This thesis, set at the crossroads of Social Web and Semantic Web, is an attempt to bridge Social tagging-based systems with structured representations such as thesauri or ontologies (in the informatics sense). Folksonomies resulting from the use of social tagging systems suffer from a lack of precision that hinders their potentials to retrieve or exchange information. This thesis proposes supporting the use of folksonomies with formal languages and ontologies from the Semantic Web. Automatic processing of tags allows bootstraping the process by using a combination of a custom method analyzing tags' labels and adapted methods analyzing the structure of folksonomies. The contributions of users are described thanks to our model SRTag, which allows supporting diverging points of view, and captured thanks to our user friendly interface allowing the users to structure tags while searching the folksonomy. Conflicts between individual points of view are detected, solved, and then exploited to help a referent user maintain a global and coherent structuring of the folksonomy, which is in return used to garanty the coherence while enriching individual contributions with the others' contributions. The result of our method allows enhancing the navigation within tag-based knowledge systems, but can also serve as a basis for building thesauri fed by a truly bottom up process.