Lifted Graphical Models: A Survey

TL;DR

This survey comprehensively reviews lifted graphical models, including their formalism, inference algorithms, and learning methods, emphasizing their growing importance in handling complex, relational data in the era of big data.

Contribution

It introduces a unified formalism for lifted graphical models and synthesizes recent advances in inference and learning, serving as an accessible overview for new researchers.

Findings

Unified formalism for lifted graphical models

Efficient lifted inference algorithms

Review of learning methods from data

Abstract

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.