Skip to main content

Combining CORI and the Decision-Theoretic Approach for Advanced Resource Selection

  • Conference paper
Advances in Information Retrieval (ECIR 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2997))

Included in the following conference series:

Abstract

In this paper we combine two existing resource selection approaches, CORI and the decision-theoretic framework (DTF). The state-of-the-art system CORI belongs to the large group of heuristic resource ranking methods which select a fixed number of libraries with respect to their similarity to the query. In contrast, DTF computes an optimum resource selection with respect to overall costs (from different sources, e.g. retrieval quality, time, money). In this paper, we improve CORI by integrating it with DTF: The number of relevant documents is approximated by applying a linear or a logistic function on the CORI library scores. Based on this value, one of the existing DTF variants (employing a recall-precision function) estimates the number of relevant documents in the result set. Our evaluation shows that precision in the top ranks of this technique is higher than for the existing resource selection methods for long queries and lower for short queries; on average the combined approach outperforms CORI and the other DTF variants.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Callan, J., Connell, M.: Query-based sampling of text databases. ACM Transactions on Information Systems 19(2), 97–130 (2001)

    Article  Google Scholar 

  2. Callan, J., Cormack, G., Clarke, C., Hawking, D., Smeaton, A. (eds.): Proceedings of the 26st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, New York (2003)

    Google Scholar 

  3. Callan, J., Lu, Z., Croft, W.: Searching distributed collections with inference networks. In: Fox, E., Ingwersen, P., Fidel, R. (eds.) Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 21–29. ACM, New York (1995) ISBN 0-89791-714-6

    Chapter  Google Scholar 

  4. Callan, J., Powell, A.L., French, J.C., Connell, M.: The effects of query-based sampling on automatic database selection algorithms. ACM Transactions on Information Systems (submitted for publication)

    Google Scholar 

  5. French, J., Powell, A., Callan, J., Viles, C., Emmitt, T., Prey, K., Mou, Y.: Comparing the performance of database selection algorithms. In: Proceedings of the 22nd International Conference on Research and Development in Information Retrieval, pp. 238–245. ACM, New York (1999)

    Google Scholar 

  6. Fuhr, N.: A decision-theoretic approach to database selection in networked IR. ACM Transactions on Information Systems 17(3), 229–249 (1999)

    Article  Google Scholar 

  7. Gravano, L., Garcia-Molina, H.: Generalizing GIOSS to vector-space databases and broker hierarchies. In: Dayal, U., Gray, P., Nishio, S. (eds.) VLDB 1995, Proceedings of 21th International Conference on Very Large Data Bases, Los Altos, California, pp. 78–89. Morgan Kaufman, San Francisco (1995)

    Google Scholar 

  8. Harman, D. (ed.): The Second Text REtrieval Conference (TREC-2), Gaithersburg, Md. 20899, National Institute of Standards and Technology (1994)

    Google Scholar 

  9. Nottelmann, H., Fuhr, N.: Evaluating different methods of estimating retrieval quality for resource selection. In: Callan, et al. [2]

    Google Scholar 

  10. Nottelmann, H., Fuhr, N.: From uncertain inference to probability of relevance for advanced IR applications. In: Sebastiani, F. (ed.) ECIR 2003. LNCS, vol. 2633, pp. 235–250. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  11. Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P. (eds.): Nested Relations and Complex Objects in Databases. Cambridge University Press, Cambridge (1992)

    Google Scholar 

  12. Robertson, S.E., Walker, S., Hancock-Beaulieu, M., Gull, A., Lau, M.: Okapi at TREC. In: Text REtrieval Conference, pp. 21–30 (1992)

    Google Scholar 

  13. Si, L., Callan, J.: Relevant document distribution estimation method for resource selection. In: Callan, et al. [2]

    Google Scholar 

  14. Si, L., Jin, R., Callan, J., Ogilvie, P.: Language model framework for resource selection and results merging. In: Grossman, D. (ed.) Proceedings of the 11th International Conference on Information and Knowledge Management, ACM, New York (2002), http://www-2.cs.cmu.edu/callan/Papers/cikm02-lsi.pdf

    Google Scholar 

  15. van Rijsbergen, C.J.: A non-classical logic for information retrieval. The Computer Journal 29(6), 481–485 (1986)

    Article  MATH  Google Scholar 

  16. van Rijsbergen, C.J.: Probabilistic retrieval revisited. The Computer Journal 35(3), 291–298 (1992)

    Article  MATH  Google Scholar 

  17. Wong, S., Yao, Y.: 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

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Nottelmann, H., Fuhr, N. (2004). Combining CORI and the Decision-Theoretic Approach for Advanced Resource Selection. In: McDonald, S., Tait, J. (eds) Advances in Information Retrieval. ECIR 2004. Lecture Notes in Computer Science, vol 2997. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24752-4_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-24752-4_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21382-6

  • Online ISBN: 978-3-540-24752-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics