Knowledge　graph　embedding　aims　to　project　entities　and　relations　into　low-dimensional　and　continuous　semantic　feature　spaces,　which　has　captured　more　attention　in　recent　years.　Most　of　the　existing　models　roughly　construct　negative　samples　via　a　uniformly　random　mode,　by　which　these　corrupted　samples　are　practically　trivial　for　training　the　embedding　model.　Inspired　by　generative　adversarial　networks　(GANs),　the　generator　can　be　employed　to　sample　more　plausible　negative　triplets,　that　boosts　the　discriminator　to　improve　its　embedding　performance　further.　However,　vanishing　gradient　on　discrete　data　is　an　inherent　problem　in　traditional　GANs.　In　this　paper,　we　propose　a　generative　adversarial　network　based　knowledge　graph　representation　learning　model　by　introducing　the　Wasserstein　distance　to　replace　traditional　divergence　for　settling　this　issue.　Moreover,　the　additional　weak　supervision　information　is　also　absorbed　to　refine　the　performance　of　embedding　model　since　these　textual　information　contains　detailed　semantic　description　and　offers　abundant　semantic　relevance.　In　the　experiments,　we　evaluate　our　method　on　the　tasks　of　link　prediction　and　triplet　classification.　The　experimental　results　indicate　that　the　Wasserstein　distance　is　capable　of　solving　the　problem　of　vanishing　gradient　on　discrete　data　and　accelerating　the　convergence,　additional　weak　supervision　information　also　can　significantly　improve　the　performance　of　the　model.　(C)　2019　Published　by　Elsevier　B.V.