Multilingual Medical Documents Classification Based on MesH Domain Ontology
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.
Keywords: multilingual classification, medical document, concept, domains ontology, Ohsumed, MeSH
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ABOUT THE AUTHORS
Elberrichi Zakaria
Zakaria Elberrichi received his Master degree with thesis in computer science from the California State University, in addition to PGCert in higher education, and received his PhD in computer science from the university Djillali Liabes, Sidi-Belabbes, where he has been a faculty member ever since. He is also a member of EEDIS (Evolutionary Engineering and Distributed Information Systems) laboratory and the project head - manager of the data mining and intelligent web team.
Taibi Malika
Taibi Malika received her Master with thesis degree from the computer science department at the UDL university with honor in 2011, and is currently a doctorate student and a member of the research team intelligent web mining.
Belaggoun Amel
Belaggoun Amel received her Master with thesis degree from the computer science department at the UDL university with honor in 2011, and is currently a doctorate students and a member of the research team intelligent web mining.
Elberrichi Zakaria
Zakaria Elberrichi received his Master degree with thesis in computer science from the California State University, in addition to PGCert in higher education, and received his PhD in computer science from the university Djillali Liabes, Sidi-Belabbes, where he has been a faculty member ever since. He is also a member of EEDIS (Evolutionary Engineering and Distributed Information Systems) laboratory and the project head - manager of the data mining and intelligent web team.
Taibi Malika
Taibi Malika received her Master with thesis degree from the computer science department at the UDL university with honor in 2011, and is currently a doctorate student and a member of the research team intelligent web mining.
Belaggoun Amel
Belaggoun Amel received her Master with thesis degree from the computer science department at the UDL university with honor in 2011, and is currently a doctorate students and a member of the research team intelligent web mining.
