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PRSPR: An Adaptive Framework for Massive RDF Stream Reasoning

  • Guozheng Rao
  • Bo Zhao
  • Xiaowang ZhangEmail author
  • Zhiyong Feng
  • Guohui Xiao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10987)

Abstract

In this paper, we propose a plugin-based framework for massive RDF stream reasoning to support complicated tasks on RDF stream in an adaptive and flexible way. Within this framework, the problem of RDF stream reasoning can be equivalently reduced to the combination problem of SPARQL querying and rule-based reasoning. Take advantage of the plug-in method, we can apply various off-the-shelf SPARQL query engines and rule-based reasoners in a simple way. Moreover, to efficiently support real-time reasoning on massive RDF stream, we develop a multi-threaded batch processing approach to manage resources and an adaptive reasoning plan for dynamically managing inference rules in the stream reasoning. Finally, our experiments evaluate on dataset built on the benchmark LUBM and DBpedia. The experimental results show that our framework is effective and efficient.

Notes

Acknowledgments

We would like to thank Qiong Li for constructive comments. This work is supported by the National Natural Science Foundation of China (61373165, 61672377), the National Key R&D Program of China (2016YFB1000603, 2017YFC0908401), and the Key Technology R&D Program of Tianjin (16YFZCGX00210).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Guozheng Rao
    • 1
    • 3
  • Bo Zhao
    • 1
    • 3
  • Xiaowang Zhang
    • 1
    • 3
    Email author
  • Zhiyong Feng
    • 2
    • 3
  • Guohui Xiao
    • 4
  1. 1.School of Computer Science and TechnologyTianjin UniversityTianjinChina
  2. 2.School of Computer SoftwareTianjin UniversityTianjinChina
  3. 3.Tianjin Key Laboratory of Cognitive Computing and ApplicationTianjinChina
  4. 4.Faculty of Computer ScienceFree University of Bozen-BolzanoBolzanoItaly

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