D2R2: Disk-Oriented Deductive Reasoning in a RISC-Style RDF Engine

  • Mohamed Yahya
  • Martin Theobald
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7018)


Deductive reasoning lies in the expressive intersection of Datalog and Description Logics. In this paper, we present the D2R2 engine, which implements deductive reasoning capabilities based on the Query-Sub-Query (QSQR) algorithm on top of the disk-oriented RDF-3X engine. D2R2 aims to bridge the gap between rule-oriented (intensional) reasoning with deduction rules and data-oriented (extensional) processing of large joins, over a set of highly tuned, disk-based index structures for large RDF collections. We present a generalization of QSQR, which allows for dynamic sub-query scheduling and chaining of extensional predicates into atomic join patterns—two key extensions for coupling QSQR with a disk-oriented storage backend. Experiments over a set of recursive queries and a very large knowledge base, consisting of 20 million RDF facts, as well as comparisons to disk-oriented reasoning engines, confirm the practical viability and significant runtime improvements of D2R2 compared to these engines.


Deductive reasoning QSQR disk-oriented RDF processing 


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  1. 1.
    Abiteboul, S., Hull, R., Vianu, V.: Foundations of Databases. Addison-Wesley, Reading (1995)zbMATHGoogle Scholar
  2. 2.
    Bancilhon, F., Maier, D., Sagiv, Y., Ullman, J.D.: Magic Sets and Other Strange Ways to Implement Logic Programs. In: PODS (1986)Google Scholar
  3. 3.
    Bancilhon, F., Ramakrishnan, R.: An Amateur’s Introduction to Recursive Query Processing Strategies. In: SIGMOD (1986)Google Scholar
  4. 4.
    Beeri, C., Ramakrishnan, R.: On the Power of Magic. In: PODS (1987)Google Scholar
  5. 5.
    Ceri, S., Gottlob, G., Tanca, L.: Logic Programming and Databases. Springer, Heidelberg (1990)CrossRefGoogle Scholar
  6. 6.
    Grosof, B.N., Horrocks, I., Volz, R., Decker, S.: Description logic programs: combining logic programs with description logic. In: WWW (2003)Google Scholar
  7. 7.
    Guo, Y., Pan, Z., Heflin, J.: LUBM: A benchmark for OWL knowledge base systems. J. Web Sem. 3(2-3) (2005)Google Scholar
  8. 8.
    Horrocks, I., Patel-Schneider, P.F., Boley, H., Tabet, S., Grosof, B., Dean, M.: SWRL: A Semantic Web rule language combining OWL and RuleML. Technical report, World Wide Web Consortium (May 2004)Google Scholar
  9. 9.
    Kaoudi, Z., Kyzirakos, K., Koubarakis, M.: SPARQL Query Optimization on Top of DHTs. 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. 418–435. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Madalinska-Bugaj, E., Nguyen, L.A.: Generalizing the QSQR Evaluation Method for Horn Knowledge Bases. In: New Challenges in Applied Intelligence Technologies (2008)Google Scholar
  11. 11.
    Nejdl, W.: Recursive strategies for answering recursive queries - the RQA/FQI strategy. In: VLDB (1987)Google Scholar
  12. 12.
    Neumann, T., Weikum, G.: RDF-3X: a RISC-style engine for RDF. In: PVLDB (2008)Google Scholar
  13. 13.
    RuleML. The rule markup initiative (July 2010),
  14. 14.
    Stocker, M., Seaborne, A., Bernstein, A., Kiefer, C., Reynolds, D.: SPARQL basic graph pattern optimization using selectivity estimation. In: WWW (2008)Google Scholar
  15. 15.
    Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a Core of Semantic Knowledge. In: WWW (2007)Google Scholar
  16. 16.
    Ullman, J.D.: Principles of Database and Knowledge-Base Systems. The New Technologies, vol. II. W. H. Freeman & Co., New York (1990)Google Scholar
  17. 17.
    Vieille, L.: Recursive Axioms in Deductive Databases: The Query/Sub-Query Approach. In: Expert Database Conf. (1986)Google Scholar
  18. 18.
    Vieille, L.: A Database-Complete Proof Procedure Based on SLD Resolution. In: ICLP (1987)Google Scholar
  19. 19.
    Warren, D.S.: Memoing for Logic Programs. Commun. ACM 35(3) (1992)Google Scholar
  20. 20.
    Gallaire, H., Minker, J., Nicolas, J.-M.: Logic and Databases: A deductive approach. ACM Comput. Surv. 16(2) (1984)Google Scholar
  21. 21.
    Kowalski, R.A., Kuehner, D.: Linear Resolution with Selection Function. Artif. Intell. 2(3/4) (1971)Google Scholar
  22. 22.
    Sagonas, K.F., Swift, T., Warren, D.S.: XSB as an Efficient Deductive Database Engine. In: SIGMOD (1994)Google Scholar
  23. 23.
    Liang, S., Fodor, P., Wan, H., Kifer, M.: OpenRuleBench: an analysis of the performance of rule engines. In: WWW (2009)Google Scholar
  24. 24.
    Tamaki, H., Sato, T.: Old Resolution with Tabulation. In: Shapiro, E. (ed.) ICLP 1986. LNCS, vol. 225, pp. 84–98. Springer, Heidelberg (1986)CrossRefGoogle Scholar
  25. 25.
    Neumann, T., Weikum, G.: Scalable Join Processing on Very Large RDF Graphs. In: SIGMOD (2009)Google Scholar
  26. 26.
    Warren, D.H.D.: An Abstract Prolog Instruction Set. Technical Report 309, AI Center, SRI International (1983)Google Scholar
  27. 27.
    Hellerstein, J.M.: Datalog redux: experience and conjecture. In: PODS (2010)Google Scholar
  28. 28.
    Lam, M.S., Whaley, J., Livshits, V.B., Martin, M.C., Avots, D., Carbin, M., Unkel, C.: Context-sensitive program analysis as database queries. In: PODS (2005)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mohamed Yahya
    • 1
  • Martin Theobald
    • 1
  1. 1.Max-Planck Institute for InformaticsSaarbrückenGermany

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