This chapter proposes a framework for the construction of cognitively plausible semantic information spaces and emphasizes on the ways in which the framework may be applied towards the design of cognitively adequate spatializations. A cognitively plausible Information Visualization is designed such that it matches human's internal visualization capabilities. The proposed framework focuses on the use of geographic space as a data generalization strategy (ontology) and the use of spatial representations or maps to depict these data abstractions. The building blocks of this spatialization framework are informed by geographic information theory and include principles of ontological modeling such as semantic generalization (spatial primitives), geometric generalization (visual variables), association (source–target domain mapping through spatial metaphors), and aggregation (hierarchical organization). Spatialization is defined as a data transformation method based on spatial metaphors, with the aim of generating a cognitively adequate graphic representation for data exploration and knowledge discovery in multi-dimensional databases. Spatialization not only provides the construction of visual descriptions and summaries of large data repositories but also creates opportunities for visual queries and sense-making approaches.