Fusing First-order Knowledge Compilation and the Lifted Junction Tree
  Algorithm
  Standard approaches for inference in probabilistic formalisms with
first-order constructs include lifted variable elimination (LVE) for single
queries as well as first-order knowledge compilation (FOKC) based on weighted
model counting. To handle multiple queries efficiently, the lifted junction
tree algorithm (LJT) uses a first-order cluster representation of a model and
LVE as a subroutine in its computations. For certain inputs, the
implementations of LVE and, as a result, LJT ground parts of a model where FOKC
has a lifted run. The purpose of this paper is to prepare LJT as a backbone for
lifted inference and to use any exact inference algorithm as subroutine. Using
FOKC in LJT allows us to compute answers faster than LJT, LVE, and FOKC for
certain inputs.
