Ontology Based Machine Learning for Semantic Multiclass Classification

François-Élie Calvier (1) , Michel Plantié (1) , Gérard Dray (1) , Sylvie Ranwez (1)

1 LGI2P - Laboratoire de Génie Informatique et Ingénierie de Production

Following the development of semantic web technologies, many ontologies and thesauri have been proposed to index resources dur- ing the last decade. However, despite their expressiveness, those knowl- edge models do not always cover all the points of interest within dedi- cated applications. Therefore, alternative ad hoc taxonomies have been developed to support resources classifying processes. This paper proposes a method that bridges existing knowledge mod- els with ad hoc taxonomies to address the problem of textual docu- ments classification. Usually, documents are indexed according to differ- ent knowledge models: keywords, thesauri, ontologies. Nevertheless, for a project leader, additional information are needed to organize documents. In response to a particular need of one of our partners, we have devel- oped a learning method based on the use of ontologies for modelling a semantic classification process. This method allows the expert user to match their needs by optimising text document classification.

Domaines: Traitement du texte et du document

François-Élie Calvier, Michel Plantié, Gérard Dray, Sylvie Ranwez. Ontology Based Machine Learning for Semantic Multiclass Classification. TOTH : Terminologie & Ontologie : Théories et Applications 2013, Jun 2013, Chambéry, France. pp.100. ⟨hal-00838262⟩