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Evaluation of an inference network-based retrieval model

Editor: Robert B. Allen Authors:

Howard Turtle,

W. Bruce CroftAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 9, Issue 3

Pages 187 - 222

https://doi.org/10.1145/125187.125188

Published: 01 July 1991 Publication History

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References

[1]

CROFT, W. B., AND DAS, R. Experiments with query acquisition and use in document retrieval systems. In Proceedings of the 13th International Conference on Research and Development in Information Retrieval Jean-Luc Vidick, Ed. ACM, Sept. 1990, pp. 349-368.

Crossref

Google Scholar

[2]

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

Google Scholar

[3]

CHEESEMAN, P. An inquiry into computer understanding. Comp. Intell. 4 (Feb. 1988), 58-66.

Google Scholar

[4]

COHEN, P. R., AND KJELDSEN, R. Information retrieval by constrained spreading activation in semantic networks. Inf. Prccess. Manage. 23, 2 (1987), 255-268.

Crossref

Google Scholar

[5]

COHEN, P.R. Heuristic Reasoning About Uncertainty: An Artificial Intelligence Approach. Pitman, Boston, Mass., 1985.

Crossref

Google Scholar

[6]

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

Google Scholar

[7]

CROFT, W. B. A model of cluster searching based on classification. Inf. Syst. 5, 3 (1980), 189-195.

Google Scholar

[8]

CROFT, W. B. Boolean queries and term dependencies in probabilistic retrieval models J. Am. Soc. Inf. Sci. 37, 2 (1986), 71-77.

Google Scholar

[9]

CROFT, W.B. Approaches to intelligent information retrieval. Inf. Process. Manage. 23, 4 (1987), 249-254.

Crossref

Google Scholar

[10]

CROFT, W. B., AND THOMPSON, R.H. The use of adaptive mechanisms for selection of search strategies in document retrieval systems. In Proceedings of the ACM/BCS Internatwnal Conference on Research and Developmellt ~n Infvrmation Retrieval, C. J. van Rijsbergen, Ed. 1984, pp. 95-110.

Crossref

Google Scholar

[11]

CROFT, W. B., AND THOMPSON, R. H. I3R: A new approach to the design of document retrieval systems. J. Am. Soc. Inf. Sci., 38 (Nov. 1987), 389-404.

Crossref

Google Scholar

[12]

CROFT, W. B., AND TURTLE, H. A retrieval model incorporating hypertext links. In Hypertext '89 Proceedings 1989, pp. 213-224.

Crossref

Google Scholar

[13]

DEMPSTER, A. P. A generalization of Bayesian inference. J. Royal Stat. Soc. B. (1968), 205-247.

Google Scholar

[14]

DOYLE, J. A truth maintenance system. Art~f. Intell. 12, 3 (1979), 231-272.

Google Scholar

[15]

FURNAS, G. W., LANDAUER, T. K., GOMEZ, L. M., AND DUMAIS, S. T. The vocabulary problem in human-system communication. Commun. ACM, 30, 11 (Nov. 1987), 964-971.

Crossref

Google Scholar

[16]

Fox, E. A., NUNN, G. L., AND LEE, W.C. Coefficients for combining concept classes in a collection. In Proceedings of the Eleventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (Grenoble, June 13-15, 1988). ACM, New York, 1988, pp. 291-308.

Crossref

Google Scholar

[17]

Fox, E.A. Characterization of two new experimental collections in computer and information science containing textual and bibliographic concepts. Technical Report 83-561. Dept. of Computer Science, Cornell Univ., Ithaca, N.Y., Sept. 1983.

Google Scholar

[18]

FURH, N. Models for retrieval with probabilistic indexing. Inf. Process. Manage. 25 1 (1989), 55-72.

Crossref

Google Scholar

[19]

KANAL, L. N., AND LEMMER, J. F., EDS. Uncertainty ~n Artificial Intelligence. North- Holland, Ameterdam, 1986.

Crossref

Google Scholar

[20]

KATZER, J., McGILL, M. J., TESSIER, J. A., FRAKES, W., AND DAsGUPTA, P. A study of the overlap among document representations. Inf. Technol. Res. Dev. I (1982), 261-274

Google Scholar

[21]

LEMMER, J. F., ANn KANAL, L. N., EDS. Uncertainty in Artificial Intelligence 2. North- Holland, Amsterdam, 1988.

Crossref

Google Scholar

[22]

LAUmTZEN, S. L., AND SPmGEL~ALTER, D. J. Local computations with probabihties on graphical structures and their application to expert systems. J. Royal Star. Soc. B, 50 2 (1988), 157-224.

Google Scholar

[23]

MARON, M. E., AND KU~NS, J. L. On relevance, probabilistic indexing and information retrieval. J. ACM, 7 (1960), 216-224.

Crossref

Google Scholar

[24]

McGILL, M., KOLL, M., AND NOREAULT, T. An evaluation of factors affecting document ranking by information retrieval systems. Tech. Rep., Syracuse Univ, School of Information Studies, 1979.

Google Scholar

[25]

NmssoN, N.J. Probabilistic logic. Art~f lntell. 28, 1, (1986), 71-87

Crossref

Google Scholar

[26]

NOREAULT, T., KOLL, M., AND McGILL, M.J. Automatic ranked output for Boolean searches in SIRE J. Am Soc Inf Scl. 28, 6 (1977), 333-339.

Google Scholar

[27]

ODD~, R. N., PALMQUIST, R. h., AND CRAWFORD, M.A. Representation of anomalous states of knowledge in information retrieval. In Proceedings of the 1986 ASIS Annual Conference. 1986, pp. 248-254.

Google Scholar

[28]

PEARL, J. Probabilist~c Reasoning in Intelligent Systems: Networks of Plaustble Inference Morgan Kaufmann Publishers, 1988.

Crossref

Google Scholar

[29]

ROBESTSON, S. E. The probability ranking principle m IR J. Doc. 33, 4 (Dec. 1977), 294-304.

Google Scholar

[30]

SALTON, G. A simple blueprint for automatic Boolean query processing. Inf. Process Manage. 24, 3 (1988), 269-280

Crossref

Google Scholar

[31]

JONES, K. S., AND BATES, R. G Research on automatic indexing 1974-1976. Tech. Rep. Computer Laboratory, Univ. of Cambridge, 1977.

Google Scholar

[32]

SALTON, G., AND BUCKLEY, C. Term weighting approaches in automatic text retrieval Inf. Process. Manage. 24, 5 (1988), 513-523

Crossref

Google Scholar

[33]

SALTON, G., Fox, E., ANn Wu, H. Extended Boolean mformatmn retrmval. Commun. ACM, 26, 11 (Nov. 1983), 1022-1036.

Crossref

Google Scholar

[34]

SHAFER, G. A Mathematical Theory of Evidence. Princeton University Press, Princeton, N J., 1976.

Google Scholar

[35]

SmGEL, S. Non-parametric Statistics for the Behavorial Sciences. McGraw-Hill, New York, 1956.

Google Scholar

[36]

SALTON, G, AND McGILL, M J. Introduction to Modern Information Retrzeval. McGraw-Hill, New York, 1983.

Crossref

Google Scholar

[37]

STmHNG, K.H. The effect of document ranking on retrieval system performance: A search for an optimal ranking rule. Proc. Am. Soc Inf. Sci. 12 (1975), 105-106.

Google Scholar

[38]

THOMPSON, R, H., AND CROFT, W.B. Support for browsing in an intelligent text retrieval system. Int. J. Man-Mach. Stud., 30 (1989), 639-668.

Crossref

Google Scholar

[39]

qZJRTLE, H., AND CROFT, W.B. Efficient evaluation for probabilistic retrieval In RIA091 Conference Proceedings (Barcelona, Apr. 3-5, 1991), pp. 644-661.

Google Scholar

[40]

TONG, R. M., AND SHAPmO, D. Experimental investigations of uncertainty in a rule-based system for information retrieval Int. J. Man-Mach. Stud. 22 (1985), 265-282.

Google Scholar

[41]

TURTLE, H. Inference Networks for Document Retrieval. PhD thems, Computer and Information Science Dept., Univ. of Massachusetts, Amherst, Mass., 1990. Available as COINS Tech. Rep. 90-92.

Crossref

Google Scholar

[42]

VAN RIJSBEROEN, C.J. Information Retrieval Butterworths, London, 1979.

Crossref

Google Scholar

[43]

VAN RIJSBEROEN, C. J. A non-classical logic for information retrmval. Comput. d. 29, 6 (1986), 481-485.

Google Scholar

[44]

WmsoN, P. Situational relevance. Inf. Storage Retrieval 9 (1973), 457-471.

Google Scholar

[45]

ZADE~, L.A. The role of fuzzy logic in the management of uncertainty in expert systems Fuzzy Sets Sys. 11, (1983), 199-228.

Google Scholar

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Fitzgerald Kde la Harpe AUys C(2023)The hybridised indexing method for research-based information retrievalJournal of Information Science10.1177/016555152199980049:2(319-334)Online publication date: 22-Mar-2023
https://dl.acm.org/doi/10.1177/0165551521999800
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
Chatterjee SSarkar K(2023)Bengali Document Retrieval Using Model CombinationProceedings of International Conference on Frontiers in Computing and Systems10.1007/978-981-99-2680-0_9(91-101)Online publication date: 1-Aug-2023
https://doi.org/10.1007/978-981-99-2680-0_9
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Index Terms

Evaluation of an inference network-based retrieval model
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
  2. Information storage systems

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Reviews

Reviewer: Duncan A. Buell

The authors introduce a network-based Bayesian retrieval model, which is suitable or extensible to hypertext and other nontraditional representations of documents. Experiments are run using document files from Communications of the ACM and CISI (published by the Compagnie Internationale de Services en Informatique) , and benefits from this retrieval model are demonstrated. Of particular interest is the ability of the authors' model to support multiple document representation schemes and the combination of different queries and query types. This paper is carefully written and complete. The assumptions are clearly stated, and the conclusions, given the data, are valid.

Reviewer: Caroline Merriam Eastman

Turtle and Croft advocate the use of inference networks in information retrieval systems. In their models, both document collections and queries are represented by networks; multiple representations can be handled. The authors compare their inference networks to probabilistic and Boolean models and show how networks can be used to simulate both of these models. Experiments were conducted using two commonly used test collections: the CACM collection with 3204 documents and the CISI collection, published by the Compagnie Internationale de Services en Informatique (the International Information Services Company), with 1460 documents. The network model performed somewhat better than the probabilistic model and much better than the Boolean model. Combining results from different versions of the same queries gave improved performance. One important result is that the use of a nonzero default probability for term belief improves performance. Different results might have been obtained if different versions of the models had been implemented. Reasonable choices appear to have been made in all cases, however. The paper is not easy to read and has few examples. It presents important results that are of interest to researchers in this area, however.

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Information

Published In

ACM Transactions on Information Systems Volume 9, Issue 3

Special issue on research and development in information retrieval

July 1991

122 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/125187

Editor:
Robert B. Allen
Bellcore, Morristown, NJ

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 July 1991

Published in TOIS Volume 9, Issue 3

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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
Chatterjee SSarkar K(2023)Bengali Document Retrieval Using Model CombinationProceedings of International Conference on Frontiers in Computing and Systems10.1007/978-981-99-2680-0_9(91-101)Online publication date: 1-Aug-2023
https://doi.org/10.1007/978-981-99-2680-0_9
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Non-relevance Feedback for Document Retrieval

Ontology-based semantic retrieval of documents using Word2vec model

Inference networks for document retrieval

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