When extending probabilistic logic to a relational setting, it is desirable to still be able to use efficient inference mechanisms developed for the propositional case. In this paper, we investigate the relational probabilistic conditional logic FO-PCL whose semantics employs the principle of maximum entropy. While in general, this semantics is defined via the ground instances of the rules in an FO-PCL knowledge base
, the maximum entropy model can be computed on the level of rules rather than on the level of instances of the rules if
is parametrically uniform, thus providing lifted inference.We elaborate in detail the reasons precluding
from being parametrically uniform. Based on this investigation, we derive a new syntactic criterion for parametric uniformity and develop an algorithm that transforms any FO-PCL knowledge base
into an equivalent knowledge base
that is parametrically uniform.