An Efficient Light Solver for Querying the Semantic Web

  • Vianney le Clément de Saint-Marcq
  • Yves Deville
  • Christine Solnon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6876)

Abstract

The Semantic Web aims at building cross-domain and distributed databases across the Internet. SPARQL is a standard query language for such databases. Evaluating such queries is however NP-hard. We model SPARQL queries in a declarative way, by means of CSPs. A CP operational semantics is proposed. It can be used for a direct implementation in existing CP solvers. To handle large databases, we introduce a specialized and efficient light solver, Castor. Benchmarks show the feasibility and efficiency of the approach.

Keywords

Resource Description Framework Constraint Programming Operational Semantic Subgraph Isomorphism SPARQL Query 
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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References

  1. 1.
    Angles, R., Gutierrez, C.: The expressive power of SPARQL. In: Sheth, A.P., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 114–129. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Arias, M., Fernández, J.D., Martínez-Prieto, M.A., de la Fuente, P.: An empirical study of real-world SPARQL queries. In: 1st International Workshop on Usage Analysis and the Web of Data (USEWOD 2011), in Conjunction with WWW 2011 (2011)Google Scholar
  3. 3.
    Baget, J.F.: RDF entailment as a graph homomorphism. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 82–96. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Broekstra, J., Kampman, A., van Harmelen, F.: Sesame: A generic architecture for storing and querying RDF and RDF Schema. In: Horrocks, I., Hendler, J. (eds.) ISWC 2002. LNCS, vol. 2342, pp. 54–68. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  5. 5.
    Cafarella, M.J., Halevy, A., Madhavan, J.: Structured data on the web. Commun. ACM 54, 72–79 (2011)CrossRefGoogle Scholar
  6. 6.
    Dynamic Decision Technologies Inc.: Comet (2010), http://www.dynadec.com
  7. 7.
    Erling, O., Mikhailov, I.: RDF support in the Virtuoso DBMS. In: Networked Knowledge – Networked Media. SCI, vol. 221, pp. 7–24. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Gecode Team: Gecode: Generic constraint development environment (2006), http://www.gecode.org
  9. 9.
    Harris, S., Shadbolt, N.: SPARQL query processing with conventional relational database systems. In: Dean, M., Guo, Y., Jun, W., Kaschek, R., Krishnaswamy, S., Pan, Z., Sheng, Q.Z. (eds.) WISE 2005 Workshops. LNCS, vol. 3807, pp. 235–244. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Harris, S., Lamb, N., Shadbolt, N.: 4store: The design and implementation of a clustered RDF store. In: 5th International Workshop on Scalable Semantic Web Knowledge Base Systems (SSWS 2009), at ISWC 2009 (2009)Google Scholar
  11. 11.
    Klyne, G., Carroll, J.J., McBride, B.: Resource description framework (RDF): Concepts and abstract syntax (2004), http://www.w3.org/TR/2004/REC-rdf-concepts-20040210/
  12. 12.
    Lohfert, R., Lu, J., Zhao, D.: Solving SQL constraints by incremental translation to SAT. In: Nguyen, N.T., Borzemski, L., Grzech, A., Ali, M. (eds.) IEA/AIE 2008. LNCS (LNAI), vol. 5027, pp. 669–676. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    Mamoulis, N., Stergiou, K.: Constraint satisfaction in semi-structured data graphs. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 393–407. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  14. 14.
    Mouhoub, M., Feng, C.: CSP techniques for solving combinatorial queries within relational databases. In: Nguyen, N.T., Szczerbicki, E. (eds.) Intelligent Systems for Knowledge Management. SCI, vol. 252, pp. 131–151. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  15. 15.
    Pérez, J., Arenas, M., Gutierrez, C.: Semantics and complexity of SPARQL. ACM Trans. Database Syst. 34, 16:1–16:45 (2009)Google Scholar
  16. 16.
    Prud’hommeaux, E., Seaborne, A.: SPARQL query language for RDF (January 2008), http://www.w3.org/TR/2008/REC-rdf-sparql-query-20080115/
  17. 17.
    Schmidt, M., Hornung, T., Lausen, G., Pinkel, C.: SP2Bench: A SPARQL performance benchmark. In: Proc. IEEE 25th Int. Conf. Data Engineering, ICDE 2009, pp. 222–233 (2009)Google Scholar
  18. 18.
    Schmidt, M., Meier, M., Lausen, G.: Foundations of SPARQL query optimization. In: Proceedings of the 13th International Conference on Database Theory, ICDT 2010, pp. 4–33. ACM, New York (2010)Google Scholar
  19. 19.
    Siva, S., Wang, L.: A SQL database system for solving constraints. In: Proceeding of the 2nd PhD Workshop on Information and Knowledge Management, PIKM 2008, pp. 1–8. ACM, New York (2008)Google Scholar
  20. 20.
    Solnon, C.: Alldifferent-based filtering for subgraph isomorphism. Artificial Intelligence 174(12-13), 850–864 (2010)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Vianney le Clément de Saint-Marcq
    • 1
    • 2
  • Yves Deville
    • 1
  • Christine Solnon
    • 2
  1. 1.ICTEAM Research InstituteUniversité catholique de LouvainLouvain-la-NeuveBelgium
  2. 2.LIRIS, CNRS UMR5205Université de Lyon, Université Lyon 1VilleurbanneFrance

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