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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.

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Cited By

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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: 02 January 1995

Published in TOIS Volume 13, Issue 1

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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
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
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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
https://doi.org/10.1093/comjnl/bxw034
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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Hunag JYuan XZhong NYao Y(2015)Modeling Tag-Aware Recommendations Based on User PreferencesInternational Journal of Information Technology & Decision Making10.1142/S021962201550019414:05(947-970)Online publication date: Sep-2015
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Aswani Kumar CChandra Mouliswaran SAmriteya PArun S(2015)Fuzzy Formal Concept Analysis Approach for Information RetrievalProceedings of the Fifth International Conference on Fuzzy and Neuro Computing (FANCCO - 2015)10.1007/978-3-319-27212-2_20(255-271)Online publication date: 25-Nov-2015
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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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