The　growing　demand　for　the　meaningful　use　of　electronic　medical　records　has　led　to　great　interest　in　medical　entities　and　relation　extraction　technologies.　Most　existing　methods　perform　relation　extraction　based　on　manually　labeled　documents　and　rarely　consider　incorporating　knowledge　graphs　that　include　rich,　valuable　structured　knowledge,　which　will　cause　semantic　ambiguities.　To　address　this　problem,　we　propose　a　knowledge-enhanced　relation　extraction　(KERE)　model.　First,　we　extract　knowledge　information　from　the　knowledge　graph　to　generate　a　knowledge-guided　word　embedding.　Then,　the　lexical　features　are　considered　supplementary　information　for　semantic　understanding.　Experiments　on　real-world　datasets　show　that　the　KERE　model　achieves　important　improvements　in　a　biomedical　relation　extraction　task.