  Many real-world domains can be expressed as graphs and, more generally, as
multi-relational knowledge graphs. Though reasoning and learning with knowledge
graphs has traditionally been addressed by symbolic approaches, recent methods
in (deep) representation learning has shown promising results for specialized
tasks such as knowledge base completion. These approaches abandon the
traditional symbolic paradigm by replacing symbols with vectors in Euclidean
space. With few exceptions, symbolic and distributional approaches are explored
in different communities and little is known about their respective strengths
and weaknesses. In this work, we compare representation learning and relational
learning on various relational classification and clustering tasks and analyse
the complexity of the rules used implicitly by these approaches. Preliminary
results reveal possible indicators that could help in choosing one approach
over the other for particular knowledge graphs.
