Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Methodologies for Intelligent Systems

ISMIS 1999: Foundations of Intelligent Systems pp 133–143Cite as

Total knowledge and partial knowledge in logical models of information retrieval

  • Communications
  • Conference paper
  • First Online:

  • 98 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1609))

Abstract

We here expand on a previous paper concerning the role of logic in information retrieval (IR) modelling. In that paper, among other things, we had pointed out how different ways of understanding the contribution of logic to IR have sprung from the (always unstated) adherence to either the total or the partial knowledge assumption. Here we make our analysis more precise by relating this dichotomy to the notion of vividness, as used in knowledge representation, and to another dichotomy which has had a profound influence in DB theory, namely the distinction between the proof-theoretic and the model-theoretic views of a database, spelled out by Reiter in his “logical reconstruction of database theory”. We show that precisely the same distinction can be applied to logical models of IR developed so far. The strengths and weaknesses of the adoption of either approach in logical models of IR are discussed.

This is a preview of subscription content, log in via an institution.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. F. Crestani and T. Rölleke. Issues in the implementation of general imaging on top of Probabilistic Datalog. In M. Lalmas, editor, Proceedings of the 1st International Workshop on Logic and Uncertainty in Information Retrieval, Glasgow, UK, 1995.

    Google Scholar 

  2. F. Crestani, F. Sebastiani, and C. J. van Rijsbergen. Imaging and information retrieval: Variations on a theme. In F. Crestani and M. Lalmas, editors, Proceedings of the 2nd International Workshop on Logic and Uncertainty in Information Retrieval, pages 48–49, Glasgow, UK, 1996.

    Google Scholar 

  3. F. Crestani and C. J. van Rijsbergen. Information retrieval by logical imaging. Journal of Documentation, 51:3–17, 1995.

    Article  Google Scholar 

  4. F. Crestani and C. J. van Rijsbergen. A study of probability kinematics in information retrieval. ACM Transactions on Information Systems, 16(3), 1998. Forthcoming.

    Google Scholar 

  5. N. Fuhr. Probabilistic Datalog: a logic for powerful retrieval methods. In Proceedings of SIGIR-95, 18th ACM International Conference on Research and Development in Information Retrieval, pages 282–290, Seattle, US, 1995.

    Google Scholar 

  6. H. J. Levesque. Making believers out of computers. Artificial Intelligence, 30:81–108, 1986.

    Article  MathSciNet  Google Scholar 

  7. H. J. Levesque. Logic and the complexity of reasoning. Journal of Philosophical Logic, 17:355–389, 1988.

    Article  MathSciNet  Google Scholar 

  8. D. K. Lewis. Probabilities of conditionals and conditional probabilities. The Philosophical Review, 85:297–315, 1976.

    Article  Google Scholar 

  9. W. Lukaszewicz. Normonotonic reasoning: formalization of commonsense reasoning. Ellis Horwood, Chichester, UK, 1990.

    Google Scholar 

  10. C. Meghini, F. Sebastiani, U. Straccia, and C. Thanos. A model of information retrieval based on a terminological logic. In Proceedings of SIGIR-93, 16th ACM International Conference on Research and Development in Information Retrieval, pages 298–307, Pittsburgh, US, 1993.

    Google Scholar 

  11. C. Meghini and U. Straccia. A relevance description logic for information retrieval. In Proceedings of SIGIR-96, 19th ACM International Conference on Research and Development in Information Retrieval, pages 197–205, Zürich, CH, 1996.

    Google Scholar 

  12. J.-Y. Nie. An information retrieval model based on modal logic. Information Processing and Management, 25:477–491, 1989.

    Article  Google Scholar 

  13. J.-Y. Nie. Towards a probabilistic modal logic for semantic-based information retrieval. In Proceedings of SIGIR-92, 15th ACM International Conference on Research and Development in Information Retrieval, pages 140–151, Kobenhavn, DK, 1992.

    Google Scholar 

  14. R. Reiter. Towards a logical reconstruction of relational database theory. In M. L. Brodie, J. Mylopoulos, and J. W. Schmidt, editors, On conceptual modelling, pages 191–233. Springer, Heidelberg, DE, 1984.

    Google Scholar 

  15. S. E. Robertson. Theories and models in information retrieval. Journal of Documentation, 33:126–148, 1977.

    Google Scholar 

  16. S. E. Robertson, M. Maron, and W. S. Cooper. Probability of relevance: a unification of two competing models for document retrieval. Information technology: research and development, 1:1–21, 1982.

    Google Scholar 

  17. S. E. Robertson and K. Sparck Jones. Relevance weighting of search terms. Journal of the American Society for Information Science, 27:129–146, 1976.

    Google Scholar 

  18. T. Rölleke. Does Probabilistic Datalog meet the requirements of imaging? In Proceedings of SIGIR-95, 18th ACM International Conference on Research and Development in Information Retrieval, page 374, Seattle, US, 1995.

    Google Scholar 

  19. G. Salton and C. Buckley. Term-weighting approaches in automatic text retrieval. Information Processing and Management, 24:513–523, 1988.

    Article  Google Scholar 

  20. G. Salton, E. A. Fox, and H. Wu. Extended Boolean information retrieval. Communications of the ACM, 26(12):1022–1036, 1983.

    Article  MATH  MathSciNet  Google Scholar 

  21. F. Sebastiani. A probabilistic terminological logic for modelling information retrieval. In Proceedings of SIGIR-94, 17th ACM International Conference on Research and Development in Information Retrieval, pages 122–130, Dublin, IE, 1994.

    Google Scholar 

  22. F. Sebastiani. Information retrieval, imaging and probabilistic logic. Computers and Artificial Intelligence, 17(1):35–50, 1998.

    MATH  MathSciNet  Google Scholar 

  23. F. Sebastiani. On the role of logic in information retrieval. Information Processing and Management, 34(1):1–18, 1998.

    Article  Google Scholar 

  24. R. C. Stalnaker and R. H. Thomason. A semantical analysis of conditional logic. Theoria, 36:23–42, 1970.

    Article  MATH  Google Scholar 

  25. C. J. van Rijsbergen. A non-classical logic for information retrieval. The Computer Journal, 29:481–485, 1986.

    Article  MATH  Google Scholar 

  26. S. M. Wong and Y. Yao. On modeling information retrieval with probabilistic inference. ACM Transactions on Information Systems, 13(1):38–68, 1995.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Istituto di Elaborazione dell’Informazione, Consiglio Nazionale delle Ricerche, Via S. Maria, 46, Pisa, Italy

    Fabrizio Sebastiani

Authors
  1. Fabrizio Sebastiani
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Zbigniew W. Raś Andrzej Skowron

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sebastiani, F. (1999). Total knowledge and partial knowledge in logical models of information retrieval. In: Raś, Z.W., Skowron, A. (eds) Foundations of Intelligent Systems. ISMIS 1999. Lecture Notes in Computer Science, vol 1609. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0095098

Download citation

  • DOI: https://doi.org/10.1007/BFb0095098

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65965-5

  • Online ISBN: 978-3-540-48828-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation