  Large probabilistic models are often shaped by a pool of known individuals (a
universe) and relations between them. Lifted inference algorithms handle sets
of known individuals for tractable inference. Universes may not always be
known, though, or may only described by assumptions such as "small universes
are more likely". Without a universe, inference is no longer possible for
lifted algorithms, losing their advantage of tractable inference. The aim of
this paper is to define a semantics for models with unknown universes decoupled
from a specific constraint language to enable lifted and thereby, tractable
inference.
