Inductive Logic Programming, Late Breaking Papers pages:38-43
Inductive Logic Programming 2008 (ILP 2008) edition:18 location:Prague, Czech Republik date:10-12 September 2008
We introduce the problem of learning the parameters of the
probabilistic database ProbLog. Given the observed success probabilities of a set of queries, we compute the probabilities attached to facts that have a low approximation error on the training examples as well as on unseen examples. Assuming Gaussian error terms on the observed success probabilities, this naturally leads to a least squares optimization problem. Our approach, called LeProbLog, is able to learn both from queries and from proofs and even from both simultaneously. This makes it ﬂexible and allows faster training in domains where the proofs are available. Experiments on real world data show the usefulness and efectiveness of this least squares calibration of probabilistic databases.