  This article presents a survey of work on lifted graphical models. We review
a general form for a lifted graphical model, a par-factor graph, and show how a
number of existing statistical relational representations map to this
formalism. We discuss inference algorithms, including lifted inference
algorithms, that efficiently compute the answers to probabilistic queries. We
also review work in learning lifted graphical models from data. It is our
belief that the need for statistical relational models (whether it goes by that
name or another) will grow in the coming decades, as we are inundated with data
which is a mix of structured and unstructured, with entities and relations
extracted in a noisy manner from text, and with the need to reason effectively
with this data. We hope that this synthesis of ideas from many different
research groups will provide an accessible starting point for new researchers
in this expanding field.
