Logically Scalable and Efficient Relational Learning

Relational learning algorithms learn the definition of a new relation in terms of existing relations in the database. The same database may be represented under different schemas for various reasons, such as efficiency, data quality, and usability. Unfortunately, the output of current relational learning algorithms tends to vary quite substantially over the choice of schema, both in terms of learning accuracy and efficiency. We introduce the property of schema independence of relational learning algorithms, and study both the theoretical and empirical dependence of existing algorithms on the common class of (de) composition schema transformations. We show theoretically and empirically that current relational learning algorithms are generally not schema independent. We propose Castor, a relational learning algorithm that achieves schema independence.