Computing the Stratified Semantics of Logic Programs over Big Data through Mass Parallelization

  • Ilias Tachmazidis
  • Grigoris Antoniou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8035)


Increasingly huge amounts of data are published on the Web, and generated from sensors and social media. This Big Data challenge poses new scientific and technological challenges and creates new opportunities - thus the increasing attention in academia and industry. Traditionally, logic programming has focused on complex knowledge structures/programs, so the question arises whether and how it can work in the face of Big Data. In this paper, we examine how stratified semantics of logic programming, equivalent to the well-founded semantics for stratified programs, can process huge amounts of data through mass parallelization. In particular, we propose and evaluate a parallel approach using the MapReduce framework. Our experimental results indicate that our approach is scalable and that stratified semantics of logic programming can be applied to billions of facts.


Logic Program Logic Programming Will Emit MapReduce Framework Common Argument 
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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  1. 1.
    Answer sets. In: van Harmelen, F., Lifschitz, V., Porter, B. (eds.) Handbook of Knowledge Representation, ch. 7Google Scholar
  2. 2.
    Afrati, F.N., Ullman, J.D.: Optimizing joins in a mapreduce environment. In: EDBT (2010)Google Scholar
  3. 3.
    Baader, F., Kosters, R.: Nonstandard Inferences in Description Logics: The Story So Far. In: Mathematical Problems from Applied Logic I. International Mathematical Series, vol. 4 (2006)Google Scholar
  4. 4.
    Billington, D.: Defeasible Logic is Stable. J. Log. Comput. 3(4), 379–400 (1993)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clustersGoogle Scholar
  6. 6.
    Duan, S., Kementsietsidis, A., Srinivas, K., Udrea, O.: Apples and oranges: a comparison of RDF benchmarks and real RDF datasetsGoogle Scholar
  7. 7.
    Fensel, D., van Harmelen, F., Andersson, B., Brennan, P., Cunningham, H., Valle, E.D., Fischer, F., Huang, Z., Kiryakov, A., Il Lee, T.K., Schooler, L., Tresp, V., Wesner, S., Witbrock, M., Zhong, N.: Towards larkc: A platform for web-scale reasoning. In: ICSC, pp. 524–529 (2008)Google Scholar
  8. 8.
    Fische, F.: Investigation & Design for Rule-based Reasoning. Tech. rep., LarKC (2010)Google Scholar
  9. 9.
    Goodman, E.L., Jimenez, E., Mizell, D., Al-Saffar, S., Adolf, B., Haglin, D.: High-Performance Computing Applied to Semantic Databases. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 2011, Part II. LNCS, vol. 6644, pp. 31–45. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    Haase, C., Lutz, C.: Complexity of Subsumption in the EL Family of Description Logics: Acyclic and Cyclic TBoxes. In: ECAI 2008, pp. 25–29 (2008)Google Scholar
  11. 11.
    Konstantinidis, G., Flouris, G., Antoniou, G., Christophides, V.: A Formal Approach for RDF/S Ontology Evolution. In: ECAI (2008)Google Scholar
  12. 12.
    Kotoulas, S., van Harmelen, F., Weaver, J.: KR and Reasoning on the Semantic Web: Web-Scale Reasoning (2011)Google Scholar
  13. 13.
    Kotoulas, S., Oren, E., van Harmelen, F.: Mind the data skew: distributed inferencing by speeddating in elastic regions. In: WWW, pp. 531–540 (2010)Google Scholar
  14. 14.
    Liang, S., Fodor, P., Wan, H., Kifer, M.: Openrulebench: an analysis of the performance of rule engines. In: Proceedings of the 18th International Conference on World Wide Web, WWW 2009, pp. 601–610. ACM, New York (2009), CrossRefGoogle Scholar
  15. 15.
    Nebel, B.: Terminological Reasoning is Inherently Intractable. Artificial Intelligence 43, 235–249 (1990)MathSciNetzbMATHCrossRefGoogle Scholar
  16. 16.
    Oren, E., Kotoulas, S., Anadiotis, G., Siebes, R., ten Teije, A., van Harmelen, F.: Marvin: Distributed reasoning over large-scale Semantic Web data. J. Web Sem. 7(4), 305–316 (2009)CrossRefGoogle Scholar
  17. 17.
    Ross, K.A.: The well-founded semantics for general logic programs. Journal of the ACM 38, 620–650 (1991)zbMATHCrossRefGoogle Scholar
  18. 18.
    Roussakis, Y., Flouris, G., Christophides, V.: Declarative Repairing Policies for Curated KBs. In: HDMS (2011)Google Scholar
  19. 19.
    Serfiotis, G., Koffina, I., Christophides, V., Tannen, V.: Containment and Minimization of RDF/S Query Patterns. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 607–623. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  20. 20.
    Tachmazidis, I., Antoniou, G., Flouris, G., Kotoulas, S.: Towards parallel nonmonotonic reasoning with billions of facts. In: KR (2012)Google Scholar
  21. 21.
    Tachmazidis, I., Antoniou, G., Flouris, G., Kotoulas, S., McCluskey, L.: Large-scale parallel stratified defeasible reasoning. In: ECAI, pp. 738–743 (2012)Google Scholar
  22. 22.
    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, Part I. LNCS, vol. 6088, pp. 213–227. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  23. 23.
    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

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ilias Tachmazidis
    • 1
  • Grigoris Antoniou
    • 1
  1. 1.University of HuddersfieldUK

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