RORS:Enhanced Rule-Based OWL Reasoning on Spark
In this paper,we present an approach to enhancing the performance of the rule-based OWL reasoning on Spark based on a locally optimal executable strategy.Firstly,we divide all rules (27 in total) into four main classes,namely,SPO rules (5 rules),type rules (7 rules),sameAs rules (7 rules),and schema rules (8 rules) since,as we investigated,those triples corresponding to the first three classes of rules are overwhelming (e.g.,over 99% in the LUBM dataset) in our practical world.Secondly,based on the interdependence among those entailment rules in each class,we pick out an optimal rule executable order of each class and then combine them into a new rule execution order of all rules.Finally,we implement the new rule execution order on Spark in a prototype called RORS.The experimental results show that the running time of RORS is improved by about 30% as compared to Kim & Parks algorithm (2015) using the LUBM200 (27.6 million triples).
Zhihui Liu Zhiyong Feng Xiaowang Zhang Xin Wang Guozheng Rao
School of Computer Science and Technology,Tianjin University,Tianjin,China;Tianjin Key Laboratory of Cognitive Computing and Application,Tianjin,China
国际会议
International Asia-Pacific Web Conference(第18届国际亚太互联网大会)
苏州
英文
444-448
2016-09-23（万方平台首次上网日期，不代表论文的发表时间）