Building　knowledge　graphs　based　on　relational　databases　has　increasingly　become　a　research　focus　in　the　field　of　knowledge　engineering.　This　paper　explores　the　mapping　methods　for　converting　relational　data　into　graph　data,　using　the　construction　of　clinical　knowledge　graphs　from　electronic　medical　record　(EMR)　relational　data　as　a　case　study.　By　analyzing　the　differences　in　data　expression　structures　between　EMR　relational　data　and　knowledge　graph　models,　the　paper　proposes　a　semantic　model　for　mapping　relational　data　to　graph　data　that　meets　application　semantic　requirements,　defines　rules　for　converting　relational　data　with　varying　granularity　and　semantic　constraints　into　graph　structures,　and　designs　a　mapping　algorithm　where　the　order　of　mapping　is　determined　by　node　degree.　Experiments　demonstrate　that　the　proposed　method　can　effectively　implemente　knowledge　graph　construction　based　on　relational　data.　©　2024　SPIE.