Large Scale Fuzzy pD* Reasoning Using MapReduce

  • Chang Liu
  • Guilin Qi
  • Haofen Wang
  • Yong Yu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7031)

Abstract

The MapReduce framework has proved to be very efficient for data-intensive tasks. Earlier work has tried to use MapReduce for large scale reasoning for pD * semantics and has shown promising results. In this paper, we move a step forward to consider scalable reasoning on top of semantic data under fuzzy pD * semantics (i.e., an extension of OWL pD * semantics with fuzzy vagueness). To the best of our knowledge, this is the first work to investigate how MapReduce can help to solve the scalability issue of fuzzy OWL reasoning. While most of the optimizations used by the existing MapReduce framework for pD * semantics are also applicable for fuzzy pD * semantics, unique challenges arise when we handle the fuzzy information. We identify these key challenges, and propose a solution for tackling each of them. Furthermore, we implement a prototype system for the evaluation purpose. The experimental results show that the running time of our system is comparable with that of WebPIE, the state-of-the-art inference engine for scalable reasoning in pD * semantics.

Keywords

Resource Description Framework Transitive Closure Fuzzy Graph MapReduce Framework Reasoning Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Chang Liu
    • 1
  • Guilin Qi
    • 2
  • Haofen Wang
    • 1
  • Yong Yu
    • 1
  1. 1.Shanghai Jiaotong UniversityChina
  2. 2.Southeast UniversityChina

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