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Graph embedding models produce embedding vectors for entities and relationsin Knowledge Graphs, often without taking literal properties into account. Weshow an initial idea based on the combination of global graph structure withadditional information provided by textual information in properties. Ourinitial experiment shows that this approach might be useful, but does notclearly outperform earlier approaches when evaluated on machine learning tasks.

A First Experiment on Including Text Literals in KGloVe