  In this thesis, we study the problem of feature learning on heterogeneous
knowledge graphs. These features can be used to perform tasks such as link
prediction, classification and clustering on graphs. Knowledge graphs provide
rich semantics encoded in the edge and node types. Meta-paths consist of these
types and abstract paths in the graph. Until now, meta-paths can only be used
as categorical features with high redundancy and are therefore unsuitable for
machine learning models. We propose meta-path embeddings to solve this problem
by learning semantical and compact vector representations of them. Current
graph embedding methods only embed nodes and edge types and therefore miss
semantics encoded in the combination of them. Our method embeds meta-paths
using the skipgram model with an extension to deal with the redundancy and high
amount of meta-paths in big knowledge graphs. We critically evaluate our
embedding approach by predicting links on Wikidata. The experiments indicate
that we learn a sensible embedding of the meta-paths but can improve it
further.
