  This thesis is a proof of concept for the potential of Variational
Auto-Encoder (VAE) on representation learning of real-world Knowledge Graphs
(KG). Inspired by successful approaches to the generation of molecular graphs,
we evaluate the capabilities of our model, the Relational Graph Variational
Auto-Encoder (RGVAE). The impact of the modular hyperparameter choices,
encoding through graph convolutions, graph matching and latent space prior, is
compared. The RGVAE is first evaluated on link prediction. The mean reciprocal
rank (MRR) scores on the two datasets FB15K-237 and WN18RR are compared to the
embedding-based model DistMult. A variational DistMult and a RGVAE without
latent space prior constraint are implemented as control models. The results
show that between different settings, the RGVAE with relaxed latent space,
scores highest on both datasets, yet does not outperform the DistMult. Further,
we investigate the latent space in a twofold experiment: first, linear
interpolation between the latent representation of two triples, then the
exploration of each latent dimension in a $95\%$ confidence interval. Both
interpolations show that the RGVAE learns to reconstruct the adjacency matrix
but fails to disentangle. For the last experiment we introduce a new validation
method for the FB15K-237 data set. The relation type-constrains of generated
triples are filtered and matched with entity types. The observed rate of valid
generated triples is insignificantly higher than the random threshold. All
generated and valid triples are unseen. A comparison between different latent
space priors, using the $\delta$-VAE method, reveals a decoder collapse.
Finally we analyze the limiting factors of our approach compared to molecule
generation and propose solutions for the decoder collapse and successful
representation learning of multi-relational KGs.
