基于Spark的分布式并行推理算法

摘要

Multiple MapReduce tasks are needed for most of current distributed parallel reasoning algorithm for RDF data; moreover, the reasoning of instances of triple antecedents under OWL rules can't be performed expeditiously by some of these algorithms during the processing of massive RDF data, and so the overall efficiency can't be fulfilled in reasoning process. In order to solve the problems mentioned above, a method named distributed parallel reasoning algorithm based on Spark with TREAT for RDF data is proposed to perform reasoning on distributed systems. First step, alpha registers of schema triples and models for rule markup with the ontology of RDF data are bui then alpha stage of TREAT algorithm is implemented with MapReduce at the phase of OWL reasoning; at last, reasoning results are dereplicated and a whole reasoning procedure within all the OWL rules is executed. Experimental results show that through this algorithm, the results of parallel reasoning for large-scale data can be achieved efficiently and correctly.%现有的RDF数据分布式并行推理算法大多需要启动多个MapReduce任务,有些算法对于含有多个实例三元组前件的OWL规则的推理效率低下,使其整体的推理效率不高.针对这些问题,文中提出结合TREAT的基于Spark的分布式并行推理算法(DPRS).该算法首先结合RDF数据本体,构建模式三元组对应的alpha寄存器和规则标记模型;在OWL推理阶段,结合MapReduce实现TREAT算法中的alpha阶段;然后对推理结果进行去重处理,完成一次OWL全部规则推理.实验表明DPRS算法能够高效正确地实现大规模数据的并行推理.