In order to search across factual knowledge and content explicated using different data formats this paper leverages a generic data model (schema) that transforms keyword-based retrieval models and queries to knowledge-oriented models and semantically-expressive queries. As each of the transformed retrieval models capitalises on a specific evidence space (term, classification, relationship and attribute), we demonstrate two possible combinations of these spaces, namely macro-based or micro-based. For bare keyword-based queries we demonstrate how the data model can be used to augment the queries with classifications, relationships, etc. that reflect the underlying constraints and objects found in the heterogeneous knowledge bases. Using the IMDb benchmark the results demonstrate the feasibility and effectiveness of the instantiated retrieval models and the query reformulation process.