquisition service. The vision of ontology learning includes a number of complementary disciplines whose feed on different types of unstructured, semi-structured and fully structured data in order to support a semi-automatic, cooperative ontology engineering process. This article presents a general review of work related to types and tasks involving OL. These works consider fundamental types of Ontology Learning, schema extraction, creation and population, besides of evaluation methods and tools.