Recognizing the most effective approximate reasoning calculi for a knowledge-based system

An important issue in the design of knowledge-based systems is how to equip them with the capability to transmit uncertainty from imprecise premises to a conclusion. Moreover, an important issue is the identification of the most appropriate calculi. This study compared the effectiveness of various calculi. The test domain was full-text retrieval of natural language documents. This test domain was chosen because natural language is inherently imprecise and there are well defined performance measures to provide the basis of comparison. Both robustness and relative retrieval quality were studied. A robust search will give comparable performance when used by a variety of different users. Retrieval quality measures the quantity and appropriateness of the retrieved information. Of particular interest in the results is that the most commonly used calculi in knowledge-based systems were not the best performing.<<ETX>>

[1]  M. E. Maron,et al.  An evaluation of retrieval effectiveness for a full-text document-retrieval system , 1985, CACM.

[2]  E H Shorthffe,et al.  Computer-based medical consultations mycin , 1976 .

[3]  Ronald R. Yager,et al.  Linguistic models and fuzzy truths , 1978 .

[4]  H. S. Heaps,et al.  Information retrieval, computational and theoretical aspects , 1978 .

[5]  Madan M. Gupta,et al.  Approximate reasoning in decision analysis , 1982 .

[6]  A. Kandel.  Fuzzy Mathematical Techniques With Applications , 1986 .

[7]  Ronald R. Yager,et al.  A note on weighted queries in information retrieval systems , 1987, J. Am. Soc. Inf. Sci..

[8]  Lotfi A. Zadeh,et al.  A Theory of Approximate Reasoning , 1979 .

[9]  W. Pedrycz.  Applications of fuzzy relational equations for methods of reasoning in presence of fuzzy data , 1985 .

[10]  J. Baldwin.  A new approach to approximate reasoning using a fuzzy logic , 1979 .

[11]  James C. Bezdek,et al.  Knowledge-assisted document retrieval. II: The retrieval process , 1987 .

[12]  Constantin Virgil Negoita.  On the application of the fuzzy sets separation theorem for automatic classification in information retrieval systems , 1973, Inf. Sci..

[13]  Richard M. Tong,et al.  RUBRIC: an environment for full text information retrieval , 1985, SIGIR '85.

[14]  Piero P. Bonissone,et al.  A Linguistic Approach to Decisionmaking with Fuzzy Sets , 1980, IEEE Transactions on Systems, Man, and Cybernetics.

[15]  James F. Baldwin,et al.  A fuzzy relational inference language , 1984 .

[16]  Etienne Kerre,et al.  A new approach to information retrieval systems using fuzzy expressions , 1985 .

[17]  R. K. Wiersba.  Review of "Information Retrieval: Computational and Theoretical Aspects, by H. S. Heaps", Academic Press Inc. , 1980, SIGF.

[18]  Craig Stanfill,et al.  Parallel free-text search on the connection machine system , 1986, CACM.

[19]  Karen Takle Quinn.  STAIRS Search Strategy: Ideas and Opinions , 1980 .

[20]  L. Zadeh.  The role of fuzzy logic in the management of uncertainty in expert systems , 1983 .

[21]  Kurt J. Schmucker,et al.  Fuzzy Sets, Natural Language Computations, and Risk Analysis , 1983 .

[22]  Richard M. Tong.  A Linguistic Approach to Decisionmaking with Fuzzy Sets , 1993 .

[23]  Ching Y. Suen,et al.  Large Tree Classifier with Heuristic Search and Global Training , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Ching Y. Suen,et al.  Analysis and Design of a Decision Tree Based on Entropy Reduction and Its Application to Large Character Set Recognition , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Donald H. Kraft,et al.  Fuzzy Sets and Generalized Boolean Retrieval Systems , 1983, Int. J. Man Mach. Stud..

[26]  K. Menger.  Statistical Metrics. , 1942, Proceedings of the National Academy of Sciences of the United States of America.

[27]  Stephen Cole Kleene,et al.  On notation for ordinal numbers , 1938, Journal of Symbolic Logic.

[28]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[29]  F. W. Lancaster,et al.  Vocabulary control for information retrieval , 1972 .

[30]  Howard Raiffa,et al.  Decision analysis: introductory lectures on choices under uncertainty. 1968. , 1969, M.D.Computing.

[31]  Alonzo Church,et al.  Review: D. A. Bocvar, On a Three-Valued Logical Calculus and its Application to the Analysis of Contradictions , 1939 .

[32]  Abraham Bookstein,et al.  Fuzzy requests: An approach to weighted boolean searches , 1980, J. Am. Soc. Inf. Sci..

[33]  Tadeusz Radecki,et al.  Fuzzy set theoretical approach to document retrieval , 1979, Inf. Process. Manag..

[34]  T. Radecki,et al.  On the inclusiveness of information retrieval systems with documents indexed by weighted descriptors , 1981 .

[35]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[36]  Duncan A. Buell,et al.  An analysis of some fuzzy subset applications to information retrieval systems , 1982 .

[37]  Daniel G. Shapiro,et al.  Experimental Investigations of Uncertainty in a Rule-Based System for Information Retrieval , 1985, Int. J. Man Mach. Stud..

[38]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .