We conduct extensive large scale experiments on Freebase and Wikipedia corpus, which show jointly embedding brings promising improvements to the accuracy of predicting facts, compared to separately embedding the knowledge graph and the text corpus, respectively.
jointly embedding enables the prediction of a candidate fact with out-of-kb entities, which can not be handled by any existing embedding methods.