  Neural link predictors learn distributed representations of entities and
relations in a knowledge graph. They are remarkably powerful in the link
prediction and knowledge base completion tasks, mainly due to the learned
representations that capture important statistical dependencies in the data.
Recent works in the area have focused on either designing new scoring functions
or incorporating extra information into the learning process to improve the
representations. Yet the representations are mostly learned from the observed
links between entities, ignoring commonsense or schema knowledge associated
with the relations in the graph. A fundamental aspect of the topology of
relational data is the cardinality information, which bounds the number of
predictions given for a relation between a minimum and maximum frequency. In
this paper, we propose a new regularisation approach to incorporate relation
cardinality constraints to any existing neural link predictor without affecting
their efficiency or scalability. Our regularisation term aims to impose
boundaries on the number of predictions with high probability, thus,
structuring the embeddings space to respect commonsense cardinality assumptions
resulting in better representations. Experimental results on Freebase, WordNet
and YAGO show that, given suitable prior knowledge, the proposed method
positively impacts the predictive accuracy of downstream link prediction tasks.
