This article argues that currently the largest gap between human and machine learning is learning algorithms’ inability to perform deep transfer, that is, generalize from one domain to another domain containing different objects, classes, properties, and relations. We argue that
second-order Markov logic is ideally suited for this purpose and propose an approach based on it. Our algorithm discovers structural regularities in the source domain in the form of Markov logic formulas with predicate variables and instantiates these formulas with predicates from the target domain. Our approach has successfully transferred learned knowledge among molecular biology, web, and social network domains.