Skip to main content

PLINI: A Probabilistic Logic Program Framework for Inconsistent News Information

  • Chapter

Part of the Lecture Notes in Computer Science book series (LNAI,volume 6565)

Abstract

News sources are reliably unreliable. Different news sources may provide significantly differing reports about the same event. Often times, even the same news source may provide widely varying data over a period of time about the same event. Past work on inconsistency management and paraconsistent logics assume that we have “clean” definitions of inconsistency. However, when reasoning about events reported in the news, we need to deal with two unique problems: (i) are two events being reported on the same or are they different? and (ii) what does it mean for two event descriptions to be mutually inconsistent, given that these events are often described using linguistic terms that do not always have a uniquely accepted formal semantics? The answers to these two questions turn out to be closely interlinked. In this paper, we propose a probabilistic logic programming language called PLINI (Probabilistic Logic for Inconsistent News Information) within which users can write rules specifying what they mean by inconsistency in situation (ii) above. We show that PLINI rules can be learned automatically from training data using standard machine learning algorithms. PLINI is a variant of the well known generalized annotated program framework that accounts for similarity of numeric, temporal, and spatial terms occurring in news. We develop a syntax, model theoretic semantics, and fixpoint semantics for PLINI rules, and show how PLINI rules can be used to detect inconsistent news reports.

Keywords

  • Logic Programming
  • Hausdorff Distance
  • News Report
  • Predicate Symbol
  • Equivalence Atom

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.

Some of the authors of this paper were funded in part by AFOSR grant FA95500610405 and ARO grant W911NF0910206.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-642-20832-4_23
  • Chapter length: 30 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   99.00
Price excludes VAT (USA)
  • ISBN: 978-3-642-20832-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   129.00
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Belnap, N.: A useful four valued logic. Modern Uses of Many Valued Logic, 8–37 (1977)

    Google Scholar 

  2. Benferhat, S., Dubois, D., Prade, H.: Some syntactic approaches to the handling of inconsistent knowledge bases: A comparative study part 1: The flat case. Studia Logica 58, 17–45 (1997)

    MathSciNet  CrossRef  MATH  Google Scholar 

  3. Besnard, P., Schaub, T.: Signed systems for paraconsistent reasoning. Journal of Automated Reasoning 20(1-2), 191–213 (1998)

    MathSciNet  CrossRef  MATH  Google Scholar 

  4. Blair, H.A., Subrahmanian, V.S.: Paraconsistent logic programming. Theoretical Computer Science 68(2), 135–154 (1989)

    MathSciNet  CrossRef  MATH  Google Scholar 

  5. da Costa, N.: On the theory of inconsistent formal systems. Notre Dame Journal of Formal Logic 15(4), 497–510 (1974)

    MathSciNet  CrossRef  MATH  Google Scholar 

  6. Fitting, M.: Bilattices and the semantics of logic programming. Journal of Logic Programming 11(2), 91–116 (1991)

    MathSciNet  CrossRef  MATH  Google Scholar 

  7. Flesca, S., Furfaro, F., Parisi, F.: Consistent query answers on numerical databases under aggregate constraints. In: Bierman, G., Koch, C. (eds.) DBPL 2005. LNCS, vol. 3774, pp. 279–294. Springer, Heidelberg (2005)

    CrossRef  Google Scholar 

  8. Flesca, S., Furfaro, F., Parisi, F.: Preferred database repairs under aggregate constraints. In: Prade, H., Subrahmanian, V.S. (eds.) SUM 2007. LNCS (LNAI), vol. 4772, pp. 215–229. Springer, Heidelberg (2007)

    CrossRef  Google Scholar 

  9. Kifer, M., Subrahmanian, V.S.: Theory of generalized annotated logic programming and its applications. Journal of Logic Programming 12(3&4), 335–367 (1992)

    MathSciNet  CrossRef  Google Scholar 

  10. Albanese, M., Subrahmanian, V.S.: T-REX: A domain-independent system for automated cultural information extraction. In: Proceedings of the First International Conference on Computational Cultural Dynamics, pp. 2–8. AAAI Press, Menlo Park (2007)

    Google Scholar 

  11. Cohn, A.G.: A many sorted logic with possibly empty sorts. In: Proceedings of the 11th International Conference on Automated Deduction, pp. 633–647 (1992)

    Google Scholar 

  12. Munkres, J.: Topology: A First Course. Prentice Hall, Englewood Cliffs (1974)

    MATH  Google Scholar 

  13. Ng, R., Subrahmanian, V.S.: Probabilistic logic programming. Information and Computation 101(2), 150–201 (1992)

    MathSciNet  CrossRef  MATH  Google Scholar 

  14. Lloyd, J.: Foundations of Logic Programming. Springer, Heidelberg (1987)

    CrossRef  MATH  Google Scholar 

  15. Ozcan, F., Subrahmanian, V.S.: Partitioning activities for agents. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence, pp. 1218–1228 (2001)

    Google Scholar 

  16. Bansal, N., Blum, A., Chawla, S.: Correlation clustering. Machine Learning 56(1), 89–113 (2004)

    MathSciNet  CrossRef  MATH  Google Scholar 

  17. Demaine, E.D., Immorlica, N.: Correlation clustering with partial information. In: Arora, S., Jansen, K., Rolim, J.D.P., Sahai, A. (eds.) RANDOM 2003 and APPROX 2003. LNCS, vol. 2764, pp. 71–80. Springer, Heidelberg (2003)

    Google Scholar 

  18. Bhattacharya, I., Getoor, L.: Collective entity resolution in relational data. ACM Transactions on Knowledge Discovery from Data (TKDD) 1(1) (2007)

    Google Scholar 

  19. Heckerman, D.: A tutorial on learning with bayesian networks. Proceedings of the NATO Advanced Study Institute on Learning in Graphical Models 89, 301–354 (1998)

    CrossRef  MATH  Google Scholar 

  20. Cristianini, N., Shawe-Taylor, J.: An introduction to support vector machines. Cambridge University Press, Cambridge (2000)

    MATH  Google Scholar 

  21. Ng, A.Y., Jordan, M.I.: On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. Advances in Neural Information Processing Systems 2, 841–848 (2002)

    Google Scholar 

  22. Getoor, L., Diehl, C.P.: Link mining: a survey. ACM SIGKDD Explorations Newsletter 7(2), 3–12 (2005)

    CrossRef  Google Scholar 

  23. Sen, P., Namata, G., Bilgic, M., Getoor, L., Gallagher, B., Eliassi-Rad, T.: Collective classification in network data. AI Magazine 29(3), 93 (2008)

    CrossRef  Google Scholar 

  24. Murphy, K., Weiss, Y., Jordan, M.I.: Loopy belief propagation for approximate inference: An empirical study. In: Foo, N.Y. (ed.) AI 1999. LNCS, vol. 1747, pp. 467–475. Springer, Heidelberg (1999)

    CrossRef  Google Scholar 

  25. Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: Proceedings of the 5th International Conference on Logic Programming, pp. 1070–1080 (1988)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Albanese, M., Broecheler, M., Grant, J., Martinez, M.V., Subrahmanian, V.S. (2011). PLINI: A Probabilistic Logic Program Framework for Inconsistent News Information. In: Balduccini, M., Son, T.C. (eds) Logic Programming, Knowledge Representation, and Nonmonotonic Reasoning. Lecture Notes in Computer Science(), vol 6565. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20832-4_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-20832-4_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20831-7

  • Online ISBN: 978-3-642-20832-4

  • eBook Packages: Computer ScienceComputer Science (R0)