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.

Relational learning algorithms learn the Datalog definition of novel relations in terms of existing relations in the database. In order to effectively use these algorithms, users must constraint the space of candidate definitions by specifying a language bias. Unfortunately, specifying the language bias takes a great deal of time and effort, as it is done via trial and error and is guided by the expert's intuitions. We demonstrate AutoMode, a system that leverages information in the schema and content of the database to automatically induce the language bias used by popular relational learning algorithms.

Given a relational database and training examples for a target relation, relational learning algorithms learn a Datalog program that defines the target relation in terms of the existing relations in the database. We demonstrate CastorX, a relational learning system that performs relational learning over heterogeneous databases. The user specifies matching attributes between (heterogeneous) databases through matching dependencies. Because the content in these attributes may not match exactly, CastorX uses similarity operators to find matching values in these attributes. As the learning process may become expensive, CastorX implements sampling techniques that allow it to learn efficiently and output accurate definitions.