Recently　the　topic　of　how　to　improve　the　efficiency　of　semantic　reasoning　on　large-scale　knowledge　graph　has　gained　considerable　attention　from　global　researchers　and　engineers.　Most　of　existing　distributed　parallel　algorithms　for　inference　based　on　OWL　Horst　ruleset　require　multiple　iterations.　Moreover,　in　the　process　of　which,　the　data　stored　repeatedly　generate　redundant　records,　resulting　the　reasoning　in　low　overall　efficiency.　In　order　to　address　the　challenges,　firstly,　we　presents　a　storage　solution　combining　variable　storage　and　multivariable　connector　in　accordance　with　characteristics　of　OWL　Horst　ruleset　in　the　context　of　knowledge　graph,　aiming　at　reduction　of　repeated　data　storage　and　data　transmission　cost.　Then,　on　the　basis　of　such　scheme,　a　streaming　reasoning　algorithm　is　introduced　to　curtail　iterations　and　promote　efficiency.　Experimental　results　on　LUBM　and　DBpedia　datasets　demonstrate　that　our　proposed　framework　and　algorithm　could　deliver　superior　performance　in　scalability　and　efficiency.