  Latent features learned by deep learning approaches have proven to be a
powerful tool for machine learning. They serve as a data abstraction that makes
learning easier by capturing regularities in data explicitly. Their benefits
motivated their adaptation to relational learning context. In our previous
work, we introduce an approach that learns relational latent features by means
of clustering instances and their relations. The major drawback of latent
representations is that they are often black-box and difficult to interpret.
This work addresses these issues and shows that (1) latent features created by
clustering are interpretable and capture interesting properties of data; (2)
they identify local regions of instances that match well with the label, which
partially explains their benefit; and (3) although the number of latent
features generated by this approach is large, often many of them are highly
redundant and can be removed without hurting performance much.
