Most　of　the　current　distributed　parallel　reasoning　algorithms　for　resource　description　framework　(RDF)　data　need　multiple　MapReduce　tasks.　However,　the　reasoning　of　instances　of　triple　antecedents　under　resource　description　framework　schema　(RDFS)　/ontology　web　language　(OWL)　rules　can　not　be　performed　expeditiously　by　some　of　these　algorithms　during　processing　massive　RDF　data,　and　the　overall　efficiency　in　reasoning　process　is　not　satisfactory.　To　solve　this　problem,　a　distributed　parallel　reasoning　algorithm　with　Rete　for　RDF　data　on　MapReduce　(DRRM)　is　proposed　to　perform　reasoning　on　distributed　systems.　Firstly,　lists　of　schema　triples　and　models　for　rule　markup　with　the　ontology　of　RDF　data　are　built,　and　then　alpha　stage　and　beta　stage　of　Rete　algorithm　are　implemented　with　MapReduce　at　the　phase　of　RDFS/OWL　reasoning.　Finally,　the　dereplication　of　reasoning　results　is　conducted　and　a　whole　reasoning　procedure　of　all　the　RDFS/OWL　rules　is　executed.　Experimental　results　show　that　the　results　of　parallel　reasoning　for　large-scale　data　can　be　achieved　efficiently　and　correctly　by　the　proposed　algorithm.　©　2016,　Science　Press.　All　right　reserved.