Article

Belief revision for adaptive information retrieval

Authors:

Raymond Y.K. Lau,

Peter D. Bruza,

Dawei SongAuthors Info & Claims

SIGIR '04: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 130 - 137

https://doi.org/10.1145/1008992.1009017

Published: 25 July 2004 Publication History

Abstract

Applying Belief Revision logic to model adaptive information retrieval is appealing since it provides a rigorous theoretical foundation to model partiality and uncertainty inherent in any information retrieval (IR) processes. In particular, a retrieval context can be formalised as a belief set and the formalised context is used to disambiguate vague user queries. Belief revision logic also provides a robust computational mechanism to revise an IR system's beliefs about the users' changing information needs. In addition, information flow is proposed as a text mining method to automatically acquire the initial IR contexts. The advantage of a belief-based IRsystem is that its IR behaviour is more predictable and explanatory. However, computational efficiency is often a concern when the belief revision formalisms are applied to large real-life applications. This paper describes our belief-based adaptive IR system which is underpinned by an efficient belief revision mechanism. Our initial experiments show that the belief-based symbolic IR model is more effective than a classical quantitative IR model. To our best knowledge, this is the first successful empirical evaluation of a logic-based IR model based on large IR benchmark collections.

References

[1]

R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In Jorge B. Bocca, Matthias Jarke, and Carlo Zaniolo, editors, VLDB'94, Proceedings of 20th International Conference on Very Large Data Bases, pages 487--499, Santiago de Chile, Chile, September 12--15 1994. Morgan Kaufmann Publishers.

Digital Library

[2]

C.E. Alchourrón, P. Gärdenfors, and D. Makinson. On the logic of theory change: partial meet contraction and revision functions. Journal of Symbolic Logic, 50:510--530, 1985.

[3]

P. Bruza, D. Song, and K. Wong. Aboutness from a commonsense perspective. Journal of the American Society for Information Science, 51(12):1090--1105, 2000.

Digital Library

[4]

P.D. Bruza and T.W.C. Huibers. Investigating Aboutness Axioms Using Information Fields. In W.B. Croft and C.J. van Rijsbergen, editors, Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 112--121, Dublin, Ireland, July 1994. ACM Press.

Digital Library

[5]

S. Card, T. Moran, and A. Newell. The Psychology of Human Computer Interaction. Lawrence Erlbaum, Hillsdale, NJ, 1983.

Digital Library

[6]

W. Cohen and Y. Singer. Context-sensitive learning methods for text categorization. In Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 307--315. ACM Press, 1996.

Digital Library

[7]

Fabio Crestani. Logical Imaging and Probabilistic Information Retrieval. In M. Lalmas F. Crestani and C.J. van Rijsbergen, editors, Information Retrieval: uncertainty and logics, pages 247--279. Kluwer Academic Publishers, Norwell, Massachusetts, 1998.

[8]

D. Dubois and H. Prade. Epistemic entrenchment and possibilistic logic. Artificial Intelligence, 50(2):223--239, 1991.

Digital Library

[9]

P. Gärdenfors. Knowledge in flux: modeling the dynamics of epistemic states. The MIT Press, Cambridge, Massachusetts, 1988.

[10]

P. Gärdenfors and D. Makinson. Revisions of knowledge systems using epistemic entrenchment. In Moshe Y. Vardi, editor, Proceedings of the Second Conference on Theoretical Aspects of Reasoning About Knowledge, pages 83--95, Pacific Grove, California, March 1988. Morgan Kaufmann.

Digital Library

[11]

P. Gärdenfors and D. Makinson. Nonmonotonic inference based on expectations. Artificial Intelligence, 65(2):197--245, 1994.

Digital Library

[12]

D. Hull. The TREC-7 Filtering Track: Description and Analysis. In E.M. Voorhees and D.K. Harman, editors, Proceedings of the seventh Text REtrieval Conference (TREC-7), pages 33--56, Gaithersburg, Maryland, November 9--11 1998. NIST. Available from http://trec.nist.gov/pubs/trec7/.

[13]

T. Kindo, H. Yoshida, T. Morimoto, and T. Watanabe. Adaptive personal information filtering system that organizes personal profiles automatically. In Martha E. Pollack, editor, Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, pages 716--721, Nagoya, Japan, August 23--29, 1997. Morgan Kaufmann publishers.

[14]

D. Koller and M. Sahami. Hierarchically classifying documents using very few words. In Douglas H. Fisher, editor, Proceedings of ICML-97, 14th International Conference on Machine Learning, pages 170--178, Nashville, Tennessee, 1997. Morgan Kaufmann Publishers, San Francisco, California.

Digital Library

[15]

M. Lalmas and P.D. Bruza. The Use of Logic in Information Retrieval Modeling. Knowledge Engineering Review, 13(3):263--295, 1998.

[16]

J. Lang. Possibilistic Logic: Algorithms and Complexity. In J. Kohlas and S. Moral, editors, Handbook of Algorithms for Uncertainty and Defeasible Reasoning. Kluwer Academic Publishers, Norwell, Massachusetts, 1997.

[17]

B. Logan, S. Reece, and K. Sprack-Jones. Modelling information retrieval agents with belief revision. In W. Bruce Croft and C. J. van Rijsbergen, editors, Proceedings of the Seventeenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 91--100, Dublin, Ireland, 1994.

Digital Library

[18]

D. Losada and A. Barreiro. Using a Belief Revision Operator for Document Ranking in Extended Boolean Models. In Fredric Gey, Marti Hearst, and Richard Tong, editors, Proceedings of the 22nd Annual ACM Conference of Research and Development in Information Retrieval (SIGIR'99), pages 66--72, Berkeley, California, 1999. ACM Press.

Digital Library

[19]

J. Nie. An Outline of a General Model for Information Retrieval Systems. In Proceedings of the 9th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 495--506. ACM Press, 1986.

Digital Library

[20]

J.Y. Nie, M. Brisebois, and F. Lepage. Information retrieval as counterfactual. The Computer Journal, 38(8):643--657, 1995.

[21]

J. Rocchio. Relevance Feedback in Information Retrieval. In G. Salton, editor, The SMART retrieval system:experiments in automatic document processing, pages 313--323. Prentice-Hall, Englewood Cliffs, New Jersey, 1971.

[22]

G. Salton. Developments in automatic text retrieval. Science, 253:974--980, August 1991.

[23]

G. Salton and M.J. McGill. Introduction to Modern Information Retrieval. McGraw-Hill, New York, New York, 1983.

Digital Library

[24]

D. Song and P. Bruza. Towards a theory of context sensitive informational inference. Journal of the American Society for Information Science and Technology, 54(4):326--339, 2003.

Digital Library

[25]

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

[26]

M.-A. Williams. Iterated theory base change: A computational model. In Chris S. Mellish, editor, Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, pages 1541--1547, Montréal, Canada, August 20--25, 1995. Morgan Kaufmann Publishers.

Digital Library

[27]

M.-A. Williams. Anytime belief revision. In Martha E. Pollack, editor, Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, pages 74--79, Nagoya, Japan, August 23--29, 1997. Morgan Kaufmann Publishers.

Digital Library

[28]

K. Wong, D. Song, P. Bruza, and C. Cheng. Application of aboutness to functional benchmarking in information retrieval. ACM Transactions on Information Systems, 19(4):337--370, October 2001.

Digital Library

Cited By

Gupta VSharma DDixit A(2022)Comparative Analysis of Term Extraction and Selection Techniques for Query Reformulation Using PRFEmergent Converging Technologies and Biomedical Systems10.1007/978-981-16-8774-7_43(515-526)Online publication date: 24-Mar-2022
https://doi.org/10.1007/978-981-16-8774-7_43
El Zein D(2022)Cognitive Information RetrievalAdvances in Information Retrieval10.1007/978-3-030-99739-7_58(473-479)Online publication date: 5-Apr-2022
https://doi.org/10.1007/978-3-030-99739-7_58
El Zein Dda Costa Pereira C(2021)A Cognitive Agent Framework in Information Retrieval: Using User Beliefs to Customize ResultsPRIMA 2020: Principles and Practice of Multi-Agent Systems10.1007/978-3-030-69322-0_21(325-333)Online publication date: 14-Feb-2021
https://doi.org/10.1007/978-3-030-69322-0_21
Show More Cited By

Index Terms

Belief revision for adaptive information retrieval
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

Recommendations

Towards a belief-revision-based adaptive and context-sensitive information retrieval system

In an adaptive information retrieval (IR) setting, the information seekers' beliefs about which terms are relevant or nonrelevant will naturally fluctuate. This article investigates how the theory of belief revision can be used to model adaptive IR. ...
Revocable Belief Revision

Krister Segerberg proposed irrevocable belief revision, to be contrasted with standard belief revision, in a setting wherein belief of propositional formulas is modelled explicitly. This suggests that in standard belief revision is revocable: one should ...
General Belief Revision

In artificial intelligence, a key question concerns how an agent may rationally revise its beliefs in light of new information. The standard (AGM) approach to belief revision assumes that the underlying logic contains classical propositional logic. This ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGIR '04: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval

July 2004

624 pages

ISBN:1581138814

DOI:10.1145/1008992

General Chair:
Mark Sanderson
University of Sheffield (UK)
,
Program Chairs:
Kalervo Järvelin
University of Tampere (Finland)
,
James Allan
University of Massachusetts (USA)
,
Peter Bruza
Distributed Systems Technology Centre (Australia)

Copyright © 2004 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 July 2004

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

SIGIR04

Sponsor:

SIGIR04: The 27th ACM/SIGIR International Symposium on Information Retrieval 2004

July 25 - 29, 2004

Sheffield, United Kingdom

Acceptance Rates

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

20
Total Citations
View Citations
921
Total Downloads

Downloads (Last 12 months)6
Downloads (Last 6 weeks)1

Reflects downloads up to 17 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Gupta VSharma DDixit A(2022)Comparative Analysis of Term Extraction and Selection Techniques for Query Reformulation Using PRFEmergent Converging Technologies and Biomedical Systems10.1007/978-981-16-8774-7_43(515-526)Online publication date: 24-Mar-2022
https://doi.org/10.1007/978-981-16-8774-7_43
El Zein D(2022)Cognitive Information RetrievalAdvances in Information Retrieval10.1007/978-3-030-99739-7_58(473-479)Online publication date: 5-Apr-2022
https://doi.org/10.1007/978-3-030-99739-7_58
El Zein Dda Costa Pereira C(2021)A Cognitive Agent Framework in Information Retrieval: Using User Beliefs to Customize ResultsPRIMA 2020: Principles and Practice of Multi-Agent Systems10.1007/978-3-030-69322-0_21(325-333)Online publication date: 14-Feb-2021
https://doi.org/10.1007/978-3-030-69322-0_21
White RHorvitz E(2015)Belief Dynamics and Biases in Web SearchACM Transactions on Information Systems10.1145/274622933:4(1-46)Online publication date: 4-May-2015
https://dl.acm.org/doi/10.1145/2746229
Albathan MLi YXu Y(2014)Using Extended Random Set to Find Specific PatternsProceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 0210.1109/WI-IAT.2014.77(30-37)Online publication date: 11-Aug-2014
https://dl.acm.org/doi/10.1109/WI-IAT.2014.77
Algarni ALi YWu SXu Y(2012)Text mining in negative relevance feedbackWeb Intelligence and Agent Systems10.5555/2589968.258997010:2(151-163)Online publication date: 1-Apr-2012
https://dl.acm.org/doi/10.5555/2589968.2589970
Carpineto CRomano G(2012)A Survey of Automatic Query Expansion in Information RetrievalACM Computing Surveys10.1145/2071389.207139044:1(1-50)Online publication date: 1-Jan-2012
https://dl.acm.org/doi/10.1145/2071389.2071390
Lau RLai CBruza PWong K(2011)Leveraging web 2.0 data for scalable semi-supervised learning of domain-specific sentiment lexiconsProceedings of the 20th ACM international conference on Information and knowledge management10.1145/2063576.2063991(2457-2460)Online publication date: 24-Oct-2011
https://dl.acm.org/doi/10.1145/2063576.2063991
Gan LTu WLiu GYu J(2011)Review and Perspective of Markov Network Information Retrieval Model2011 7th International Conference on Wireless Communications, Networking and Mobile Computing10.1109/wicom.2011.6040277(1-4)Online publication date: Sep-2011
https://doi.org/10.1109/wicom.2011.6040277
Li YAlgarni AXu Y(2010)A pattern mining approach for information filtering systemsInformation Retrieval10.1007/s10791-010-9154-414:3(237-256)Online publication date: 14-Dec-2010
https://doi.org/10.1007/s10791-010-9154-4
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten