Our work is at the crossroads of educational adaptive hypermedia and the competency based approach. Adaptive Hypermedia Systems (AHS) are based on a domain model and a learner model to provide a virtual hypermedia to the learner. To achieve the interconnection between the domain representation and a virtual hypermedia, different techniques are used. We are particularly interested by the technique of direct relationship that allows building the structure of the virtual hypermedia by associating to each concept of the discipline one or more physical pages of the virtual hypermedia in one hand. In the other hand, each of the semantic links between the discipline concepts corresponds to hyperlinks in the virtual hypermedia. The AHS have already proven their usefulness in the context of self-learning of subject knowledge in the case of objective based pedagogies. We propose here to use them in the context of self-learning of disciplinary competencies. Indeed, we suggest an AHS that generates a guide map of the disciplinary competencies with all the other connected concepts to study according to the semantics of the discipline defined by the domain ontology in the background of the disciplinary knowledge base and the learner's profile. This last is deduced from the learner's model where we put mainly three classes of parameters: cognitive ones that define the state of knowledge particularly the respective performance level for each competency, resolved problem situation ..., type of intelligence which defines a learning style, preferences and identification parameters. The disciplinary competencies domain ontology used to generate the AHS domain is designed around these main concepts: disciplinary composed competency, disciplinary elementary or granular competency, contextualized problem situation, potential obstacles, potential instructions ... Also; we have semantic relations including the prerequisite relationship and the composition one. A prototype was developed to test the feasibility of the dynamic generation of the guide map for the case of first level relational databases learning where a knowledge base was generated from the proposed domain ontology. After that, the prototype was tested with a group of students from our university. The results were interesting.