Decision knowledge is a particular type of knowledge that aims at describing the processes of decision making. In oncology, this knowledge is generally grouped into clinical practice guidelines. The publication of the guidelines is provided by medical organizations as a result of complex collaborative editing processes. The computerization of guides has led to the desire of formalizing the knowledge so as to supply decision-support systems. Thus, editing can be seen as a knowledge acquisition issue. In this context, this thesis aims at proposing methods and tools for factorizing editing guides and their formalization. The first contribution of this thesis is the integration of social semantic web technologies in the editing process. The creation of the semantic wiki OncoLogiK allows to implement this proposal. Thus, a feedback and methods are presented for the migration from a static web solution. The second contribution consists in a solution to exploit the knowledge present in the decision-making guides. Thus, KcatoS framework defines a simple decision tree language for which a translation based on semantic web technologies is developed. KcatoS also proposes an editor of trees, allowing collaborative editing online. The third contribution is to combine in a single system approaches for the creation of clinical guidelines: the approach based on the knowledge symbolized by KcatoS and the documentary approach symbolized by OncoLogiK. Their joint operation can propose a solution benefiting from the advantages of both approaches. Many future works are proposed. Most of them aim at improving services to users and the expressiveness of the knowledge base. Taking into account the work and prospects, a realistic model to create a decision-support system based on clinical guidelines is proposed.
