  Lifting exploits symmetries in probabilistic graphical models by using a
representative for indistinguishable objects, allowing to carry out query
answering more efficiently while maintaining exact answers. In this paper, we
investigate how lifting enables us to perform probabilistic inference for
factor graphs containing factors whose potentials are unknown. We introduce the
Lifting Factor Graphs with Some Unknown Factors (LIFAGU) algorithm to identify
symmetric subgraphs in a factor graph containing unknown factors, thereby
enabling the transfer of known potentials to unknown potentials to ensure a
well-defined semantics and allow for (lifted) probabilistic inference.
