Relational Machine Learning

                                      Filip Železný

                Czech Technical University in Prague, Czech Republic

Abstract. I will explain the main concepts of relational machine learning, or more
precisely, those parts of it employing logic as the knowledge-representation formalism.
The talk will not cover other relational approaches such as graph-mining. I will follow
what I consider the three main stages of the field’s historical development. First, I will
visit the roots of relational learning lying in the area of inductive logic programming.
Here, one learns logical theories from examples, formalizing the problem as search in
a clause subsumption lattice. A newer stream of research called statistical relational
learning extended the logical underpinnings with probabilistic inference. I will illustrate
this with an example of a logical graphical probabilistic model. Most recently, relational
learning has received a new impetus from the current revival of (deep) neural networks.
I will exemplify some promising crossovers of the two fields, including the paradigm of
Lifted Relational Neural Networks conceived in my lab.