1. A machine learning system comprising: an input arranged to access a plurality of relational databases; at least one store holding a model of a probabilistic relational database formed from one or more of the plurality of relational databases, the model including one or more relation schemas, each relation schema having a plurality of probabilistic attributes, wherein the model includes relational algebra instructions that query data from the one or more of the plurality of relational databases, the relational algebra instructions including: one or more factor operations each of which specifies a mapping for the queried data from each relational database to a probability distribution; for each relation schema, at least one augmentation clause that adds the plurality of probabilistic attributes to the relation schema; and at least one factor clause that specifies how a factor node is to be connected between probabilistic attributes in a factor graph data structure; a processor arranged to translate the model into the factor graph data structure using rules stored at a memory; and an inference engine arranged to perform an inference according to the factor graph data structure by using the queried data from each relational database and updating values of the plurality of probabilistic attributes for each relation schema.
7. A machine learning system as claimed in claim 1 wherein the inference engine is further arranged to perform the inference using message passing applied to the factor graph data structure.