- 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 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. Google Scholar
- 5 COHEN, P.R. Heuristic Reasoning About Uncertainty: An Artificial Intelligence Approach. Pitman, Boston, Mass., 1985. 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. 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. 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. Google Scholar
- 12 CROFT, W. B., AND TURTLE, H. A retrieval model incorporating hypertext links. In Hypertext '89 Proceedings 1989, pp. 213-224. 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. 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. 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. Google Scholar
- 19 KANAL, L. N., AND LEMMER, J. F., EDS. Uncertainty ~n Artificial Intelligence. North- Holland, Ameterdam, 1986. 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), 261-274Google Scholar
- 21 LEMMER, J. F., ANn KANAL, L. N., EDS. Uncertainty in Artificial Intelligence 2. North- Holland, Amsterdam, 1988. 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. 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 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. 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 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 Google Scholar
- 33 SALTON, G., Fox, E., ANn Wu, H. Extended Boolean mformatmn retrmval. Commun. ACM, 26, 11 (Nov. 1983), 1022-1036. 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. 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. 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. Google Scholar
- 42 VAN RIJSBEROEN, C.J. Information Retrieval Butterworths, London, 1979. 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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