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RORS: Enhanced Rule-Based OWL Reasoning on Spark

  • Zhihui Liu
  • Zhiyong Feng
  • Xiaowang Zhang
  • Xin Wang
  • Guozheng Rao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9932)

Abstract

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 & Park’s algorithm (2015) using the LUBM200 (27.6 million triples).

Notes

Acknowledgments

This work is supported by the program of the National Key Research and Development Program of China (2016YFB1000603) and the National Natural Science Foundation of China (NSFC) (61502336, 61373035, 61572353). Xiaowang Zhang is supported by Tianjin Thousand Young Talents Program.

References

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Zhihui Liu
    • 1
    • 2
  • Zhiyong Feng
    • 1
    • 2
  • Xiaowang Zhang
    • 1
    • 2
  • Xin Wang
    • 1
    • 2
  • Guozheng Rao
    • 1
    • 2
  1. 1.School of Computer Science and TechnologyTianjin UniversityTianjinChina
  2. 2.Tianjin Key Laboratory of Cognitive Computing and ApplicationTianjinChina

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