الفهرس 
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Abstract
Recently, many researchers pay attention to ontology learning because of the wide range of the use of ontologies in different fields of science. Ontology learning concerns of the constructing ontologies from unstructured, semi-structured, or structured data sources in an efficient manner. The main challenge in the ontology learning process is the extraction of ontology components such as concepts, taxonomic and non-taxonomic relationships without human intervention. Furthermore, enriching ontologies from multilingual data remains a challenge. Therefore, there is a pressing need to develop innovative approaches, which are capable of enriching ontologies using a well-formed one, even in a different natural language. Few researchers have addressed the problem of multilingual and cross-lingual ontology enrichment. The main objective of this thesis is to enrich ontologies from multilingual and different domains data source using multi-agent approach. Multi-agent systems have been widely employed in many domains due to the distributed processing offered, efficiency, maintainability, and scalability. This thesis presents the cross-lingual ontology enrichment (CLOE2.0) approach based on a multi-agent architecture in order to enrich several ontologies, in different languages, belonging to different domains, from a multilingual text or other ontologies.