  We started with a knowledge graph of connected entities and descriptive
properties of those entities, from which, a hierarchical representation of the
knowledge graph is derived. Using a graphical, energy-based neural network, we
are able to show that the structure of the hierarchy can be internally captured
by the neural network, which allows for efficient output of the underlying
equilibrium distribution from which the data are drawn.
