In addition to knowledge embedding, another interesting approach is the word embedding method word2vec (Mikolov et al., 2013b), which shows that learning word embeddings from an unlabeled text corpus can make the vectors connecting the pairs of words of some certain relation almost parallel, e.g., vec("China") − vec("Beijing") ≈ vec("Japan") − vec("Tokyo").
the embeddings are usually learnt by minimizing a global loss function of all the entities and relations in the knowledge graph.