  Machine learning, deep learning, and NLP methods on knowledge graphs are
present in different fields and have important roles in various domains from
self-driving cars to friend recommendations on social media platforms. However,
to apply these methods to knowledge graphs, the data usually needs to be in an
acceptable size and format. In fact, knowledge graphs normally have high
dimensions and therefore we need to transform them to a low-dimensional vector
space. An embedding is a low-dimensional space into which you can translate
high dimensional vectors in a way that intrinsic features of the input data are
preserved. In this review, we first explain knowledge graphs and their
embedding and then review some of the random walk-based embedding methods that
have been developed recently.
