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In this paper, we propose an approach of information extraction, based on an ontology, and applied to documents from advertising catalogs. Documents are relatively poor descriptions of products. The information to be extracted, or annotations, concern the categories and features of the products, listed in a domain ontology. Thus, the information extraction about a product is actually an ontology population process, more precisely the population of concepts representing its categories and features. The poverty of the descriptions makes a fully automatic population impossible. We propose a two-step approach: (1) a first semi-Automatic annotation step, which covers a small set of documents; (2) a second step, which annotates all other documents, in an entirely automatic way, based on machine learning mechanisms exploiting the results of the first step. The originality of this work relies on an incremental approach to refine the extracted information. The work described has been applied on real data, in the toy domain.
information extraction, ontology population, semantic annotation, B2C application.
Nous remercions la société Wepingo qui a financé ce travail dans le cadre du projet PORASO.
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