  Logical rules are a popular knowledge representation language in many
domains, representing background knowledge and encoding information that can be
derived from given facts in a compact form. However, rule formulation is a
complex process that requires deep domain expertise,and is further challenged
by today's often large, heterogeneous, and incomplete knowledge graphs. Several
approaches for learning rules automatically, given a set of input example
facts,have been proposed over time, including, more recently, neural systems.
Yet, the area is missing adequate datasets and evaluation approaches: existing
datasets often resemble toy examples that neither cover the various kinds of
dependencies between rules nor allow for testing scalability. We present a tool
for generating different kinds of datasets and for evaluating rule learning
systems, including new performance measures.
