  This article deals with the semantic Web and ontologies. It addresses the
issue of the classification of multilingual Web documents, based on domain
ontology. The objective is being able, using a model, to classify documents in
different languages. We will try to solve this problematic using two different
approaches. The two approaches will have two elementary stages: the creation of
the model using machine learning algorithms on a labeled corpus, then the
classification of documents after detecting their languages and mapping their
terms into the concepts of the language of reference (English). But each one
will deal with the multilingualism with a different approach. One supposes the
ontology is monolingual, whereas the other considers it multilingual. To show
the feasibility and the importance of our work, we implemented it on a domain
that attracts nowadays a lot of attention from the data mining community: the
biomedical domain. The selected documents are from the biomedical benchmark
corpus Ohsumed, and the associated ontology is the thesaurus MeSH (Medical
Subject Headings). The main idea in our work is a new document representation,
the masterpiece of all good classification, based on concept. The experimental
results show that the recommended ideas are promising.
