Probabilistic relational data analysis

A non-transitory storage medium storing instructions executable by an electronic data processing device to perform a method including:

• representing a multi-relational data set by a probabilistic multi-relational data model for V relations where V≧2 and at least one relation of the V relations is a relation between at least three entity types and in which each entity of the multi-relational data set is represented by a D-dimensional latent feature vector;
• and
• training by approximating Bayesian posterior probability distributions over the latent feature vectors that are assigned prior distributions based on a collection of observations, wherein the collection of observations include observations of the V relations, the training generating optimized D-dimensional latent feature vectors representing the entities of the multi-relational data set;
• and
• generating a prediction for an observation of a relation v∈V linking at least two entities of the multi-relational data set based on a dot-product of the optimized D-dimensional latent feature vectors representing the entities.