Getting Answers to Fuzzy and Flexible Searches by Easy Modelling of Real-World Knowledge

Victor Pablos-Ceruelo, Susana Munoz-Hernandez

2013

Abstract

We present a framework for merging the non-fuzzy real-world information stored in databases with the fuzzy knowledge that we (human beings) have. The interest in this aggregation is providing a (fuzzy and non-fuzzy) search engine able to answer flexible and expressive queries without sacrificing a friendly user interface. We achieve this task by using a new syntax (whose semantics are included too) for modelling the domain knowledge and a flexible and enough general structure to represent any user query. We expect this work contributes to the development of more human-oriented fuzzy search engines.

References

  1. Baldwin, J. F., Martin, T. P., and Pilsworth, B. W. (1995). Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence. John Wiley & Sons, Inc., New York, NY, USA.
  2. Bobillo, F. and Straccia, U. (2008). fuzzydl: An expressive fuzzy description logic reasoner. In 2008 International Conference on Fuzzy Systems (FUZZ-08), pages 923-930. IEEE Computer Society.
  3. Bordogna, G. and Pasi, G. (1994). A fuzzy query language with a linguistic hierarchical aggregator. In Proceedings of the 1994 ACM symposium on Applied computing, SAC 7894, pages 184-187, New York, NY, USA. ACM.
  4. Bosc, P. and Pivert, O. (1995). Sqlf: a relational database language for fuzzy querying. Fuzzy Systems, IEEE Transactions on, 3(1):1 -17.
  5. Bosc, P. and Pivert, O. (2011). On a strengthening connective for flexible database querying. In Fuzzy Systems (FUZZ), 2011 IEEE International Conference on, pages 1233-1238.
  6. Dubois, D. and Prade, H. (1997). Using fuzzy sets in flexible querying: why and how? In Andreasen, T., Christiansen, H., and Larsen, H. L., editors, Flexible query answering systems, pages 45-60. Kluwer Academic Publishers, Norwell, MA, USA.
  7. Guadarrama, S., Mun˜oz-Hernández, S., and Vaucheret, C. (2004). Fuzzy prolog: a new approach using soft constraints propagation. Fuzzy Sets and Systems (FSS), 144(1):127 - 150. Possibilistic Logic and Related Issues.
  8. Ishizuka, M. and Kanai, N. (1985). Prolog-elf incorporating fuzzy logic. In IJCAI'85: Proceedings of the 9th international joint conference on Artificial intelligence, pages 701-703, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.
  9. Li, D. and Liu, D. (1990). A fuzzy Prolog database system. John Wiley & Sons, Inc., New York, NY, USA.
  10. Medina, J., Ojeda-Aciego, M., and Vojtás?, P. (2001a). A completeness theorem for multi-adjoint logic programming. In FUZZ-IEEE, pages 1031-1034.
  11. Medina, J., Ojeda-Aciego, M., and Vojtás?, P. (2001b). Multi-adjoint logic programming with continuous semantics. In Eiter, T., Faber, W., and Truszczynski, M., editors, LPNMR, volume 2173 of Lecture Notes in Computer Science, pages 351-364. Springer.
  12. Medina, J., Ojeda-Aciego, M., and Vojtás?, P. (2001c). A procedural semantics for multi-adjoint logic programming. In Brazdil, P. and Jorge, A., editors, EPIA, volume 2258 of Lecture Notes in Computer Science, pages 290-297. Springer.
  13. Medina, J., Ojeda-Aciego, M., and Vojtás?, P. (2002). A multi-adjoint approach to similarity-based unification. Electronic Notes in Theoretical Computer Science, 66(5):70 - 85. UNCL'2002, Unification in NonClassical Logics (ICALP 2002 Satellite Workshop).
  14. Medina, J., Ojeda-Aciego, M., and Vojtás?, P. (2004). Similarity-based unification: a multi-adjoint approach. Fuzzy Sets and Systems, 146(1):43-62.
  15. Morcillo, P. J. and Moreno, G. (2008). Floper, a fuzzy logic programming environment for research. In de Oviedo, F. U., editor, Proceedings of VIII Jornadas sobre Programación y Lenguajes (PROLE'08), pages 259-263, Gijón, Spain.
  16. Moreno, J. M. and Ojeda-Aciego, M. (2002). On firstorder multi-adjoint logic programming. In 11th Spanish Congress on Fuzzy Logic and Technology.
  17. Mun˜oz-Hernández, S., Pablos-Ceruelo, V., and Strass, H. (2011). Rfuzzy: Syntax, semantics and implementation details of a simple and expressive fuzzy tool over prolog. Information Sciences, 181(10):1951 - 1970. Special Issue on Information Engineering Applications Based on Lattices.
  18. Pablos-Ceruelo, V. and Mun˜oz-Hernández, S. (2011). Introducing priorities in rfuzzy: Syntax and semantics. In Proceedings of the 11th International Conference on Computational and Mathematical Methods in Science and Engineering, CMMSE. to be published.
  19. Ribeiro, R. A. and Moreira, A. M. (2003). Fuzzy query interface for a business database. International Journal of Human-Computer Studies, 58(4):363 - 391.
  20. Rodriguez, L. J. T. (2005). (phd. thesis) a contribution to database flexible querying: Fuzzy quantified queries evaluation.
  21. Vaucheret, C., Guadarrama, S., and Mun˜oz-Hernández, S. (2002). Fuzzy prolog: A simple general implementation using CLP(R). In Baaz, M. and Voronkov, A., editors, LPAR, volume 2514 of Lecture Notes in Artificial Intelligence, pages 450-464. Springer.
  22. Vojtás?, P. (2001). Fuzzy logic programming. Fuzzy Sets and Systems, 124(3):361-370.
  23. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3):338-353.
Download


Paper Citation


in Harvard Style

Pablos-Ceruelo V. and Munoz-Hernandez S. (2013). Getting Answers to Fuzzy and Flexible Searches by Easy Modelling of Real-World Knowledge . In Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (IJCCI 2013) ISBN 978-989-8565-77-8, pages 265-272. DOI: 10.5220/0004555302650272


in Bibtex Style

@conference{fcta13,
author={Victor Pablos-Ceruelo and Susana Munoz-Hernandez},
title={Getting Answers to Fuzzy and Flexible Searches by Easy Modelling of Real-World Knowledge},
booktitle={Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (IJCCI 2013)},
year={2013},
pages={265-272},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004555302650272},
isbn={978-989-8565-77-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (IJCCI 2013)
TI - Getting Answers to Fuzzy and Flexible Searches by Easy Modelling of Real-World Knowledge
SN - 978-989-8565-77-8
AU - Pablos-Ceruelo V.
AU - Munoz-Hernandez S.
PY - 2013
SP - 265
EP - 272
DO - 10.5220/0004555302650272