Inductive　Knowledge　Graph　Completion　(KGC)　poses　challenges　due　to　the　absence　of　emerging　entities　during　training.　Current　methods　utilize　Graph　Neural　Networks　(GNNs)　to　learn　and　propagate　entity　representations,　achieving　notable　performance.　However,　these　approaches　primarily　focus　on　chain-based　logical　rules,　limiting　their　ability　to　capture　the　rich　semantics　of　knowledge　graphs.　To　address　this　challenge,　we　propose　to　generate　Graph-based　Rules　for　Enhancing　Logical　Reasoning　(GRELR),　a　novel　framework　that　leverages　graph-based　rules　for　enhanced　reasoning.　GRELR　formulates　graph-based　rules　by　extracting　relevant　subgraphs　and　fuses　them　to　construct　comprehensive　relation　representations.　This　approach,　combined　with　subgraph　reasoning,　significantly　improves　inference　capabilities　and　showcases　the　potential　of　graph-based　rules　in　inductive　KGC.　To　demonstrate　the　effectiveness　of　the　GRELR　framework,　we　conduct　experiments　on　three　benchmark　datasets,　and　our　approach　achieves　state-of-the-art　performance.