Advertisement

RDF Query Relaxation Strategies Based on Failure Causes

  • Géraud Fokou
  • Stéphane JeanEmail author
  • Allel Hadjali
  • Mickaël Baron
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9678)

Abstract

Recent advances in Web-information extraction have led to the creation of several large Knowledge Bases (KBs). Querying these KBs often results in empty answers that do not serve the users’ needs. Relaxation of the failing queries is one of the cooperative techniques used to retrieve alternative results. Most of the previous work on RDF query relaxation compute a set of relaxed queries and execute them in a similarity-based ranking order. Thus, these approaches relax an RDF query without knowing its failure causes (FCs). In this paper, we study the idea of identifying these FCs to speed up the query relaxation process. We propose three relaxation strategies based on various information levels about the FCs of the user query and of its relaxed queries as well. A set of experiments conducted on the LUBM benchmark show the impact of our proposal in comparison with a state-of-the-art algorithm.

Keywords

Execution Time SPARQL Query Relaxation Strategy Triple Pattern Initial 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.

References

  1. 1.
    Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N., Hellmann, S., Morsey, M., van Kleef, P., Auer, S., Bizer, C.: DBpedia - a large-scale, multilingual knowledge base extracted from wikipedia. Semant. Web 6(2), 167–195 (2015)Google Scholar
  2. 2.
    Dong, X., Gabrilovich, E., Heitz, G., Horn, W., Lao, N., Murphy, K., Strohmann, T., Sun, S., Zhang, W.: Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: ACM SIGKDD, pp. 601–610 (2014)Google Scholar
  3. 3.
    Saleem, M., Ali, M.I., Hogan, A., Mehmood, Q., Ngomo, A.-C.N.: LSQ: the linked SPARQL queries dataset. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9367, pp. 261–269. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-25010-6_15 CrossRefGoogle Scholar
  4. 4.
    Hurtado, C.A., Poulovassilis, A., Wood, P.T.: Query relaxation in RDF. In: Spaccapietra, S. (ed.) Journal on Data Semantics X. LNCS, vol. 4900, pp. 31–61. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Huang, H., Liu, C., Zhou, X.: Approximating query answering on RDF databases. J. World Wide Web 15(1), 89–114 (2012)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Fokou, G., Jean, S., Hadjali, A.: Endowing semantic query languages with advanced relaxation capabilities. In: Andreasen, T., Christiansen, H., Cubero, J.-C., Raś, Z.W. (eds.) ISMIS 2014. LNCS, vol. 8502, pp. 512–517. Springer, Heidelberg (2014)Google Scholar
  7. 7.
    Calì, A., Frosini, R., Poulovassilis, A., Wood, P.T.: Flexible querying for SPARQL. In: Meersman, R., Panetto, H., Dillon, T., Missikoff, M., Liu, L., Pastor, O., Cuzzocrea, A., Sellis, T. (eds.) OTM 2014. LNCS, vol. 8841, pp. 473–490. Springer, Heidelberg (2014)Google Scholar
  8. 8.
    Dolog, P., Stuckenschmidt, H., Wache, H., Diederich, J.: Relaxing RDF queries based on user and domain preferences. IJIIS 33(3), 239–260 (2009)Google Scholar
  9. 9.
    Elbassuoni, S., Ramanath, M., Weikum, G.: Query relaxation for entity-relationship search. 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. 62–76. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    Hogan, A., Mellotte, M., Powell, G., Stampouli, D.: Towards fuzzy query-relaxation for RDF. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 687–702. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  11. 11.
    Fokou, G., Jean, S., Hadjali, A., Baron, M.: Cooperative techniques for SPARQL query relaxation in RDF databases. In: Gandon, F., Sabou, M., Sack, H., d’Amato, C., Cudré-Mauroux, P., Zimmermann, A. (eds.) ESWC 2015. LNCS, vol. 9088, pp. 237–252. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  12. 12.
    Godfrey, P.: Minimization in cooperative response to failing database queries. Int. J. Coop. Inf. Syst. 6(2), 95–149 (1997)CrossRefGoogle Scholar
  13. 13.
    Pérez, J., Arenas, M., Gutierrez, C.: Semantics and complexity of SPARQL. ACM Trans. Database Syst. 34(3), 16:1–16:45 (2009)CrossRefGoogle Scholar
  14. 14.
    Bizer, C., Schultz, A.: The Berlin SPARQL benchmark. Semant. Web Inf. Syst. 5(2), 1–24 (2009)CrossRefGoogle Scholar
  15. 15.
    Campinas, S.: Live SPARQL auto-completion. In: ISWC 2014 (Posters & Demos), pp. 477–480 (2014)Google Scholar
  16. 16.
    Bosc, P., Hadjali, A., Pivert, O.: Incremental controlled relaxation of failing flexible queries. JIIS 33(3), 261–283 (2009)Google Scholar
  17. 17.
    Jannach, D.: Fast computation of query relaxations for knowledge-based recommenders. AI Commun. 22(4), 235–248 (2009)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Pivert, O., Smits, G., Hadjali, A., Jaudoin, H.: Efficient detection of minimal failing subqueries in a fuzzy querying context. In: Eder, J., Bielikova, M., Tjoa, A.M. (eds.) ADBIS 2011. LNCS, vol. 6909, pp. 243–256. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Géraud Fokou
    • 1
  • Stéphane Jean
    • 1
    Email author
  • Allel Hadjali
    • 1
  • Mickaël Baron
    • 1
  1. 1.LIAS/ISAE-ENSMAUniversity of PoitiersFuturoscope CedexFrance

Personalised recommendations