Modelling Probabilistic Inference Networks and Classification in Probabilistic Datalog

  • Miguel Martinez-Alvarez
  • Thomas Roelleke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6379)


Probabilistic Graphical Models (PGM) are a well-established approach for modelling uncertain knowledge and reasoning. Since we focus on inference, this paper explores Probabilistic Inference Networks (PIN’s) which are a special case of PGM. PIN’s, commonly referred as Bayesian Networks, are used in Information Retrieval to model tasks such as classification and ad-hoc retrieval.

Intuitively, a probabilistic logical framework such as Probabilistic Datalog (PDatalog) should provide the expressiveness required to model PIN’s. However, this modelling turned out to be more challenging than expected, requiring to extend the expressiveness of PDatalog. Also, for IR and when modelling more general tasks, it turned out that 1st generation PDatalog has expressiveness and scalability bottlenecks. Therefore, this paper makes a case for 2nd generation PDatalog which supports the modelling of PIN’s. In addition, the paper reports the implementation of a particular PIN application: Bayesian Classifiers to investigate and demonstrate the feasibility of the proposed approach.


Information Retrieval Bayesian Network Probabilistic Inference Document Retrieval Abstraction Layer 
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.


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  1. 1.
    Bekkerman, R., et al.: Automatic categorization of email into folders: Benchmark experiments on enron and sri corpora. Tech. Rep. Center for Intelligent Information Retrieval (2004)Google Scholar
  2. 2.
    Forst, J.F., Tombros, A., Roelleke, T.: Polis: A probabilistic logic for document summarisation. In: Studies in Theory of Information Retrieval, pp. 201–212 (2007)Google Scholar
  3. 3.
    Frommholz, I.: Annotation-based document retrieval with probabilistic logics. In: Kovács, L., Fuhr, N., Meghini, C. (eds.) ECDL 2007. LNCS, vol. 4675, pp. 321–332. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Fuhr, N.: Probabilistic datalog - a logic for powerful retrieval methods. In: ACM SIGIR, pp. 282–290 (1995)Google Scholar
  5. 5.
    Fuhr, N.: Optimum database selection in networked ir. In: NIR 1996, SIGIR (1996)Google Scholar
  6. 6.
    Kheirbeck, A., Chiaramella, Y.: Integrating hypermedia and information retrieval with conceptual graphs formalism. In: Hypertext - Information Retrieval - Multimedia, Synergieeffekte elektronischer Informationssysteme, pp. 47–60 (1995)Google Scholar
  7. 7.
    Klimt, B., Yang, Y.: The Enron corpus: A new dataset for email classification research. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 217–226. Springer, Heidelberg (2004)Google Scholar
  8. 8.
    McCallum, A., Nigam, K.: A comparison of event models for naive bayes text classification. In: AAAI/ICML-1998 Workshop on Learning for Text Categorization, p. 41 (1998)Google Scholar
  9. 9.
    Meghini, C., Sebastiani, F., Straccia, U., Thanos, C.: A model of information retrieval based on a terminological logic. In: ACM SIGIR, pp. 298–308 (1993)Google Scholar
  10. 10.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufman, San Mateo (1988)Google Scholar
  11. 11.
    Polleres, A.: From SPARQL to rules (and back). In: 16th international conference on World Wide Web (WWW), pp. 787–796. ACM, New York (2007)CrossRefGoogle Scholar
  12. 12.
    Roelleke, T., Fuhr, N.: Information retrieval with probabilistic datalog. In: Uncertainty and Logics - Advanced Models for the Representation and Retrieval of Information (1998)Google Scholar
  13. 13.
    Roelleke, T., Wu, H., Wang, J., Azzam, H.: Modelling retrieval models in a probabilistic relational algebra with a new operator: The relational Bayes. VLDB Journal (2009)Google Scholar
  14. 14.
    Schenk, S.: A SPARQL semantics based on Datalog. In: Hertzberg, J., Beetz, M., Englert, R. (eds.) KI 2007. LNCS (LNAI), vol. 4667, pp. 160–174. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  15. 15.
    Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (2002)CrossRefGoogle Scholar
  16. 16.
    Turtle, H., Croft, W.: Efficient probabilistic inference for text retrieval. In: Proceedings RIAO 1991, pp. 644–661 (1991)Google Scholar
  17. 17.
    Turtle, H., Croft, W.B.: Inference networks for document retrieval. In: ACM SIGIR, New York, pp. 1–24 (1990)Google Scholar
  18. 18.
    van Rijsbergen, C.J.: Towards an information logic. In: ACM SIGIR, pp. 77–86 (1989)Google Scholar
  19. 19.
    Wong, S., Yao, Y.: On modeling information retrieval with probabilistic inference. ACM Transactions on Information Systems 13(1), 38–68 (1995)CrossRefGoogle Scholar
  20. 20.
    Wu, H., Kazai, G., Roelleke, T.: Modelling anchor text retrieval in book search based on back-of-book index. In: SIGIR Workshop on Focused Retrieval, pp. 51–58 (2008)Google Scholar
  21. 21.
    Yang, Y.: A study on thresholding strategies for text categorization. In: ACM SIGIR, pp. 137–145 (2001) (press)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Miguel Martinez-Alvarez
    • 1
  • Thomas Roelleke
    • 1
  1. 1.Queen Mary, University of London 

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