  Recent years have seen increasing popularity of logic-based reasoning
systems, with research and industrial interest as well as many flourishing
applications in the area of Knowledge Graphs. Despite that, one can observe a
substantial lack of specific tools able to generate nontrivial reasoning
settings and benchmark scenarios. As a consequence, evaluating, analysing and
comparing reasoning systems is a complex task, especially when they embody
sophisticated optimizations and execution techniques that leverage the
theoretical underpinnings of the adopted logic fragment. In this paper, we aim
at filling this gap by introducing iWarded, a system that can generate very
large, complex, realistic reasoning settings to be used for the benchmarking of
logic-based reasoning systems adopting Datalog+/-, a family of extensions of
Datalog that has seen a resurgence in the last few years. In particular,
iWarded generates reasoning settings for Warded Datalog+/-, a language with a
very good tradeoff between computational complexity and expressive power. In
the paper, we present the iWarded system and a set of novel theoretical results
adopted to generate effective scenarios. As Datalog-based languages are of
general interest and see increasing adoption, we believe that iWarded is a step
forward in the empirical evaluation of current and future systems.
