The crisp/brittle semantic model behind databases limits the scale in which data consumers can query, explore, integrate and process structured data. Approaches aiming to provide more comprehensive semantic models for databases, which are purely logic-based (e.g. as in Semantic Web databases) have major scalability limitations in the acquisition of structured semantic and commonsense data. This work describes a complementary semantic model for databases which has semantic approximation at its center. This model uses distributional semantic models (DSMs) to extend structured data semantics. DSMs support the automatic construction of semantic and commonsense models from large-scale unstructured text and provides a simple model to analyze similarities in the structured data. The combination of distributional and structured data semantics provides a simple and promising solution to address the challenges associated with the interaction and processing of structured data.