  RDF2vec is a technique for creating vector space embeddings from an RDF
knowledge graph, i.e., representing each entity in the graph as a vector. It
first creates sequences of nodes by performing random walks on the graph. In a
second step, those sequences are processed by the word2vec algorithm for
creating the actual embeddings. In this paper, we explore the use of external
edge weights for guiding the random walks. As edge weights, transition
probabilities between pages in Wikipedia are used as a proxy for the human
feedback for the importance of an edge. We show that in some scenarios, RDF2vec
utilizing those transition probabilities can outperform both RDF2vec based on
random walks as well as the usage of graph internal edge weights.
