A　knowledge　graph　(KG)　has　been　widely　adopted　to　improve　recommendation　performance.　The　multi-hop　user-item　connections　in　a　KG　can　provide　reasons　for　recommending　an　item　to　a　user.　However,　existing　methods　do　not　effectively　leverage　the　relations　of　entities　and　interpretable　paths　in　a　KG.　To　address　this　limitation,　in　this　paper,　we　propose　a　novel　recommendation　framework　called　relation-enhanced　knowledge　graph　reasoning　for　recommendation　(RE-KGR)　that　combines　recommendation　and　explainability　by　reasoning　user-item　interaction　paths　(UIIPs).　First,　instead　of　applying　an　alignment　algorithm　for　preprocessing,　RE-KGR　directly　learns　the　semantic　representation　of　entities　from　structured　knowledge　by　stacking　relation-based　convolutional　layers　to　take　full　advantage　of　the　KG.　Moreover,　RE-KGR　infers　user　preferences　by　calculating　the　sum　of　all　UIIPs　between　users　and　items.　Finally,　RE-KGR　selects　several　UIIPs　with　the　highest　probabilities　as　possible　reasons　for　the　recommendations.　Extensive　experiments　on　three　real-world　datasets　demonstrate　that　our　proposed　method　significantly　outperforms　several　state-of-the-art　baselines　and　achieves　superior　performance　and　explainability.