Researchers　have　investigated　various　graph　embedding　methods　to　complete　Knowledge　Graphs　(KGs),　most　of　which　merely　focus　on　Static　KGs　(SKGs)　without　emphasizing　the　time　dependence　of　triple-formed　facts.　However,　in　reality,　KGs　are　dynamic　and　definitely　there　is　correlations　between　facts　with　different　timestamps.　Due　to　the　sparsity　of　Temporal　KGs　(TKGs),　SKG’s　embedding　methods　cannot　be　directly　applied　to　TKGs,　which　triggers　the　current　discussions　about　TKG　Completion　(TKGC)　task.　And　existing　TKGC　methods　universally　suffer　from　two　issues:　(i)　The　modeling　procedure　for　temporal　information　in　encoder　is　usually　disjointed　or　conflict　with　that　in　decoder.　(ii)　Current　methods　are　overwhelmingly　dependent　on　temporal　signals　for　measuring　the　probability　of　candidate　entity,　while　ignoring　other　signals　(such　as　entity’s　semantics,　etc.,).　To　overcome　these　problems,　this　paper　proposes　a　novel　semantic-driven　time-aware　relational　graph　neural　network　model　for　TKGC　task,　which　consists　of　a　semantic-enhanced　encoder　and　a　convolution-based　decoder.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG　2023.