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Introducing Similarity Relations in a Framework for Modelling Real-World Fuzzy Knowledge

  • Conference paper
Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 444))

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Abstract

There is no need for justifying the use of fuzzy logic (FL) to model the real-world knowledge. Bi-valued logic cannot conclude if a real-world sentence like “the restaurant is close to the city center” is true or false because it is neither true nor false. Letting apart paradoxes’ sentences, there are sentences (as the previous one) that are not true nor false but true up to some degree of truth or true at least to some degree of truth. In order to represent the truth or falsity of such sentences we need FL.

Similarity is a relation between real-world concepts. As in the representation of the truth of the first sentence, the representation of the similarity between two (fuzzy or not) concepts can be true, false or true up to (or at least to) some degree. We present syntactic constructions (and their semantics) for modelling such relation between concepts. The interest is in, for example, obtaining “spanish food restaurants” when asking for “mediterranean food restaurants” (only if the similarity between spanish and mediterranean food is explicitly stated in the program file). We hope this allows to represent in a better way the real-world knowledge, specially the concepts that are defined just by their similarity relations to some other concepts.

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References

  1. Baldwin, J.F., Martin, T.P., Pilsworth, B.W.: Fril-Fuzzy and Evidential Reasoning in Artificial Intelligence. John Wiley & Sons, Inc., New York (1995)

    Google Scholar 

  2. Bobillo, F., Straccia, U.: fuzzydl: An expressive fuzzy description logic reasoner. In: 2008 International Conference on Fuzzy Systems (FUZZ 2008), pp. 923–930. IEEE Computer Society (2008)

    Google Scholar 

  3. Dubois, D., Prade, H.: Comparison of two fuzzy set-based logics: similarity logic and possibilistic logic. In: Proceedings of 1995 IEEE Int. Fuzzy Systems, International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium, vol. 3, pp. 1219–1226 (1995)

    Google Scholar 

  4. Esteva, F., Garcia, P., Godo, L., Ruspini, E., Valverde, L.: On similarity logic and the generalized modus ponens. In: Proceedings of the Third IEEE Conference on Computational Intelligence, Fuzzy Systems, IEEE World Congress on Computational Intelligence, vol. 2, pp. 1423–1427 (1994)

    Google Scholar 

  5. Godo, L., Rodriguez, R.O.: A fuzzy modal logic for similarity reasoning. In: Cai, K.-Y., Chen, G., Ying, M. (eds.) Fuzzy Logic And Soft Computing. Kluwer Academic (1999)

    Google Scholar 

  6. Guadarrama, S., Muñoz-Hernández, S., Vaucheret, C.: Fuzzy prolog: a new approach using soft constraints propagation. Fuzzy Sets and Systems 144(1), 127–150 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  7. Ishizuka, M., Kanai, N.: Prolog-elf incorporating fuzzy logic. In: IJCAI 1985: Proceedings of the 9th International Joint Conference on Artificial Intelligence, pp. 701–703. Morgan Kaufmann Publishers Inc., San Francisco (1985)

    Google Scholar 

  8. Li, D., Liu, D.: A fuzzy Prolog database system. John Wiley & Sons, Inc., New York (1990)

    Google Scholar 

  9. Medina, J., Ojeda-Aciego, M., Vojtáš, P.: A completeness theorem for multi-adjoint logic programming. In: FUZZ-IEEE, pp. 1031–1034 (2001)

    Google Scholar 

  10. Medina, J., Ojeda-Aciego, M., Vojtáš, P.: Multi-adjoint logic programming with continuous semantics. In: Eiter, T., Faber, W., Truszczyński, M. (eds.) LPNMR 2001. LNCS (LNAI), vol. 2173, pp. 351–364. Springer, Heidelberg (2001)

    Google Scholar 

  11. Medina, J., Ojeda-Aciego, M., Vojtáš, P.: A procedural semantics for multi-adjoint logic programming. In: Brazdil, P., Jorge, A. (eds.) EPIA 2001. LNCS (LNAI), vol. 2258, pp. 290–297. Springer, Heidelberg (2001)

    Google Scholar 

  12. Medina, J., Ojeda-Aciego, M., Vojtáš, P.: A multi-adjoint approach to similarity-based unification. Electronic Notes in Theoretical Computer Science 66(5), 70–85 (2002), UNCL’2002, Unification in Non-Classical Logics (ICALP 2002 Satellite Workshop)

    Google Scholar 

  13. Medina, J., Ojeda-Aciego, M., Vojtáš, P.: Similarity-based unification: a multi-adjoint approach. Fuzzy Sets and Systems 146(1), 43–62 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  14. Moreno, J.M., Ojeda-Aciego, M.: On first-order multi-adjoint logic programming. In: 11th Spanish Congress on Fuzzy Logic and Technology (2002)

    Google Scholar 

  15. Morcillo, P.J., Moreno, G.: Floper, a fuzzy logic programming environment for research. In: Fundación Universidad de Oviedo (ed.) Proceedings of VIII Jornadas sobre Programación y Lenguajes (PROLE 2008), Gijón, Spain, pp. 259–263 (october 2008)

    Google Scholar 

  16. Muñoz-Hernández, S., Pablos-Ceruelo, V., Strass, H.: Rfuzzy: Syntax, semantics and implementation details of a simple and expressive fuzzy tool over prolog. Information Sciences 181(10), 1951–1970 (2011), Special Issue on Information Engineering Applications Based on Lattices

    Google Scholar 

  17. Pablos-Ceruelo, V., Muñoz-Hernández, S.: Introducing priorities in rfuzzy: Syntax and semantics. In: CMMSE 2011: Proceedings of the 11th International Conference on Mathematical Methods in Science and Engineering, Benidorm, Alicante, Spain, vol. 3, pp. 918–929 (June 2011)

    Google Scholar 

  18. Pablos-Ceruelo, V., Muñoz-Hernández, S.: Getting answers to fuzzy and flexible searches by easy modelling of real-world knowledge. In: FCTA 2013: Proceedings of the 5th International Conference on Fuzzy Computation Theory and Applications (2013)

    Google Scholar 

  19. Vaucheret, C., Guadarrama, S., Muñoz-Hernández, S.: Fuzzy prolog: A simple general implementation using CLP(R). In: Baaz, M., Voronkov, A. (eds.) LPAR 2002. LNCS (LNAI), vol. 2514, pp. 450–464. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  20. Vojtáš, P.: Fuzzy logic programming. Fuzzy Sets and Systems 124(3), 361–370 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  21. Wang, J.-B., Xu, Z.-Q., Wang, N.-C.: A fuzzy logic with similarity. In: Proceedings of the 2002 International Conference on Machine Learning and Cybernetics, vol. 3, pp. 1178–1183 (2002)

    Google Scholar 

  22. Zadeh, L.A.: Fuzzy sets. Information and Control 8(3), 338–353 (1965)

    Article  MATH  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. The Babel Research Group, Facultad de Informática, Universidad Politécnica de Madrid, Spain

    Víctor Pablos-Ceruelo & Susana Muñoz-Hernández

Authors
  1. Víctor Pablos-Ceruelo
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  2. Susana Muñoz-Hernández
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Editor information

Editors and Affiliations

  1. University Montpellier 2, LIRMM - CNRS UMR 5506, 161, Rue Ada, 34392, Montpellier Cedex 5, France

    Anne Laurent

  2. Institute for Human Factors and Technology Management, IAT, University of Stuttgart, Nobelstraße 12, 70569, Stuttgart, Germany

    Oliver Strauss

  3. LIP6, UPMC Univ. Paris 06, CNRS UMR 7606, F-75005, Paris, France

    Bernadette Bouchon-Meunier

  4. Dept. of Information Systems, Iona College, 710 North Ave, 10801, New Rochelle, NY, USA

    Ronald R. Yager

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© 2014 Springer International Publishing Switzerland

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Pablos-Ceruelo, V., Muñoz-Hernández, S. (2014). Introducing Similarity Relations in a Framework for Modelling Real-World Fuzzy Knowledge. In: Laurent, A., Strauss, O., Bouchon-Meunier, B., Yager, R.R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2014. Communications in Computer and Information Science, vol 444. Springer, Cham. https://doi.org/10.1007/978-3-319-08852-5_6

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  • DOI: https://doi.org/10.1007/978-3-319-08852-5_6

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08851-8

  • Online ISBN: 978-3-319-08852-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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