With　the　upcoming　data　deluge　of　semantic　data,　the　fast　growth　of　ontology　bases　has　brought　significant　challenges　in　performing　efficient　and　scalable　reasoning.　Traditional　centralized　reasoning　methods　are　not　sufficient　to　process　large　ontologies.　Distributed　reasoning　methods　are　thus　required　to　improve　the　scalability　and　performance　of　inferences.　This　paper　proposes　an　incremental　and　distributed　inference　method　for　large-scale　ontologies　by　using　MapReduce,　which　realizes　high-performance　reasoning　and　runtime　searching,　especially　for　incremental　knowledge　base.　By　constructing　transfer　inference　forest　and　effective　assertional　triples,　the　storage　is　largely　reduced　and　the　reasoning　process　is　simplified　and　accelerated.　Finally,　a　prototype　system　is　implemented　on　a　Hadoop　framework　and　the　experimental　results　validate　the　usability　and　effectiveness　of　the　proposed　approach.