Abstract
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.