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Towards Large-Scale Probabilistic OBDA

  • Joerg SchoenfischEmail author
  • Heiner Stuckenschmidt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9310)

Abstract

Ontology-based Data Access has intensively been studied as a very relevant problem in connection with semantic web data. Often it is assumed, that the accessed data behaves like a classical database, i.e. it is known which facts hold for certain. Many Web applications, especially those involving information extraction from text, have to deal with uncertainty about the truth of information. In this paper, we introduce an implementation and a benchmark of such a system on top of relational databases. Furthermore, we propose a novel benchmark for systems handling large probabilistic ontologies. We describe the benchmark design and show its characteristics based on the evaluation of our implementation.

Keywords

Query Processing Description Logic Query Time Conjunctive Query Query Plan 
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.

Notes

Acknowledgement

The authors want to thank Christian Meilicke for his ongoing support and fruitful discussions about the topic of this paper.

References

  1. 1.
    Artale, A., Calvanese, D., Kontchakov, R., Zakharyaschev, M.: The DL-Lite family and relations. J. Artif. Intell. Res. 36, 1–69 (2009)zbMATHMathSciNetGoogle Scholar
  2. 2.
    Lukasiewicz, T., Straccia, U.: Managing uncertainty and vagueness in description logics for the Semantic Web. Web Semant. 6(4), 291–308 (2008)CrossRefGoogle Scholar
  3. 3.
    Riguzzi, F., Bellodi, E., Lamma, E., Zese, R.: BUNDLE: a reasoner for probabilistic ontologies. In: Faber, W., Lembo, D. (eds.) RR 2013. LNCS, vol. 7994, pp. 183–197. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  4. 4.
    Riguzzi, F., Bellodi, E., Lamma, E., Zese, R.: Probabilistic description logics under the distribution semantics. Semant. Web-Interoperability, Usability, Applicability (2014, to appear)Google Scholar
  5. 5.
    Klinov, P., Parsia, B.: Pronto: a practical probabilistic description logic reasoner. In: Bobillo, F., Costa, P.C.G., d’Amato, C., Fanizzi, N., Laskey, K.B., Laskey, K.J., Lukasiewicz, T., Nickles, M., Pool, M. (eds.) URSW 2008-2010/UniDL 2010. LNCS, vol. 7123, pp. 59–79. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  6. 6.
    Niepert, M., Noessner, J., Stuckenschmidt, H.: Log-linear description logics. In: IJCAI International Joint Conference on Artificial Intelligence, pp. 2153–2158 (2011)Google Scholar
  7. 7.
    De Raedt, L., Kimmig, A., Toivonen, H.: ProbLog: a probabilistic prolog and its application in link discovery. In: IJCAI International Joint Conference on Artificial Intelligence, pp. 2468–2473 (2007)Google Scholar
  8. 8.
    Carlson, A., Betteridge, J., Kisiel, B.: Toward an architecture for never-ending language learning. In: Proceedings of the Conference on Artificial Intelligence, pp. 1306–1313 (2010)Google Scholar
  9. 9.
    Jung, J.C., Lutz, C.: Ontology-based access to probabilistic data with \({\sf {OWL QL}}\). In: Cudré-Mauroux, P., Heflin, J., Sirin, E., Tudorache, T., Euzenat, J., Hauswirth, M., Parreira, J.X., Hendler, J., Schreiber, G., Bernstein, A., Blomqvist, E. (eds.) ISWC 2012, Part I. LNCS, vol. 7649, pp. 182–197. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  10. 10.
    Fader, A., Soderland, S., Etzioni, O.: Identifying relations for open information extraction. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1535–1545 (2011)Google Scholar
  11. 11.
    Calvanese, D., Giacomo, G., Lembo, D., Lenzerini, M., Rosati, R.: Tractable reasoning and efficient query answering in description logics: the DL-Lite family. J. Autom. Reason. 39(3), 385–429 (2007)zbMATHCrossRefGoogle Scholar
  12. 12.
    Suciu, D., Olteanu, D., Ré, C., Koch, C.: Probabilistic Databases. Morgan & Claypool Publishers, San Rafael (2011)zbMATHGoogle Scholar
  13. 13.
    Guo, Y., Pan, Z., Heflin, J.: LUBM: a benchmark for OWL knowledge base systems. Web Semant. 3, 158–182 (2005)CrossRefGoogle Scholar
  14. 14.
    Lutz, C., Seylan, I., Toman, D., Wolter, F.: The combined approach to OBDA: taming role hierarchies using filters. In: Alani, H., Kagal, L., Fokoue, A., Biemann, C., Groth, P., et al. (eds.) ISWC 2013. LNCS, vol. 8218, pp. 314–330. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  15. 15.
    Schmidt, M., Hornung, T., Lausen, G., Pinkel, C.: SP2 Bench: a SPARQL performance benchmark. Data Eng. 25, 222–233 (2009)Google Scholar
  16. 16.
    Bizer, C., Schultz, A.: The Berlin SPARQL benchmark. Int. J. Semant. Web Inf. Syst. 5(2), 1–24 (2001)CrossRefGoogle Scholar
  17. 17.
    Bail, S., Alkiviadous, S., Parsia, B., Workman, D., Van Harmelen, M., Goncalves, R.S., Garilao, C.: FishMark: a linked data application benchmark. In: CEUR Workshop Proceedings, vol. 943 (2012)Google Scholar
  18. 18.
    Lakshmanan, L.V.S., Sadri, F.: Probabilistic deductive databases. In: SLP, pp. 254–268, June 1994Google Scholar
  19. 19.
    Klinov, P., Parsia, B.: Optimization and evaluation of reasoning in probabilistic description logic: towards a systematic approach. In: Sheth, A.P., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 213–228. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  20. 20.
    Lanti, D., Rezk, M., Xiao, G., Calvanese, D.: The NPD benchmark : reality check for OBDA systems. In: Proceedings of the 18th International Conference on Extending Database Technology (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Data- and Web Science GroupUniversity of MannheimMannheimGermany

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