A large body of machine learning and AI is focused on learning models composed of (probabilistic) logical rules, i.e., relational models, over relational databases and knowledge bases. To learn effective relational models over the huge space of possible ones efficiently, users of the current learning systems must restrict the structure of the candidate models using language bias. ML experts have to spend a long time inspecting the data and performing many rounds of trial and error to develop an effective language bias. We propose AutoBias, a system that leverages information in the underlying data to generate the language bias. As its induced language bias may not restrict the set of candidate models as tightly as the manually-written ones, learning may not scale to large datasets. Thus, we design novel and efficient methods to sample and learn effective relational models over large data. Our extensive empirical study shows that AutoBias delivers the same accuracy as using manually-written language bias by imposing only a slight overhead on the learning time.