Growth Patterns of Inference

What properties of a first-order search space support/hinder inference? What kinds of facts would be most effective to learn? Answering these questions is essential for understanding the dynamics of deductive reasoning and creating large-scale knowledge-based learning systems that support efficient inference. We address these questions by developing a model of how the distribution of ground facts affects inference performance in search spaces. Experiments suggest that uniform search spaces are suitable for larger KBs whereas search spaces with skewed degree distribution show better performance in smaller KBs. A sharp transition in Q/A performance is seen in some cases, suggesting that analysis of the structure of search spaces with existing knowledge should be used to guide the acquisition of new ground facts in learning systems.