Trust-Based Probabilistic Query Answering

  • Achille Fokoue
  • Mudhakar Srivatsa
  • Robert Young
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6997)

Abstract

On the semantic web, information from several sources (with disparate trust levels) may be fused with the goal of answering complex queries from end users. In such context, it is critical to aggregate information from heterogeneous sources and support trust-based query answering over the integrated knowledge base. In this paper, we describe a scalable probabilistic query answering over complex, uncertain and partially consistent knowledge bases. The key contributions in this paper are fourfold. First, our approach provides an intuitive query answering semantics over a probabilistic knowledge base. Second, our approach tolerates inconsistencies in knowledge bases. Third, we propose and evaluate a scalable error-bounded approximation query answering over a large knowledge base. Finally, we empirically show the value of taking into account trust information in the query answering process.

Keywords

Bayesian Network Description Logic Query Answering Primitive Event Trust Inference 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P.: The Description Logic Handbook. Cambridge University Press, Cambridge (2003)MATHGoogle Scholar
  2. 2.
    Cheng, J., Druzdzel, M.J.: AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential Reasoning in Large Bayesian Networks. Journal of AI Research (2000)Google Scholar
  3. 3.
    D’Amato, C., Fanizzi, N., Lukasiewicz, T.: Tractable reasoning with bayesian description logics. In: Greco, S., Lukasiewicz, T. (eds.) SUM 2008. LNCS (LNAI), vol. 5291, pp. 146–159. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Dolby, J., Fokoue, A., Kalyanpur, A., Kershenbaum, A., Schonberg, E., Srinivas, K., Ma, L.: Scalable semantic retrieval through summarization and refinement. In: AAAI, pp. 299–304 (2007)Google Scholar
  5. 5.
    Fikes, R., Ferrucci, D., Thurman, D.: Knowledge associates for novel intelligence (kani) (2005), https://analysis.mitre.org/proceedings/Final_Papers_Files/174_Camera_Ready_Paper.pdf
  6. 6.
    Fokoue, A., Srivatsa, M., Rohatgi, P., Wrobel, P., Yesberg, J.: A decision support system for secure information sharing. In: ACM SACMAT, pp. 105–114 (2009)Google Scholar
  7. 7.
    Fokoue, A., Srivatsa, M., Young, R.: Assessing trust in uncertain information. 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. 209–224. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Fokoue, A., Srivatsa, M., Young, R.: Trust Inference and Query Answering over Uncertain Information. Technical Report RC25157, IBM Research (2011)Google Scholar
  9. 9.
    Guo, Y., Heflin, J.: An Initial Investigation into Querying an Untrustworthy and Inconsistent Web. In: Workshop on Trust, Security and Reputation on the Semantic Web (2004)Google Scholar
  10. 10.
    Heinsohn, J.: Probabilistic description logics. In: UAI 1994 (1994)Google Scholar
  11. 11.
    Huang, H., Liu, C.: Query evaluation on probabilistic RDF databases. In: Vossen, G., Long, D.D.E., Yu, J.X. (eds.) WISE 2009. LNCS, vol. 5802, pp. 307–320. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  12. 12.
    Jaeger, M.: Probabilistic reasoning in terminological logics. In: KR 1994 (1994)Google Scholar
  13. 13.
    Kamvar, S., Schlosser, M., Garcia-Molina, H.: EigenTrust: Reputation management in P2P networks. In: WWW Conference (2003)Google Scholar
  14. 14.
    Klinov, P., Parsia, B.: On improving the scalability of checking satisfiability in probabilistic description logics. In: Godo, L., Pugliese, A. (eds.) SUM 2009. LNCS, vol. 5785, pp. 138–149. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  15. 15.
    Koller, D., Levy, A., Pfeffer, A.: P-classic: A tractable probabilistic description logic. In: AAAI 1997, pp. 390–397 (1997)Google Scholar
  16. 16.
    Kuter, U., Golbeck, J.: SUNNY: A New Algorithm for Trust Inference in Social Networks, using Probabilistic Confidence Models. In: AAAI 2007 (2007)Google Scholar
  17. 17.
    Lukasiewicz, T.: Expressive probabilistic description logics. Artif. Intell. 172(6-7), 852–883 (2008)MathSciNetCrossRefMATHGoogle Scholar
  18. 18.
    Ma, L., Yang, Y., Qiu, Z., Xie, G., Pan, Y.: Towards a complete OWL ontology benchmark. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS, vol. 4011, pp. 124–139. Springer, Heidelberg (2006)Google Scholar
  19. 19.
    Netflix. Netflix Prize, http://www.netflixprize.com/
  20. 20.
    Patel, C., Cimino, J.J., Dolby, J., Fokoue, A., Kalyanpur, A., Kershenbaum, A., Ma, L., Schonberg, E., Srinivas, K.: Matching patient records to clinical trials using ontologies. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 816–829. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  21. 21.
    Schafer, J.B., Konstan, J., Riedl, J.: Recommender Systems in E-Commerce. In: ACM Conference on Electronic Commerce (1999)Google Scholar
  22. 22.
    Udrea, O., Subrahmanian, V.S., Majkic, Z.: Probabilistic rdf. In: IRI (2006)Google Scholar
  23. 23.
    Xiong, L., Liu, L.: Supporting reputation based trust in peer-to-peer communities. IEEE TKDE 71, 16(7) (July 2004)Google Scholar
  24. 24.
    Yelland, P.M.: An alternative combination of bayesian networks and description logics. In: KR, pp. 225–234 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Achille Fokoue
    • 1
  • Mudhakar Srivatsa
    • 1
  • Robert Young
    • 2
  1. 1.IBM T.J. Watson Research CenterUSA
  2. 2.Defence Science and Technology LabsUK

Personalised recommendations