We propose a novel approach to enrich Wikidata with the textual content of Wikipedia. Specifically, we leverage knowledge graph (KG) embedding models to classify the hyperlinks between Wikipedia articles and predict the corresponding facts. For instance, we would like to complete the triple (Berlin, *, Germany) with the relation capital of, given a hyperlink from Berlin to Germany in Wikipedia. While existing KG embedding models can be used for this task of relation prediction, they were not explicitly designed for it and their performance is not satisfactory. In this paper, we propose two methods that greatly improve the performance of these models on this task: first, a new negative sampling method that balances the roles of entities and relations during training; second, a method to exploit the types of entities in the selection of candidate relations. We obtain accuracy scores as high as 94% on the popular FB15k237 dataset and 75% on WDV5, an extraction of Wikidata. The efficiency of the approach is illustrated on some Wikipedia pages, where new facts unknown to Wikidata are predicted by our method.