OBDA Constraints for Effective Query Answering

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9718)


In Ontology Based Data Access (OBDA) users pose SPARQL queries over an ontology that lies on top of relational datasources. These queries are translated on-the-fly into SQL queries by OBDA systems. Standard SPARQL-to-SQL translation techniques in OBDA often produce SQL queries containing redundant joins and unions, even after a number of semantic and structural optimizations. These redundancies are detrimental to the performance of query answering, especially in complex industrial OBDA scenarios with large enterprise databases. To address this issue, we introduce two novel notions of OBDA constraints and show how to exploit them for efficient query answering. We conduct an extensive set of experiments on large datasets using real world data and queries, showing that these techniques strongly improve the performance of query answering up to orders of magnitude.


Database Schema Conjunctive Query SPARQL Query Triple Pattern Query Answering 
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.



This work is partially supported by the EU under IP project Optique (Scalable End-user Access to Big Data), grant agreement n. FP7-318338.


  1. 1.
    Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P.F. (eds.): The Description Logic Handbook: Theory, Implementation and Applications, 2nd edn. Cambridge University Press, Cambridge (2007)MATHGoogle Scholar
  2. 2.
    Beeri, C., Vardi, M.Y.: The implication problem for data dependencies. In: Even, S., Kariv, O. (eds.) ICALP. LNCS, vol. 115, pp. 73–85. Springer, Heidelberg (1981)Google Scholar
  3. 3.
    Bienvenu, M., Ortiz, M., Simkus, M., Xiao, G.: Tractable queries for lightweight description logics. In: Proceedings of IJCAI. IJCAI/AAAI (2013)Google Scholar
  4. 4.
    Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., Rosati, R.: Tractable reasoning and efficient query answering in description logics: the DL-Lite family. JAR 39(3), 385–429 (2007)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Chakravarthy, U.S., Fishman, D.H., Minker, J.: Semantic query optimization in expert systems and database systems. In: Proceedings of DEXA, pp. 659–674 (1986)Google Scholar
  6. 6.
    Das, S., Sundara, S., Cyganiak, R.: R2RML: RDB to RDF mapping language. W3C Recommendation, W3C, September 2012. http://www.w3.org/TR/r2rml/
  7. 7.
    DeWitt, D.J.: The wisconsin benchmark: past, present, and future. In: Gray, J. (ed.) The Benchmark Handbook. Morgan Kaufmann (1993)Google Scholar
  8. 8.
    Di Pinto, F., Lembo, D., Lenzerini, M., Mancini, R., Poggi, A., Rosati, R., Ruzzi, M., Savo, D.F.: Optimizing query rewriting in ontology-based data access. In: Proceedings of EDBT, pp. 561–572. ACM Press (2013)Google Scholar
  9. 9.
    Glimm, B., Ogbuji, C.: SPARQL 1.1 entailment regimes. W3C Recommendation, W3C, March 2013. http://www.w3.org/TR/sparql11-entailment/
  10. 10.
    Gottlob, G., Kikot, S., Kontchakov, R., Podolskii, V.V., Schwentick, T., Zakharyaschev, M.: The price of query rewriting in ontology-based data access. AIJ 213, 42–59 (2014)MathSciNetMATHGoogle Scholar
  11. 11.
    He, B., Zou, L., Zhao, D.: Using conditional functional dependency to discover abnormal data in RDF graphs. In: Proceedings of SWIM, pp. 43: 1–43: 7. ACM (2014)Google Scholar
  12. 12.
    Hovland, D., Lanti, D., Rezk, M., Xiao, G.: OBDA constraints for effective query answering (extended version). CoRR Technical report abs/1605.04263, arXiv.org e-Print archive (2016). http://arxiv.org/abs/1605.04263
  13. 13.
    Kikot, S., Kontchakov, R., Podolskii, V., Zakharyaschev, M.: Exponential lower bounds and separation for query rewriting. In: Czumaj, A., Mehlhorn, K., Pitts, A., Wattenhofer, R. (eds.) ICALP 2012, Part II. LNCS, vol. 7392, pp. 263–274. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  14. 14.
    Kikot, S., Kontchakov, R., Zakharyaschev, M.: Conjunctive query answering with OWL 2 QL. In: Proceedings of KR, pp. 275–285 (2012)Google Scholar
  15. 15.
    Kontchakov, R., Rezk, M., Rodríguez-Muro, M., Xiao, G., Zakharyaschev, M.: Answering SPARQL queries over databases under OWL 2 QL entailment regime. In: Mika, P., Tudorache, T., Bernstein, A., Welty, C., Knoblock, C., Vrandečić, D., Groth, P., Noy, N., Janowicz, K., Goble, C. (eds.) ISWC 2014, Part I. LNCS, vol. 8796, pp. 552–567. Springer, Heidelberg (2014)Google Scholar
  16. 16.
    Lanti, D., Rezk, M., Xiao, G., Calvanese, D.: The NPD benchmark: reality check for OBDA systems. In: Proceedings of EDBT (2015)Google Scholar
  17. 17.
    Mora, J., Rosati, R., Corcho, O.: kyrie2: query rewriting under extensional constraints in ELHIO. In: Proceedings of ISWC, pp. 568–583 (2014)Google Scholar
  18. 18.
    Poggi, A., Lembo, D., Calvanese, D., De Giacomo, G., Lenzerini, M., Rosati, R.: Linking data to ontologies. In: Spaccapietra, S. (ed.) Journal on Data Semantics X. LNCS, vol. 4900, pp. 133–173. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  19. 19.
    Rodríguez-Muro, M., Kontchakov, R., Zakharyaschev, M.: Ontology-based data access: Ontop of databases. In: Alani, H., Kagal, L., Fokoue, A., Groth, P., Biemann, C., Parreira, J.X., Aroyo, L., Noy, N., Welty, C., Janowicz, K. (eds.) ISWC 2013, Part I. LNCS, vol. 8218, pp. 558–573. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  20. 20.
    Rodriguez-Muro, M., Rezk, M.: Efficient SPARQL-to-SQL with R2RML mappings. J. Web Semant. 33, 141–169 (2015)CrossRefGoogle Scholar
  21. 21.
    Rosati, R.: Prexto: query rewriting under extensional constraints in DL-Lite. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 360–374. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  22. 22.
    Rosati, R., Almatelli, A.: Improving query answering over DL-Lite ontologies. In: Proceedings of KR, pp. 290–300 (2010)Google Scholar
  23. 23.
    Weddell, G.E.: Reasoning about functional dependencies generalized for semantic data models. ACM Trans. Database Syst. 17(1), 32–64 (1992)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Yu, Y., Heflin, J.: Extending functional dependency to detect abnormal data in RDF graphs. 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. 794–809. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.University of OsloOsloNorway
  2. 2.Free University of Bozen-BolzanoBolzanoItaly

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