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.

翻译：在具有第一级构造的概率化形式主义中,标准推论方法包括取消单项查询的可变消除(LVE)和根据加权模型计算得出的第一级知识汇编(FOKC)。为了高效处理多个查询,已取消的交接点树算法(LJT)在计算时使用一阶模型和LVE作为子例。对于某些投入,LVE的实施,以及因此,FOKC已解除运行的模型的LJT地面部分。本文的目的是准备LJT作为升序推断的支柱,并使用任何精确推算法作为子路程。在LJT中,使用FOKC作为计算某些输入的速度比LJT、LVE和FOKC更快的答案。