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RDF2vec is a technique for creating vector space embeddings from an RDFknowledge graph, i.e., representing each entity in the graph as a vector. Itfirst creates sequences of nodes by performing random walks on the graph. In asecond step, those sequences are processed by the word2vec algorithm forcreating the actual embeddings. In this paper, we explore the use of externaledge weights for guiding the random walks. As edge weights, transitionprobabilities between pages in Wikipedia are used as a proxy for the humanfeedback for the importance of an edge. We show that in some scenarios, RDF2vecutilizing those transition probabilities can outperform both RDF2vec based onrandom walks as well as the usage of graph internal edge weights.

Towards Exploiting Implicit Human Feedback for Improving RDF2vec Embeddings