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Evaluation of an inference network-based retrieval model

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  1. 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. Google ScholarGoogle Scholar
  2. 2 CROFT, W. B., AND HARPER, D.J. Using probabilistic models of document retrieval without relevance information. J. Doc. 35 (1979), 285-295.Google ScholarGoogle Scholar
  3. 3 CHEESEMAN, P. An inquiry into computer understanding. Comp. Intell. 4 (Feb. 1988), 58-66.Google ScholarGoogle Scholar
  4. 4 COHEN, P. R., AND KJELDSEN, R. Information retrieval by constrained spreading activation in semantic networks. Inf. Prccess. Manage. 23, 2 (1987), 255-268. Google ScholarGoogle Scholar
  5. 5 COHEN, P.R. Heuristic Reasoning About Uncertainty: An Artificial Intelligence Approach. Pitman, Boston, Mass., 1985. Google ScholarGoogle Scholar
  6. 6 COOPER, W.S. A definition of relevance for information retrieval. Inf. Storage Retrwval, 7 (1971), 19-37.Google ScholarGoogle Scholar
  7. 7 CROFT, W. B. A model of cluster searching based on classification. Inf. Syst. 5, 3 (1980), 189-195.Google ScholarGoogle Scholar
  8. 8 CROFT, W. B. Boolean queries and term dependencies in probabilistic retrieval models J. Am. Soc. Inf. Sci. 37, 2 (1986), 71-77.Google ScholarGoogle Scholar
  9. 9 CROFT, W.B. Approaches to intelligent information retrieval. Inf. Process. Manage. 23, 4 (1987), 249-254. Google ScholarGoogle Scholar
  10. 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. Google ScholarGoogle Scholar
  11. 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. Google ScholarGoogle Scholar
  12. 12 CROFT, W. B., AND TURTLE, H. A retrieval model incorporating hypertext links. In Hypertext '89 Proceedings 1989, pp. 213-224. Google ScholarGoogle Scholar
  13. 13 DEMPSTER, A. P. A generalization of Bayesian inference. J. Royal Stat. Soc. B. (1968), 205-247.Google ScholarGoogle Scholar
  14. 14 DOYLE, J. A truth maintenance system. Art~f. Intell. 12, 3 (1979), 231-272.Google ScholarGoogle Scholar
  15. 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. Google ScholarGoogle Scholar
  16. 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. Google ScholarGoogle Scholar
  17. 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 ScholarGoogle Scholar
  18. 18 FURH, N. Models for retrieval with probabilistic indexing. Inf. Process. Manage. 25 1 (1989), 55-72. Google ScholarGoogle Scholar
  19. 19 KANAL, L. N., AND LEMMER, J. F., EDS. Uncertainty ~n Artificial Intelligence. North- Holland, Ameterdam, 1986. Google ScholarGoogle Scholar
  20. 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), 261-274Google ScholarGoogle Scholar
  21. 21 LEMMER, J. F., ANn KANAL, L. N., EDS. Uncertainty in Artificial Intelligence 2. North- Holland, Amsterdam, 1988. Google ScholarGoogle Scholar
  22. 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 ScholarGoogle Scholar
  23. 23 MARON, M. E., AND KU~NS, J. L. On relevance, probabilistic indexing and information retrieval. J. ACM, 7 (1960), 216-224. Google ScholarGoogle Scholar
  24. 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 ScholarGoogle Scholar
  25. 25 NmssoN, N.J. Probabilistic logic. Art~f lntell. 28, 1, (1986), 71-87 Google ScholarGoogle Scholar
  26. 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 ScholarGoogle Scholar
  27. 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 ScholarGoogle Scholar
  28. 28 PEARL, J. Probabilist~c Reasoning in Intelligent Systems: Networks of Plaustble Inference Morgan Kaufmann Publishers, 1988. Google ScholarGoogle Scholar
  29. 29 ROBESTSON, S. E. The probability ranking principle m IR J. Doc. 33, 4 (Dec. 1977), 294-304.Google ScholarGoogle Scholar
  30. 30 SALTON, G. A simple blueprint for automatic Boolean query processing. Inf. Process Manage. 24, 3 (1988), 269-280 Google ScholarGoogle Scholar
  31. 31 JONES, K. S., AND BATES, R. G Research on automatic indexing 1974-1976. Tech. Rep. Computer Laboratory, Univ. of Cambridge, 1977.Google ScholarGoogle Scholar
  32. 32 SALTON, G., AND BUCKLEY, C. Term weighting approaches in automatic text retrieval Inf. Process. Manage. 24, 5 (1988), 513-523 Google ScholarGoogle Scholar
  33. 33 SALTON, G., Fox, E., ANn Wu, H. Extended Boolean mformatmn retrmval. Commun. ACM, 26, 11 (Nov. 1983), 1022-1036. Google ScholarGoogle Scholar
  34. 34 SHAFER, G. A Mathematical Theory of Evidence. Princeton University Press, Princeton, N J., 1976.Google ScholarGoogle Scholar
  35. 35 SmGEL, S. Non-parametric Statistics for the Behavorial Sciences. McGraw-Hill, New York, 1956.Google ScholarGoogle Scholar
  36. 36 SALTON, G , AND McGILL, M J. Introduction to Modern Information Retrzeval. McGraw-Hill, New York, 1983. Google ScholarGoogle Scholar
  37. 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 ScholarGoogle Scholar
  38. 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. Google ScholarGoogle Scholar
  39. 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 ScholarGoogle Scholar
  40. 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 ScholarGoogle Scholar
  41. 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. Google ScholarGoogle Scholar
  42. 42 VAN RIJSBEROEN, C.J. Information Retrieval Butterworths, London, 1979. Google ScholarGoogle Scholar
  43. 43 VAN RIJSBEROEN, C. J. A non-classical logic for information retrmval. Comput. d. 29, 6 (1986), 481-485.Google ScholarGoogle Scholar
  44. 44 WmsoN, P. Situational relevance. Inf. Storage Retrieval 9 (1973), 457-471.Google ScholarGoogle Scholar
  45. 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 ScholarGoogle Scholar

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  1. Evaluation of an inference network-based retrieval model

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      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.

      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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      • Published in

        cover image ACM Transactions on Information Systems
        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
        Issue’s Table of Contents

        Copyright © 1991 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 1 July 1991
        Published in tois Volume 9, Issue 3

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