Abstract
Conceptual graphs and fuzzy logic are two logical formalisms that emphasize the target of natural language, where conceptual graphs provide a structure of formulas close to that of natural language sentences while fuzzy logic provides a methodology for computing with words. This paper proposes fuzzy conceptual graphs as a knowledge representation language that combines the advantages of both the two formalisms for computational intelligence approaching human expression and reasoning. Firstly, simple fuzzy conceptual graphs are defined as bipartite graphs of concepts alternate with conceptual relations. Fuzzy types are introduced to represent uncertainty and/or partial truth about concept or relation types. For representing complex information, simple fuzzy conceptual graphs are extended to nested fuzzy conceptual graphs. Then, as a basic operation for inference, the projection operation that matches a (nested) fuzzy conceptual graph to another one and measures the relative necessity degree of the former given the latter is defined.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cao, T.H. (2000), Annotated fuzzy logic programs. International Journal for Fuzzy Sets and Systems, 113 (2), 277–298.
Cao, T.H. (1999), Foundations of order-sorted fuzzy set logic programming in predicate logic and conceptual graphs. PhD Thesis, University of Queensland.
Cao, T.H. and Creasy, P.N. (1998), Fuzzy order-sorted logic programming in conceptual graphs with a sound and complete proof procedure. In Mugnier, M.L. and Chein, M. (eds), Conceptual Structures: Theory, Tools and Applications, LNAI 1453, Springer-Verlag, pp. 270–284.
Cao, T.H., Creasy, P.N. and Wuwongse, V. (1997), Fuzzy types and their lattices. In Proceedings of the 6th IEEE International Conference on Fuzzy Systems, pp. 805–812.
Lawry, J. (2000). An alternative interpretation of linguistic variables and computing with words. In Proceedings of the 8th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems. To appear.
Sowa, J.F. (1984), Conceptual Structures - Information Processing in Mind and Machine. Addison-Wesley Publishing Company.
Sowa, J.F. (1991), Towards the expressive power of natural language. In Sowa, J.F. (ed.), Principles of Semantic Networks–Explorations in the Representation of Knowledge, Morgan Kaufmann Publishers, pp. 157–189.
Sowa, J.F. (1997), Matching logical structure to linguistic structure. In Houser, N. and Roberts, D.D. and Van Evra, J. (eds), Studies in the Logic of Charles Sanders Peirce, Indiana University Press, pp. 418–444.
Zadeh, L.A. (1975), Fuzzy logic and approximate reasoning (In memory of Grigore Moisil). Synthese, 30, 407–428.
Zadeh, L.A. (1978), PRUF–a meaning representation language for natural languages. International Journal of Man-Machine Studies, 10, 395–460.
Zadeh, L.A. (1983), A fuzzy-set-theoretic approach to fuzzy quantifiers in natural languages. Computers and Mathematics with Applications, 9, 149–184.
Zadeh, L.A. (1996), Fuzzy logic = computing with words. IEEE Transactions on Fuzzy Systems, 4, 103–111.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cao, T.H. (2000). Fuzzy Conceptual Graphs: A Language for Computational Intelligence Approaching Human Expression and Reasoning. In: Sinčák, P., Vaščák, J., Kvasnička, V., Mesiar, R. (eds) The State of the Art in Computational Intelligence. Advances in Soft Computing, vol 5. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1844-4_20
Download citation
DOI: https://doi.org/10.1007/978-3-7908-1844-4_20
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1322-7
Online ISBN: 978-3-7908-1844-4
eBook Packages: Springer Book Archive