  Lifted probabilistic inference algorithms exploit regularities in the
structure of graphical models to perform inference more efficiently. More
specifically, they identify groups of interchangeable variables and perform
inference once per group, as opposed to once per variable. The groups are
defined by means of constraints, so the flexibility of the grouping is
determined by the expressivity of the constraint language. Existing approaches
for exact lifted inference use specific languages for (in)equality constraints,
which often have limited expressivity. In this article, we decouple lifted
inference from the constraint language. We define operators for lifted
inference in terms of relational algebra operators, so that they operate on the
semantic level (the constraints extension) rather than on the syntactic level,
making them language-independent. As a result, lifted inference can be
performed using more powerful constraint languages, which provide more
opportunities for lifting. We empirically demonstrate that this can improve
inference efficiency by orders of magnitude, allowing exact inference where
until now only approximate inference was feasible.
