Using singular value decomposition on the graph structure of ConceptNet yields AnalogySpace, a vector space representation of common sense knowledge. In this technique, the analogical closure of a semantic network is formed through dimensionality reduction. It self-organizes concepts around dimensions that can be seen as making distinctions such as "good vs. bad" or "animate vs. inanimate", and generalizes its knowledge by judging where concepts lie along these dimensions. This representation reveals large-scale patterns in the data, while smoothing over noise, and predicts new knowledge that the database should contain. Additional techniques extend this model to streaming data sets, association-based similarity, metacognition, and information that varies over time.