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)


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.


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.


  1. 1.
    Dean, J., Ghemawat, S.: MapReduce: Simplified Data Processing on Large Clusters. In: Proc. of OSDI 2004, pp. 137–147 (2004)Google Scholar
  2. 2.
    Guo, Y., Pan, Z., Heflin, J.: Lubm: A benchmark for owl knowledge base systems. Journal of Web Semantics 3(2), 158–182 (2005)CrossRefGoogle Scholar
  3. 3.
    Horst, H.J.: Completeness, decidability and complexity of entailment for rdf schema and a semantic extension involving the owl vocabulary. Journal of Web Semantics 3(2-3), 79–115 (2005)CrossRefGoogle Scholar
  4. 4.
    Liu, C., Qi, G., Wang, H., Yu, Y.: Fuzzy Reasoning over RDF Data using OWL Vocabulary. In: Proc. of WI 2011 (2011)Google Scholar
  5. 5.
    Newman, A., Li, Y.-F., Hunter, J.: Scalable semantics - the silver lining of cloud computing. In: Proc. of ESCIENCE 2008 (2008)Google Scholar
  6. 6.
    Lopes, N., Polleres, A., Straccia, U., Zimmermann, A.: AnQL: SPARQLing up Annotated RDFS. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010, Part I. LNCS, vol. 6496, pp. 518–533. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Pan, J.Z., Stamou, G., Stoilos, G., Taylor, S., Thomas, E.: Scalable querying services over fuzzy ontologies. In: Proc. of WWW 2008, pp. 575–584 (2008)Google Scholar
  8. 8.
    Schlicht, A., Stuckenschmidt, H.: Peer-to-peer reasoning for interlinked ontologies, vol. 4, pp. 27–58 (2010)Google Scholar
  9. 9.
    Soma, R., Prasanna, V.: Parallel inferencing for owl knowledge bases. In: Proc. of ICPP 2008, pp. 75–82 (2008)Google Scholar
  10. 10.
    Straccia, U.: A minimal deductive system for general fuzzy RDF. In: Proc. of RR 2009, pp. 166–181 (2009)Google Scholar
  11. 11.
    Straccia, U., Lopes, N., Lukacsy, G., Polleres, A.: A general framework for representing and reasoning with annotated semantic web data. In: Proc. of AAAI 2010, pp. 1437–1442. AAAI Press (2010)Google Scholar
  12. 12.
    Urbani, J., Kotoulas, S., Maassen, J., van Harmelen, F., Bal, H.: OWL Reasoning with WebPIE: Calculating the Closure of 100 Billion Triples. In: Aroyo, L., Antoniou, G., Hyvönen, E., ten Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC 2010. LNCS, vol. 6088, pp. 213–227. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Urbani, J., Kotoulas, S., Oren, E., van Harmelen, F.: Scalable Distributed Reasoning Using MapReduce. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 634–649. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    Weaver, J., Hendler, J.A.: Parallel materialization of the finite RDFS closure for hundreds of millions of triples. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 682–697. Springer, Heidelberg (2009)CrossRefGoogle Scholar

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

Personalised recommendations