  Latent factor models are increasingly popular for modeling multi-relational
knowledge graphs. By their vectorial nature, it is not only hard to interpret
why this class of models works so well, but also to understand where they fail
and how they might be improved. We conduct an experimental survey of
state-of-the-art models, not towards a purely comparative end, but as a means
to get insight about their inductive abilities. To assess the strengths and
weaknesses of each model, we create simple tasks that exhibit first, atomic
properties of binary relations, and then, common inter-relational inference
through synthetic genealogies. Based on these experimental results, we propose
new research directions to improve on existing models.
