Open Mind Common Sense (OMCS) is a freely available crowd-sourced knowledge base of natural language statements about the world. The goal of Open Mind Common Sense is to provide intuition to AI systems and applications by giving them access to a broad collection of basic information and the computational tools to work with this data. For our system demo, we will be presenting three aspects of the OMCS project: the OMCS knowledge base, the Concept-Net semantic network (Liu and Singh 2004) (Havasi, Speer, and Alonso 2007), and the AnalogySpace algorithm (Speer, Havasi, and Lieberman 2008) which deals well with noisy, user-contributed data. Figure 1: AnalogySpace discovers patterns in common sense Open Mind Common Sense takes a distributed approach knowledge and uses them for inference. The project allows the general public to enter commonsense score to indicate its reliability, which increases either when knowledge into it, without requiring any knowledge a contributor votes for a statement through our Web site of linguistics, artificial intelligence, or computer science.The or when multiple contributors submit equivalent statements OMCS has been collecting commonsense statements from independently.

Increasingly, we need to computationally understand real-time streams of information in places such as news feeds, speech streams, and social networks. We present Streaming AnalogySpace, an efficient technique that discovers correlations in and makes predictions about sparse natural-language data that arrives in a real-time stream. AnalogySpace is a noise-resistant PCA-based inference technique designed for use with collaboratively collected common sense knowledge and semantic networks. Streaming AnalogySpace advances this work by computing it incrementally using CCIPCA, and keeping a dense cache of recently-used features to efficiently represent a sparse and open domain. We show that Streaming AnalogySpace converges to the results of standard AnalogySpace, and verify this by evaluating its accuracy empirically on common-sense predictions against standard AnalogySpace.