Knowledge　graph　reasoning　is　discovering　new　entity　relations　by　computing　and　inference　from　existing　relations.　However,　most　reasoning　models　of　translation　embedding-based　knowledge　graphs　have　not　considered　the　semantic-type　constraints　of　relations　in　the　construction　of　corrupted　triplets.　Hence,　the　constructed　corrupted　triplets　may　not　conform　to　the　actual　semantic　information　and　may,　thus,　significantly　affect　the　prediction　accuracy　of　the　model.　Therefore,　we　propose　a　constraint-based　embedding　model　in　this　paper.　First,　the　model　establishes　the　head　and　tail　entity　set　for　each　relationship.　Then,　it　ensures　that　both　the　replacing　head　and　tail　entities　in　the　corrupted　triplet　belong　to　the　respective　entity　set　so　that　the　corrupted　triplets　that　do　not　conform　to　the　responding　semantic　relations　are　excluded.　To　evaluate　the　proposed　model,　we　conduct　link　prediction　and　triple　classification　on　WordNet　and　Freebase　databases.　The　experimental　results　show　that　our　method　remarkably　improves　the　performance　compared　to　several　state-of-the-art　baselines.　©　2017,　Ubiquitous　International.　All　rights　reserved.