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Generation of Parametrically Uniform Knowledge Bases in a Relational Probabilistic Logic with Maximum Entropy Semantics

In a relational setting, the maximum entropy model of a set of probabilistic conditionals can be defined referring to the full set of ground instances of the conditionals. The logic FO-PCL uses the notion of parametric uniformity to ensure that the full grounding of the conditionals can be avoided, thereby greatly simplifying the maximum entropy model computation. In this paper, we describe a system that realises an approach transforming an FO-PCL knowledge base consisting of relational probabilistic conditionals into a knowledge base having the same maximum entropy model that is parametrically uniform. The implemented system provides different execution and evaluation modes, including the generation of all possible solutions, and is available within an integrated development environment for relational probabilistic logic.