Abstract
Our concern is with the development of tools useful for the construction of semantically intelligent web based systems. We indicate that fuzzy set based reasoning systems such as approximate reasoning provide a fertile framework for the construction of these types of tools. Central to this framework is the representation of knowledge as the association of a constraint with a variable. Here we study one important type of variable, veristic, These are variables that can assume multiple values. Different types of statements providing information about veristic variables are described. A methodology is presented for representing and manipulating veristic information. We consider the role of these veristic variables in databases and describe methods for representing and evaluating queries involving veristic variables.
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References
T. Andreasen, H. Christianisen and H. L. Larsen, Flexible Query Answering Systems, Kluwer Academic Publishers: Norwell, MA, 1997.
G. Antoniou and F. van Harmelen, A Semantic Web Primer, The MIT Press: Cambridge, MA, 2004.
W. Bandler and L. Kohout, L., ‘‘Fuzzy power sets and fuzzy implication operators,’’ Fuzzy Sets and Systems 4, 13–30, 1980.
T. Berners-Lee, J. Hendler and O. Lassila, ‘‘The semantic web,’’ Scientific America May, 2001.
P. Bosc and H. Prade, ‘‘An introduction to the fuzzy set and possibility-based treatment of flexible queries and uncertain or imprecise databases,’’ in Uncertainty Management of Information Systems, edited by Motro, A. and Smets, P., Kluwer Academic Publishers: Norwell, MA, 285–326, 1997.
D. Dubois and H. Prade, ‘‘Incomplete conjunctive information,’’ Computational Mathematics Applications 15, 797–810, 1988a.
D. Dubois and H. Prade, Possibility Theory : An Approach to Computerized Processing of Uncertainty, Plenum Press: New York, 1988b.
D. Dubois and H. Prade, ‘‘Fuzzy sets in approximate reasoning Part I: Inference with possibility distributions,’’ Fuzzy Sets and Systems 40, 143–202, 1991.
V. Novak, The Alternative Mathematical Model of Linguistic Semantics and Pragmatics, Plenum Press: New York, 1992.
D. Fensel, J. Hendler, H. Lieberman and W. Wahlster, Spinning the Semantic Web, MIT Press: Cambridge, MA, 2003.
T. B. Passin, Explorers Guide to the SEmantic Web, Manning: Greenwich, CT, 2004.
F. E. Petry, Fuzzy Databases Principles and Applications, Kluwer: Boston, 1996.
R. Reiter, ‘‘On closed world data bases,’’ in Logic and Data Bases, Gallaire, H., & Minker, J. (eds.), New York: Plenum, 119–140, 1978.
E. Sanchez, Fuzzy Logic and the Semantic Web, Elsevier: Amsterdam, 2006.
R. Vandenberghe, A. Van Schooten, R. De Caluwe and E. E. Kerre, ‘‘Some practical aspects of fuzzy database techniques; An example,’’ Information Sciences 14, 465–472, 1989.
R. R. Yager, ‘‘Some questions related to linguistic variables,’’ Busefal 10, 54–65, 1982.
R. R. Yager, ‘‘On different classes of linguistic variables defined via fuzzy subsets,’’ Kybernetes 13, 103–110, 1984.
R. R. Yager, ‘‘Set based representations of conjunctive and disjunctive knowledge,’’ Information Sciences 41, 1–22, 1987a.
R. R. Yager, ‘‘Toward a theory of conjunctive variables,’’ Int. J. of General Systems 13, 203–227, 1987b.
R. R. Yager, ‘‘Reasoning with conjunctive knowledge,’’ Fuzzy Sets and Systems 28, 69–83, 1988.
R. R. Yager, ‘‘Veristic variables,’’ IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics 30, 71–84, 2000.
R. R. Yager, ‘‘Querying databases containing multivalued attributes using veristic variables,’’ Fuzzy Sets and Systems 129, 163–185, 2002.
R. R. Yager and D. P. Filev, Essentials of Fuzzy Modeling and Control, John Wiley: New York, 1994.
L. A. Zadeh, ‘‘Fuzzy sets,’’ Information and Control 8, 338–353, 1965.
L. A. Zadeh, ‘‘Fuzzy sets as a basis for a theory of possibility,’’ Fuzzy Sets and Systems 1, 3–28, 1978.
L. A. Zadeh, ‘‘A theory of approximate reasoning,’’ in Machine Intelligence, Vol. 9, edited by Hayes, J., Michie, D. and Mikulich, L. I., Halstead Press: New York, 149–194, 1979a.
[27]L. A. Zadeh, ‘‘Fuzzy sets and information granularity,’’ in Advances in Fuzzy Set Theory and Applications, edited by Gupta, M. M., Ragade, R. K. and Yager, R. R., North-Holland: Amsterdam, 3–18, 1979b.
[30]L. A. Zadeh, ‘‘Fuzzy logic = computing with words,’’ IEEE Transactions on Fuzzy Systems 4, 103–111, 1996.
[31]L. A. Zadeh, ‘‘Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic,’’ Fuzzy Sets and Systems 90, 111–127, 1997.
[b]L. A. Zadeh, ‘‘Toward a generalized theory of uncertainty (GTU)-An outline,’’ Information Sciences 172, 1–40, 2005.
[33]M. Zemankova and A. Kandel, Fuzzy Relational Data Bases – A Key to Expert Systems, Verlag TUV Rheinland: Cologne, 1984.
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Yager, R.R. (2007). Veristic Variables and Approximate Reasoning for Intelligent Semantic Web Systems. In: Nikravesh, M., Kacprzyk, J., Zadeh, L.A. (eds) Forging New Frontiers: Fuzzy Pioneers I. Studies in Fuzziness and Soft Computing, vol 217. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73182-5_12
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DOI: https://doi.org/10.1007/978-3-540-73182-5_12
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