  We present discriminative Gaifman models, a novel family of relational
machine learning models. Gaifman models learn feature representations bottom up
from representations of locally connected and bounded-size regions of knowledge
bases (KBs). Considering local and bounded-size neighborhoods of knowledge
bases renders logical inference and learning tractable, mitigates the problem
of overfitting, and facilitates weight sharing. Gaifman models sample
neighborhoods of knowledge bases so as to make the learned relational models
more robust to missing objects and relations which is a common situation in
open-world KBs. We present the core ideas of Gaifman models and apply them to
large-scale relational learning problems. We also discuss the ways in which
Gaifman models relate to some existing relational machine learning approaches.
