Link　prediction　in　open　knowledge　graphs　(OpenKGs)　is　crucial　for　applications　like　question　answering　and　recommendation　systems.　Existing　OpenKG　models　leverage　the　semantic　information　of　noun　phrases　(NPs)　to　enhance　the　performance　in　the　link　prediction　task.　However,　these　models　only　extract　superficial　semantic　information　from　NPs,　ignoring　the　fact　that　an　NP　possesses　diverse　semantics.　Furthermore,　these　models　have　not　fully　exploited　the　semantic　information　of　the　relation　phrases　(RPs).　To　address　these　issues,　we　propose　a　model　for　link　prediction　called　Open　Knowledge　Graph　Link　Prediction　with　Semantic　-Aware　Embedding　(SeAE).　First,　we　develop　an　adaptive　disentanglement　embedding　(ADE)　mechanism　to　learn　the　intrinsically　abundant　semantics　of　NPs.　The　ADE　mechanism　can　adaptively　calculate　the　embedding　segmentation　number　according　to　the　dataset　and　has　an　ingenious　method　for　updating　embeddings.　Second,　we　integrate　the　attention　mechanism　into　the　GRU　encoder　to　obtain　the　distribution　of　importance　inside　RP,　facilitating　a　more　comprehensive　capture　of　the　RP＇s　semantic　information　and　enhancing　the　model＇s　interpretability.　Finally,　we　design　a　relation　gate,　which　extracts　the　RP　semantic　features　of　tail　NP　from　the　shared　edge.　This　gate　realizes　the　relation　constraints　on　entities　while　enhancing　the　interaction　between　entities　and　relations.　Extensive　experiments　on　four　benchmarks　demonstrate　that　SeAE　outperforms　the　state-of-the-art　models,　resulting　in　improvements　of　approximately　5.4%　and　7.4%　in　MRR　on　ReVerb45K　and　ReVerb45KF　datasets　respectively.