This PhD thesis deals with topographic databases integration. This process aims at facilitating the use of several heterogeneous databases by making the relationships between them explicit. To automatically achieve databases integration, several aspects of data heterogeneity must be detected and solved. Identifying heterogeneities between topographic databases implies comparing some knowledge about their respective contents. Therefore, we propose to formalise and acquire this knowledge and to use it for topographic databases integration. Our work focuses on the specific problem of topographic databases schema matching, as a first step in an integration application. To reach this goal, we propose to use a specific knowledge source, namely the databases specifications, which describe the data implementing rules. Firstly, they are used as the main resource for the knowledge acquisition process in an ontology learning application. As a first approach for schema matching, the domain ontology created from the texts of IGN's databases specifications is used as a background knowledge source in a schema matching application based on terminological and structural matching techniques. In a second approach, this ontology is used to support the representation, in the OWL 2 language, of topographic entities selection and geometry capture rules described in the databases specifications. This knowledge is then used by a reasoner in a semantic-based schema matching application
