Advertisement

RDFS Reasoning on Massively Parallel Hardware

  • Norman Heino
  • Jeff Z. Pan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7649)

Abstract

Recent developments in hardware have shown an increase in parallelism as opposed to clock rates. In order to fully exploit these new avenues of performance improvement, computationally expensive workloads have to be expressed in a way that allows for fine-grained parallelism. In this paper, we address the problem of describing RDFS entailment in such a way. Different from previous work on parallel RDFS reasoning, we assume a shared memory architecture. We analyze the problem of duplicates that naturally occur in RDFS reasoning and develop strategies towards its mitigation, exploiting all levels of our architecture. We implement and evaluate our approach on two real-world datasets and study its performance characteristics on different levels of parallelization. We conclude that RDFS entailment lends itself well to parallelization but can benefit even more from careful optimizations that take into account intricacies of modern parallel hardware.

Keywords

Hash Table Work Item Parallel Hardware Schema Subject Modern GPUs 
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.

References

  1. 1.
    Abadi, D.: Query Execution in Column-Oriented Database Systems. PhD thesis, Massachusetts Institute of Technology (2008)Google Scholar
  2. 2.
    Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.: DBpedia – A crystallization point for the Web of Data. Web Semantics: Science, Services and Agents on the World Wide Web 7(3), 154–165 (2009)CrossRefGoogle Scholar
  3. 3.
    Viegas Damásio, C., Ferreira, F.: Practical RDF Schema Reasoning with Annotated Semantic Web Data. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 746–761. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    DeWitt, D.J., Katz, R.H., Olken, F., Shapiro, L.D., Stonebraker, M.R., Wood, D.A.: Implementation Techniques for Main Memory Database Systems. In: Proc. of the 1984 ACM SIGMOD Intl. Conf. on Management of Data, pp. 1–8. ACM (1984)Google Scholar
  5. 5.
    Hayes, P.: RDF Semantics. W3C Recommendation, W3C (2004), http://www.w3.org/TR/2004/REC-rdf-mt-20040210/
  6. 6.
    Hoffart, J., Berberich, K., Weikum, G.: YAGO2: a Spatially and Temporally Enhanced Knowledge Base from Wikipedia. Artificial Intelligence Journal, Special Issue on Artificial Intelligence, Wikipedia and Semi-Structured Resources (2012)Google Scholar
  7. 7.
    Kaoudi, Z., Miliaraki, I., Koubarakis, M.: RDFS Reasoning and Query Answering on Top of DHTs. In: Sheth, A.P., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 499–516. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Kazakov, Y., Krötzsch, M., Simančík, F.: Concurrent Classification of \(\mathcal{EL}\) Ontologies. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 305–320. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Muñoz, S., Pérez, J., Gutierrez, C.: Minimal Deductive Systems for RDF. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp. 53–67. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Neumann, T., Weikum, G.: RDF-3X: a RISC-style engine for RDF. In: Proc. of the VLDB Endowment, pp. 647–659. VLDB Endowment (2008)Google Scholar
  11. 11.
    Nickolls, J., Dally, W.J.: The GPU Computing Era. IEEE Micro 30(2), 56–69 (2010)CrossRefGoogle Scholar
  12. 12.
    Nuutila, E.: An efficient transitive closure algorithm for cyclic digraphs. Information Processing Letters 52(4), 207–213 (1994)MathSciNetzbMATHCrossRefGoogle Scholar
  13. 13.
    Oren, E., Kotoulas, S., Anadiotis, G., Siebes, R., Ten Teije, A., van Harmelen, F.: Marvin: A platform for large-scale analysis of Semantic Web data. In: Proc. of the WebSci 2009 (2009)Google Scholar
  14. 14.
    Ren, Y., Pan, J.Z., Lee, K.: Parallel ABox Reasoning of \({\mathcal{EL}}\) Ontologies. In: Pan, J.Z., Chen, H., Kim, H.-G., Li, J., Wu, Z., Horrocks, I., Mizoguchi, R., Wu, Z. (eds.) JIST 2011. LNCS, vol. 7185, pp. 17–32. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  15. 15.
    Satish, N., Harris, M., Garland, M.: Designing Efficient Sorting Algorithms for Manycore GPUs. In: Proc. of the IEEE Intl. Symp. on Parallel & Distributed Processing (2009)Google Scholar
  16. 16.
    Sengupta, S., Harris, M., Zhang, Y., Owens, J.: Scan primitives for GPU computing. In: Proc. of the 22nd ACM SIGGRAPH/EUROGRAPHICS Symposium on Graphics Hardware, pp. 97–106. Eurographics Association (2007)Google Scholar
  17. 17.
    Straccia, U., Lopes, N., Lukácsy, G., Polleres, A.: A General Framework for Representing and Reasoning with Annotated Semantic Web Data. In: Proc. of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2010), pp. 1437–1442. AAAI Press (2010)Google Scholar
  18. 18.
    ter Horst, H.J.: Completeness, decidability and complexity of entailment for RDF Schema and a semantic extension involving the OWL vocabulary. Web Semantics: Science, Services and Agents on the World Wide Web 3, 79–115 (2005)MathSciNetCrossRefGoogle Scholar
  19. 19.
    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
  20. 20.
    Warshall, S.: A Theorem on Boolean Matrices. Journal of the ACM 9(1), 11–12 (1962)MathSciNetzbMATHCrossRefGoogle Scholar
  21. 21.
    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 2012

Authors and Affiliations

  • Norman Heino
    • 1
  • Jeff Z. Pan
    • 2
  1. 1.Agile Knowledge Engineering and Semantic Web (AKSW), Department of Computer ScienceLeipzig UniversityLeipzigGermany
  2. 2.Department of Computing ScienceUniversity of AberdeenAberdeenUK

Personalised recommendations