Pour identifier des mappings entre les concepts de deux ontologies, de nombreux travaux récents portent sur l'utilisation de connaissances complémentaires dites de "background" ou de support, représentées sous la forme d'une 3 ème ontologie. Leur objectif commun est de compléter les techniques classiques d'appariement qui exploitent la structure ou la richesse du langage de représentation des ontologies, et qui ne s'appliquent plus quand les ontologies à apparier sont faiblement structurées ou se limitent à de simples taxonomies. Cet article comporte deux parties. La première présente une étude de différents travaux utilisant des connaissances de support, en commençant par leur schéma général commun, suivi par une analyse des travaux en fonction du type de connaissance de support utilisée. Une seconde partie est consacrée au système d'alignement TaxoMap. Nous présentons le système et son contexte d'utilisation. Nous décrivons ensuite l'utilisation de WordNet comme connaissance de support ainsi que les résultats d'expérimentation obtenus. ABSTRACT. A lot of alignment systems providing mappings between the concepts of two ontologies rely on the use of background knowledge, represented most of the time by a third ontology. The common objective is to complement current matching techniques which exploit structure or features represented in ontology representation languages and which fail when ontologies are only hierarchies or weakly structured models. This paper has two parts. First, we present a state-of-the-art of research work using background knowledge. A common general scheme is first introduced followed by an analysis of works that differ by the kind of background knowledge they use. The second part is dedicated to TaxoMap. We present the use context and the general architecture of the system. Then, we describe the way WordNet is exploited in TaxoMap as support knowledge together with experimental results.

Ontology alignment is an important task for information integration systems that can make different resources, described by various and heterogeneous ontologies, interoperate. However very large ontologies have been built in some domains such as medicine or agronomy and the challenge now lays in scaling up alignment techniques that often perform complex tasks. In this paper, we propose two partitioning methods which have been designed to take the alignment objective into account in the partitioning process as soon as possible. These methods transform the two ontologies to be aligned into two sets of blocks of a limited size. Furthermore, the elements of the two ontologies that might be aligned are grouped in a minimal set of blocks and the comparison is then enacted upon these blocks. Results of experiments performed by the two methods on various pairs of ontologies are promising.