Time – Space Trade-Offs in Scaling up RDF Schema Reasoning

  • Heiner Stuckenschmidt
  • Jeen Broekstra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3807)

Abstract

A common way of reducing run time complexity of RDF Schema reasoning is to compute (parts of) the deductive closure of a model offline. This reduces the complexity at run time, but increases the space requirements and model maintenance because derivable facts have to be stored explicitly and checked for validity when the model is updated. In this paper we experimentally identify certain kinds of statements as the major sources for the increase. Based on this observation, we develop a new approach for RDF reasoning that only computes a small part of the implied statements offline thereby reducing space requirements, upload time and maintenance overhead. The computed fragment is chosen in such a way that the problem of inferring implied statements at run time can be reduced to a simple form of query re-writing. This new methods has two benefits: it reduces the amount of storage space needed and it allows to perform online reasoning without using a dedicated inference engine.

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References

  1. 1.
    Broekstra, J., Kampman, A., van Harmelen, F.: Sesame: A generic architecture for storing and querying RDF and RDF schema. In: ISWC 2002. LNCS, vol. 2342, pp. 54–68. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  2. 2.
    Broekstra, J., Kampman., A.: Inferencing and truth maintenance in rdf schema: exploring a naive practical approach. In: Workshop on Practical and Scalable Semantic Systems (PSSS) at the Second International Semantic Web Conference (ISWC), Sanibel Island, Florida (October 2003)Google Scholar
  3. 3.
    Forgy, C.L.: A fast algorithm for the many pattern / many object pattern match problem. Artificial Intelligence 19, 17–37 (1982)CrossRefGoogle Scholar
  4. 4.
    Guo, Y., Pan, Z., Heflin, J.: An evaluation of knowledge base systems for large OWL datasets. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004, vol. 3298, pp. 274–288. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Gutierrez, C., Hurtado, C., Mendelzon, A.O.: Foundations of semantic web databases. In: ACM Symposium on Principles of Database Systems (PODS), Paris, France (June 2004)Google Scholar
  6. 6.
    Hayes, P.: Rdf semantics. Recommendation, W3C, February 10, 2004 (2004)Google Scholar
  7. 7.
    Horrocks, I., Hendler, J. (eds.): ISWC 2002. LNCS, vol. 2342. Springer, Heidelberg (2002)MATHGoogle Scholar
  8. 8.
    Lassila, O.: Taking the rdf model theory out for a spin. In: The Semantic Web-ISWC 2002 [7], pp. 307–317Google Scholar
  9. 9.
    Wielemaker, J., Schreiber, G., Wielinga, B.: Prolog-based infrastructure for rdf: Scalability and performance. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 644–658. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Heiner Stuckenschmidt
    • 1
  • Jeen Broekstra
    • 2
  1. 1.Vrije Universiteit Amsterdam 
  2. 2.Aduna, Amerfoort 

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