More Web Proxy on the site http://driver.im/

column

Inference Networks for Document Retrieval

Authors:

W. Bruce CroftAuthors Info & Claims

ACM SIGIR Forum, Volume 51, Issue 2

Pages 124 - 147

https://doi.org/10.1145/3130348.3130361

Published: 02 August 2017 Publication History

Abstract

The use of inference networks to support document retrieval is introduced. A network-basead retrieval model is described and compared to conventional probabilistic and Boolean models.

References

[1]

W. Bruce Croft and D. J. Harper. Using probabilistic models of document retrieval without relevance information. Journal of Documentation, 35:285--295, 1979.

[2]

Peter Cheeseman. An inquiry into computer understanding. Computational Intelligence, 4:58--66, February 1988. Article is part of a debate between logic and probability schools in AI.

[3]

Paul R. Cohen and Rick Kjeldsen. Information retrieval by constrained spreading activation in semantic networks. Information Processing and Management, 23(2):255--268, 1987.

Digital Library

[4]

Paul R. Cohen. Heuristic Reasoning About Uncertainty: An Artificial Intelligence Approach. Pitman, Boston, MA, 1985.

Digital Library

[5]

W. S. Cooper. A definition of relevance for information retrieval. Information Storage and Retrieval, 7:19--37, 1971.

[6]

W. Bruce Croft. A model of cluster searching based on classification. Znformalion Systems, 5:189--195, 1980.

[7]

W. Bruce Croft. Boolean queries and term dependencies in probabilistic retrieval models. Journal of the American Society for Information Science, 37(2):71--77, 1986.

[8]

W. Bruce Croft. Approaches to intelligent information retrieval. Information Processing and Management, 23(4):249--254, 1987.

Digital Library

[9]

W. Bruce Croft and Roger II. Thompson. 13R: A new approach to the design of document retrieval systems. Journal of the American Society for Information Science, 38(6):38%404, November 1987.

[10]

W. Bruce Croft and Howard Turtle. A retrieval model incorporating hypertext links. In Bypertest '89 Proceedings, pages 213--224, 1989.

[11]

A. P. Dempster. A generalization of Bayesian inference. Journal of the Royal Statistical Society B, 30:205--247, 1968.

[12]

John Doyle. A truth maintenance system. Artificial Intelligence, 12(3):231--272, 1979.

[13]

G. W. Furnas, T. K. Landauer, L. M. Gomez, and S. T. Dumais. The vocabulary problem in human-system communication. Communications of the ACM, 30(11):964--971, November 1987.

Digital Library

[14]

Edward A. Fox, Gary L. Nunn, and Whay C. Lee. Coefficients for combining concept classes in a collection. In Proceedings of the Eleventh Annual Intemational ACM SIGIR Conference on Research and Development in Information Retrieval, pages 291--308, New York, NY, 1988. ACM.

Digital Library

[15]

Norbert Fuhr. Models for retrieval with probabilistic indexing. Information Processing and Management, 25( 1):55--72, 1989.

Digital Library

[16]

Laveen N. Kanal and John F. Lemmer, editors. Uncertainty in Artificial Intelligence. North-Holland, Amsterdam, 1986.

Digital Library

[17]

J. Katzer, M. J. McGilI, J. A. Tessier, W. Frakes, and P. DasGupta. A study of the overlap among document representations. Information Technology: Research and Development, 1:261--274, 1982.

[18]

John F. Lemmer and Laveen N. Kanal, editors. Uncertainty in Artificial Intelligence 2. North-Holland, Amsterdam, 1988.

Digital Library

[19]

S. L. Lauritzen and D. J. Spiegelhalter. Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society B, 50(2):157--224, 1988.

[20]

M. E. Maron and J. L. Kuhns. On relevance, probabilstic indexing and information retrieval. Journal of the ACM, 7:216--244, 1960.

Digital Library

[21]

Michael McGill, Mathew Koll, and Terry Noreault. An evaluation of factors. affecting document ranking by information retrieval systems. Technical report, Syracuse University, School of Information Studies, 1979. Funded under NSFJST-78-10454.

[22]

Nils J. Nilsson. Probabilistic logic. Artificial Intelligence, 28( 1):71--87, 1986.

Digital Library

[23]

Robert N. Oddy, Ruth A. Palmquist, and Margaret A. Crawford. Representation of anomalous states of knowledge in information retrieval. In Pmeedings of the 1986 ASIS Annual Conference, pages 248--254,1986.

[24]

Judea Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, 1988.

Digital Library

[25]

S. E. Robertson. The probability ranking principle in IR. Journal of Documentation, 33(4):294--304, December 1977.

[26]

Gerard Salton. A simple biueprint for automatic boolean query processing. Information Processing and Management, 24(3):26%280, 1988.

Digital Library

[27]

Glen Shafer. A Mathematical Theory of Evidence. Princeton University Press, 1976.

[28]

Gerard Salton and Michael J. McGill. Introduction to Modem information Retrieval. McGraw-Hill, 1983.

[29]

K. H. Stirling. The effect of document ranking on retrieval system performance: A search for an optimal ranking rule. Proceedings of the American Society for Information Science, 12:105--106, 1975.

[30]

Roger H. Thompson and W. Bruce Croft. Support for browsing in an intelligent text retrieval system. International Journal of Man-Machine Studies, 30:639--668, 1989.

Digital Library

[31]

Richard M. Tong and Daniel Shapiro. Experimental investigations of uncertainty in a rule-based system for information retrieval. International Journal of Man-Machine Studies, 22:265--282, 1985.

[32]

C. J. van Rijsbergen. Information Retrieual. Butterworths, 1979.

[33]

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

[34]

Patrick Wilson. Situational relevance. Information Storage and Retrieval, 9:457--471, 1973.

[35]

Lotfi A. Zadeh. The role of fuzzy logic in the management of uncertainty in expert systems. Fuzzy Sets and Systems, 11:199--228, 1983.

Digital Library

Cited By

Zhang LXu JGong YYu LZhang JShen J(2022)Unsupervised Image and Text Fusion for Travel Information EnhancementIEEE Transactions on Multimedia10.1109/TMM.2021.306440824(1415-1425)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1109/TMM.2021.3064408
Xu XLin KGao LLu HShen HLi X(2022)Learning Cross-Modal Common Representations by Private–Shared Subspaces SeparationIEEE Transactions on Cybernetics10.1109/TCYB.2020.300900452:5(3261-3275)Online publication date: May-2022
https://doi.org/10.1109/TCYB.2020.3009004
Cao RCao YZhou GLuo P(2022)Extracting Variable-Depth Logical Document Hierarchy from Long Documents: Method, Evaluation, and ApplicationJournal of Computer Science and Technology10.1007/s11390-021-1076-737:3(699-718)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s11390-021-1076-7
Show More Cited By

Index Terms

Inference Networks for Document Retrieval

Index terms have been assigned to the content through auto-classification.

Recommendations

Inference networks for document retrieval
SIGIR '90: Proceedings of the 13th annual international ACM SIGIR conference on Research and development in information retrieval

The use of inference networks to support document retrieval is introduced. A network-based retrieval model is described and compared to conventional probabilistic and Boolean models.
Inference networks for document retrieval
Inference Networks for Document Retrieval

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM SIGIR Forum

ACM SIGIR Forum Volume 51, Issue 2

SIGIR Test-of-Time Awardees 1978-2001

July 2017

276 pages

ISSN:0163-5840

DOI:10.1145/3130348

Editors:
Donna Harman
National Institutes of Science & Technology, Gaithersburg MD, USA
,
Diane Kelly
University of Tennessee, Knoxville TN, USA

Issue’s Table of Contents

Copyright © 2017 Copyright is held by the owner/author(s).

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 August 2017

Published in SIGIR Volume 51, Issue 2

Check for updates

Qualifiers

Column

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
324
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)0

Reflects downloads up to 13 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zhang LXu JGong YYu LZhang JShen J(2022)Unsupervised Image and Text Fusion for Travel Information EnhancementIEEE Transactions on Multimedia10.1109/TMM.2021.306440824(1415-1425)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1109/TMM.2021.3064408
Xu XLin KGao LLu HShen HLi X(2022)Learning Cross-Modal Common Representations by Private–Shared Subspaces SeparationIEEE Transactions on Cybernetics10.1109/TCYB.2020.300900452:5(3261-3275)Online publication date: May-2022
https://doi.org/10.1109/TCYB.2020.3009004
Cao RCao YZhou GLuo P(2022)Extracting Variable-Depth Logical Document Hierarchy from Long Documents: Method, Evaluation, and ApplicationJournal of Computer Science and Technology10.1007/s11390-021-1076-737:3(699-718)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s11390-021-1076-7
Boughanem MAkermi IPasi GAbdulahhad K(2020)Information Retrieval and Artificial IntelligenceA Guided Tour of Artificial Intelligence Research10.1007/978-3-030-06170-8_5(147-180)Online publication date: 8-May-2020
https://doi.org/10.1007/978-3-030-06170-8_5
Heng HXiaozhong LYan Y(2019)Domain-aware Neural Model for Sequence Labeling using Joint LearningThe World Wide Web Conference10.1145/3308558.3313566(2837-2843)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313566
Bafna PPramod DShrwaikar SHassan A(2019)Semantic key phrase-based model for document managementBenchmarking: An International Journal10.1108/BIJ-04-2018-010226:6(1709-1727)Online publication date: 19-Jun-2019
https://doi.org/10.1108/BIJ-04-2018-0102
Tumeng RJawawi DIsa M(2019)An enhanced latent semantic indexing with term frequency-inverse document frequency variant for software traceabilityIOP Conference Series: Materials Science and Engineering10.1088/1757-899X/551/1/012073551:1(012073)Online publication date: 1-Aug-2019
https://doi.org/10.1088/1757-899X/551/1/012073
(2018)Comparison and Evaluation of Information Retrieval ModelsVFAST Transactions on Software Engineering10.21015/vtse.v13i1.496(7-14)Online publication date: 3-Feb-2018
https://doi.org/10.21015/vtse.v13i1.496

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.

Media

Figures

Other

Tables

View Issue’s Table of Contents