This work proposes a process for the identification of the requirements associated with the data, along with and a set of transformations in Model Driven Architecture (MDA) context, in order to obtain a semantically annotated dataset, as a result of the unification and alignment of the data in the context of its initial domain. Our proposal identifies four phases (from CIM to code), in which is describe the artifacts and transformations required to progress to the next phase: a target domain model is first obtained from the data requirements, after which the ontological schema and the ontology is generated from the previous model. A domain-specific language (DSL), also proposed in this work, is then used to obtain the semantic data model (the DSL code), which generates the final semantic dataset. We have validated the proposal by studying two cases: one with data from the public transport domain and the other with data concerning those affected by the COVID-19 pandemic.