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On modeling information retrieval with probabilistic inference

Editor: Robert B. Allen Authors:

S. K. M. Wong,

Y. Y. YaoAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 13, Issue 1

Pages 38 - 68

https://doi.org/10.1145/195705.195713

Published: 02 January 1995 Publication History

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Abstract

This article examines and extends the logical models of information retrieval in the context of probability theory. The fundamental notions of term weights and relevance are given probabilistic interpretations. A unified framework is developed for modeling the retrieval process with probabilistic inference. This new approach provides a common conceptual and mathematical basis for many retrieval models, such as the Boolean, fuzzy set, vector space, and conventional probabilistic models. Within this framework, the underlying assumptions employed by each model are identified, and the inherent relationships between these models are analyzed. Although this article is mainly a theoretical analysis of probabilistic inference for information retrieval, practical methods for estimating the required probabilities are provided by simple examples.

References

[1]

ADAMS, E.W. 1975. The Logic ofCondtttonals. Reidel, Dordrecht.

Google Scholar

[2]

BELLMAN, R. E. AND ZADEH, L.A. 1970 Decislon-making in fuzzy environment Manage. Sci. 17, 141-164.

Google Scholar

[3]

BOOKSTEIN, A. 1985. Implications of Boolean structure for information retrieval. In Proceedings of the ACM SIGIR Conference on Research and Development ~n Information Retrieval. ACM, New York, 11-17.

Crossref

Google Scholar

[4]

BUELL, D. A. AND KRAFT, D.H. 1981a. Threshold values and Boolean retrieval systems. Inf. Process. Manage. 17, 127-136

Google Scholar

[5]

BUELL, D. A. AND KRAFT, D.H. 1981b. A model for a weighted retrieval system. J. Am. Soc. Inf. Sci. 32, 211-216.

Google Scholar

[6]

CARNAP, R. 1971. Inductive logic and rational decision. In Studies m Inductive Logic and Probab~ltty. Vol. 1. University of California Press, Berkeley, Calif., 5-31.

Google Scholar

[7]

CAR~, R. 1962. Logical Foundations of Probability. 2nd ed. University of Chicago Press, Chicago, Ill.

Google Scholar

[8]

CmAR~IELLA, Y. AND CHEVALLET, J.P. 1992. About retrieval models and logic. Comput. J. 35, 233 242.

Crossref

Google Scholar

[9]

C~ow, C. K. AND LIu, C. N. 1968. Approximating discrete probability distributions with dependence trees. IEEE Trans. Inf. Theor. IT-14, 462-467.

Google Scholar

[10]

COOPER, W.S. 1971. A definition of relevance for information retrieval. Inf. Storage Retriev. 7, 19-37

Google Scholar

[11]

CROFT, W. B. AND HARPER, D. J. 1979. Using probabilistic models of document retrieval without relevance information. J. Doc. 35, 285 295.

Google Scholar

[12]

CROFT, W. B., CRINGEAN, T. J. J., AND WILLETT, P. 1989. Retrieving documents by plausible inference: An experimental study. Inf. Process. Manage. 25, 599-614.

Crossref

Google Scholar

[13]

DE FINET~I, B. 1974. Theory of Probability. Wiley, New York.

Google Scholar

[14]

FINE, T L. 1973. Theorzes of Probability: An Examination of Foundatzons Academic Press, New York.

Google Scholar

[15]

FUHR, N. 1992. Probabilistic models in information retrieval. Comput. J. 35, 243-255.

Crossref

Google Scholar

[16]

FUHR, N. 1986. Two models of retrieval with probabilistic indexing. In Proceedings of the ACM SIGIR Conference On Research and Development in Informatzon Retrieval ACM, New York, 249-257.

Crossref

Google Scholar

[17]

GILES, R. 1976. Lukasiewicz logic fuzzy theory. Int. J. Man-Machine Stud. 8, 313-327.

Google Scholar

[18]

GOOD, I.J. 1983. Good Thinking: The Foundations of Probability and Its Application. University of Minnesota Press, Minneapolis.

Google Scholar

[19]

GORDON, M. AND KOCHE~, M. 1989. Recall-precision trade-off: A derivation. J. Am. Soc. Inf. Sc~. 40, 145 151.

Crossref

Google Scholar

[20]

HACKING, I. 1975. The Emergence of Probability. Cambridge University Press, London.

Google Scholar

[21]

HARRISON, M.A. 1965. Introduction to Switching and Automata Theory. McGraw-Hill Book Company, New York.

Google Scholar

[22]

JAMES, E.T. 1979. Where do we stand on maximum entropy. In The Maximum Entropy Formalism. The MIT Press, Cambridge, Mass.

Google Scholar

[23]

KLm, G. J. ANn FOLGER, T.A. 1988. Fuzzy sets, Uncertainty, and Information. Prentice-Hall, Englewood Cliffs, N. J.

Google Scholar

[24]

MARON, M. E. AND KUHNS, J.L. 1960. On relevance, probabilistic indexing and information retrieval. J. ACM 7, 216-244.

Crossref

Google Scholar

[25]

MARR, D. 1982. Vision. Freeman, San Francisco.

Google Scholar

[26]

NIE, J.Y. 1992. Towards a probabilistic modal logic for semantic-based information retrieval. In Proceedings of the 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, New York, 140-151.

Crossref

Google Scholar

[27]

Nm, J. 1989. An information retrieval model based on modal logic. Inf. Process. Manage. 25, 477-491.

Google Scholar

[28]

PAWLAK, Z., WONG, S. K. M., AND ZIARKO, W. 1988 Rough sets: Probabilistic versus deterministic approach. Int. J. Man-Machine Stud. 29, 81-95.

Crossref

Google Scholar

[29]

PEARL, J. 1988. Probabil~stic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo, Calif.

Crossref

Google Scholar

[30]

RADECKI, T. 1976. Mathematic model of information retrieval based on the concept of a fuzzy thesaurus. Inf. Process. Manage. 12,313-318.

Google Scholar

[31]

RAGHAVAN, V. V. AND WONG, S. K. M. 1986. A critical analysis of vector space model in information retrieval. J. Am. Soc. Inf. Sc~. 37, 279-287.

Google Scholar

[32]

ROBERTSON, S.E. 1977. The probability ranking principle in IR. J. Doc. 33, 294-304.

Google Scholar

[33]

ROBERTSON, S. E. ANn SPARCK JONES, K. 1976. Relevance weighting of search terms. J. Am. Soc. Inf. Sci. 27, 129-146.

Google Scholar

[34]

ROBERTSON, S. E., MARON, M. E., AND COOPER, W. S. 1982. Probability of relevance: A unification of two competing models for document retrieval. Inf. Tech. Res. Dev. 1, 1-21.

Google Scholar

[35]

SALTON, G., (ED). 1971. The SMART Retrieval System--Experiments in Automatic Document Processing. Prentice-Hall, Englewood Cliffs, N. J.

Crossref

Google Scholar

[36]

SALTON, G. AND McGmL, M.H. 1983. Introduction to Modern Information Retrieval. McGraw- Hill, New York.

Crossref

Google Scholar

[37]

SALTON, G., Fox, E. A., AND Wu, H. 1983. Extended Boolean information retrieval. Commun. ACM 26, 1022-1036.

Crossref

Google Scholar

[38]

SARACEVIC, T. 1975. Relevance: A review of and a framework for the thinking on the notion in information science. J. Am. Soc. Inf. Sci. 26, 321-343.

Google Scholar

[39]

SAVAGE, L.J. 1972. The Foundations of Statistics. Dover, New York.

Google Scholar

[40]

SCH~UBLE, P. 1987. Thesaurus based concept spaces. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, New York, 254-262.

Crossref

Google Scholar

[41]

SH~ER, G. 1987. Probability judgment in artificial and expert systems. Stat. Sci. 2, 3-16.

Google Scholar

[42]

SHANNON, C.E. 1948. The mathematical theory of communication. Bell Syst. Tech. J. 27, 379-423,623-656.

Google Scholar

[43]

T~OMPSON, P. 1990. A combination of expert opinion approach to probabilistic information retrieval, Part 1: The conceptual model; Part 2: Mathematical treatment of CEO model 3. Inf. Process. Manage. 26, 371-382. 383 394.

Crossref

Google Scholar

[44]

THOMPSON, P. 1988. Subjective probability and information retrieval: A review of the psychological literature. J. Doc. 44, 119-143.

Crossref

Google Scholar

[45]

TURTLE, H. R. AND CROFT, W.B. 1992. A comparison of text retrieval models. Comput. J. 35, 279-290.

Crossref

Google Scholar

[46]

TURTLE, $. AND CROFT, W.B. 1990. Inference network for document retrieval. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, New York, 1-24.

Crossref

Google Scholar

[47]

VAN RIJSBERGEN, C.J. 1992. Probabilistic retrieval revisited. Comput. J. 35, 291-298.

Crossref

Google Scholar

[48]

vAN RIJSBERGEN, C.J. 1989. Towards an information logic. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, New York, 77-86.

Crossref

Google Scholar

[49]

VAN RIJSBERGEN, C.J. 1986 A non-classical logic for information retrieval. Comput. J. 29, 481-485.

Google Scholar

[50]

VAN RIJSBERGEN, C.J. 1979. Information Retrieval. Butterworth, London.

Crossref

Google Scholar

[51]

WATANABE, S. 1985. Pattern Recognition: Human and Mechanical. Wiley, New York.

Crossref

Google Scholar

[52]

WON6, S K. M. AND YAO, Y.Y. 1991. A probabilistic inference model for information retrieval. Inf. Syst. 16, 301-321.

Crossref

Google Scholar

[53]

WONG, S. K. M. AND YAO, Y.Y. 1990. A generalized binary probabilistic independence model. J. Am Soc. Inf. Sci. 41,324-329.

Google Scholar

[54]

WONG, S. K. M., BOLLMANN, P., AND YAO, Y.Y. 1991. Information retrieval based on axiomatic decision theory. Int. J. Gen. Syst. 19,301-321.

Google Scholar

[55]

WONG, S. K. M., ZIARKO, W., RAGHAVAN, V. V., ~D WONG, P. C.N. 1987. On modeling of information retrieval concepts in vector spaces. ACM Trans. Database Syst. 12,299-321.

Crossref

Google Scholar

[56]

YAO, Y. Y. AND WONG, S. K. M. 1992. A decision theoretic framework for approximating concepts. Int. J. Man-machtne Stud. 37, 793 807.

Crossref

Google Scholar

[57]

Yu, C. T. AND SALTON, G. 1976. Precision weighting--An effective automatic indexing method. J. ACM 23, 76-85.

Crossref

Google Scholar

[58]

ZADEH, L.A. 1965. Fuzzy sets. Inf. Contr. 8, 338-353.

Google Scholar

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Chatterjee SSarkar K(2023)Bengali document retrieval using a language modeling approach enhanced by improved cluster-based smoothingSādhanā10.1007/s12046-023-02258-148:4Online publication date: 4-Oct-2023
https://doi.org/10.1007/s12046-023-02258-1
Paik JAgrawal YRishi SShah V(2021)Truncated Models for Probabilistic Weighted RetrievalACM Transactions on Information Systems10.1145/347683740:3(1-24)Online publication date: 8-Dec-2021
https://dl.acm.org/doi/10.1145/3476837
Gudivada VRao DGudivada A(2018)Information Retrieval: Concepts, Models, and SystemsComputational Analysis and Understanding of Natural Languages: Principles, Methods and Applications10.1016/bs.host.2018.07.009(331-401)Online publication date: 2018
https://doi.org/10.1016/bs.host.2018.07.009
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Index Terms

On modeling information retrieval with probabilistic inference
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Probabilistic reasoning
      2. Vagueness and fuzzy logic
2. Information systems
  1. Information retrieval
    1. Information retrieval query processing

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Reviews

Reviewer: Duncan A. Buell

The problem of the mathematical framework for the docu<__?__Pub Fmt hyphen-point>ments-to-queries matching portion of an information retrieval system is examined. Assuming some universe in which probabilities can be computed, the fundamental notion is that the degree of support for a query of q provided by a document d is P q&vbm0;d = P q?d P d . Through a series of examples, the authors then show that this probabilistic inference model is similar to other models for information retrieval (Boolean, fuzzy-set, vector space, and so on). Although this paper is interesting and easy to read, what is most striking about it is the discrepancy between the broad claim that this method “provides a common conceptual and mathematical basis” for other retrieval models, which are called “special cases,” and the many caveats that appear when the individual models are examined. For example, the authors show that different variations in the new model lead to approximations to the vector space model under a given similarity measure, but some well-known similarity measures (like the cosine measure)<__?__Pub Caret> cannot be interpreted thus. Similarly, the new model is the same as the standard Boolean model or the fuzzy-set model, provided one views the latter two in a particularly narrow way.

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Published In

ACM Transactions on Information Systems Volume 13, Issue 1

Jan. 1995

111 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/195705

Editor:
Robert B. Allen
Bellcore, Morristown, NJ

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 January 1995

Published in TOIS Volume 13, Issue 1

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Paik JAgrawal YRishi SShah V(2021)Truncated Models for Probabilistic Weighted RetrievalACM Transactions on Information Systems10.1145/347683740:3(1-24)Online publication date: 8-Dec-2021
https://dl.acm.org/doi/10.1145/3476837
Gudivada VRao DGudivada A(2018)Information Retrieval: Concepts, Models, and SystemsComputational Analysis and Understanding of Natural Languages: Principles, Methods and Applications10.1016/bs.host.2018.07.009(331-401)Online publication date: 2018
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Zhai CLafferty J(2017)A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information RetrievalACM SIGIR Forum10.1145/3130348.313037751:2(268-276)Online publication date: 2-Aug-2017
https://dl.acm.org/doi/10.1145/3130348.3130377
Abdulahhad KChevallet JBerrut C(2016)Logics, Lattices and Probability: The Missing Links to Information RetrievalThe Computer Journal10.1093/comjnl/bxw034Online publication date: 29-Jul-2016
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Huang JZhu KZhong N(2016)A probabilistic inference model for recommender systemsApplied Intelligence10.1007/s10489-016-0783-145:3(686-694)Online publication date: 1-Oct-2016
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Improving probabilistic information retrieval by modeling burstiness of words

Modeling information retrieval with user preference and probabilistic inference

Modeling term proximity for probabilistic information retrieval models

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