  We explore the idea of using a "possibilistic graphical model" as the basis
for a world model that drives a dialog system. As a first step we have
developed a system that uses text-based dialog to derive a model of the user's
family relations. The system leverages its world model to infer relational
triples, to learn to recover from upstream coreference resolution errors and
ambiguities, and to learn context-dependent paraphrase models. We also explore
some theoretical aspects of the underlying graphical model.
