Improving the Efficiency of Approximate Inference for Probabilistic
  Logical Models by means of Program Specialization
  We consider the task of performing probabilistic inference with probabilistic
logical models. Many algorithms for approximate inference with such models are
based on sampling. From a logic programming perspective, sampling boils down to
repeatedly calling the same queries on a knowledge base composed of a static
part and a dynamic part. The larger the static part, the more redundancy there
is in these repeated calls. This is problematic since inefficient sampling
yields poor approximations.
  We show how to apply logic program specialization to make sampling-based
inference more efficient. We develop an algorithm that specializes the
definitions of the query predicates with respect to the static part of the
knowledge base. In experiments on real-world data we obtain speedups of up to
an order of magnitude, and these speedups grow with the data-size.
