  As KGs are symbolic constructs, specialized techniques have to be applied in
order to make them compatible with data mining techniques. RDF2Vec is an
unsupervised technique that can create task-agnostic numerical representations
of the nodes in a KG by extending successful language modelling techniques. The
original work proposed the Weisfeiler-Lehman (WL) kernel to improve the quality
of the representations. However, in this work, we show both formally and
empirically that the WL kernel does little to improve walk embeddings in the
context of a single KG. As an alternative to the WL kernel, we propose five
different strategies to extract information complementary to basic random
walks. We compare these walks on several benchmark datasets to show that the
\emph{n-gram} strategy performs best on average on node classification tasks
and that tuning the walk strategy can result in improved predictive
performances.
