Abstract 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.
About author:: 汪璟玢(通讯作者),女,1973年生,硕士,副教授,主要研究方向为海量数据管理、网络数据库、智能技术.E-mail:wjbcc@263.net. (WANG Jingbin(Corresponding author), born in 1973, master, associate professor. Her research interests include big data management, network database and intelligence technology.) 郑翠春,女,1989年生,硕士研究生,主要研究方向为海量数据管理、智能技术.E-mail:software_cui@sina.com. (ZHENG Cuichun, born in 1989, master student. Her research interests include big data management and intelligence technology.)
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2016, Vol. 29 
