Loglinear models for first-order probabilistic reasoning
  Recent work on loglinear models in probabilistic constraint logic programming
is applied to first-order probabilistic reasoning. Probabilities are defined
directly on the proofs of atomic formulae, and by marginalisation on the atomic
formulae themselves. We use Stochastic Logic Programs (SLPs) composed of
labelled and unlabelled definite clauses to define the proof probabilities. We
have a conservative extension of first-order reasoning, so that, for example,
there is a one-one mapping between logical and random variables. We show how,
in this framework, Inductive Logic Programming (ILP) can be used to induce the
features of a loglinear model from data. We also compare the presented
framework with other approaches to first-order probabilistic reasoning.
