This paper presents an integrated modeling framework where the learning and knowledge retrieval mechanisms of the ACT-R cognitive architecture are combined with a semantic resource. We aim to extend ACT-R with a scalable knowledge model, in order to support sub-symbolic processes with consistent, general high-level declarative representations. Design principles, methodology and implementation examples are provided.