  Learning knowledge representation is an increasingly important technology
that supports a variety of machine learning related applications. However, the
choice of hyperparameters is seldom justified and usually relies on exhaustive
search. Understanding the effect of hyperparameter combinations on embedding
quality is crucial to avoid the inefficient process and enhance practicality of
vector representation methods. We evaluate the effects of distinct values for
the margin parameter focused on translational embedding representation models
for multi-relational categorized data. We assess the margin influence regarding
the quality of embedding models by contrasting traditional link prediction task
accuracy against a classification task. The findings provide evidence that
lower values of margin are not rigorous enough to help with the learning
process, whereas larger values produce much noise pushing the entities beyond
to the surface of the hyperspace, thus requiring constant regularization.
Finally, the correlation between link prediction and classification accuracy
shows traditional validation protocol for embedding models is a weak metric to
represent the quality of embedding representation.
