Skip to main content
SpringerLink
Log in
Book cover

International Conference on Human Interaction and Emerging Technologies

IHIET-AI 2021: Human Interaction, Emerging Technologies and Future Applications IV pp 186–199Cite as

Personalizing Fuzzy Search Criteria for Improving User-Based Flexible Search

  • Conference paper
  • First Online:

  • 1385 Accesses

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1378))

Abstract

This proposal provides a user-friendly way of personalizing fuzzy search criteria in an expressive searching platform. The interest is in, for example, if we have a fuzzy criterion “expensive” for searching expensive restaurants defined in the system, by personalization, any user can access the criterion and personalize it with his/her preferences and values that satisfies his/her needs. In this way, every user retrieves different results while querying over a single fuzzy search criterion. The system executes this personalized fuzzy searching criterion if the logged-in user has previously personalized that criterion definition. Moreover, our framework is user-friendly enough to perform expressive searches over modern and conventional database formats without knowing the low-level syntax of the criteria of the framework. Furthermore, we present the architecture of this novel framework, with its design and implementation details. We provide a clarifying case study on our system by providing an experiment. We have analyzed the results obtained from the experiment to show our system’s behavior and performance after incorporating the functionality of the personalization of fuzzy search criteria.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Bosc, P., Pivert, O.: SQLf: a relational database language for fuzzy querying. IEEE Trans. Fuzzy Syst. 3(1), 1–17 (1995). https://doi.org/10.1109/91.366566

    Article  Google Scholar 

  2. Dubois, D., Prade, H.: Using fuzzy sets in flexible querying: why and how? In: Troels, A., Henning, C., Legind, L.H. (eds.) Flexible Query Answering Systems, pp. 45–60 (1997). https://dl.acm.org/citation.cfm

  3. Tahani, V.: A conceptual framework for fuzzy query processing: a step toward very intelligent database systems. Inf. Process Manag. 13, 289–303 (1977)

    Article  Google Scholar 

  4. Rodriguex, L.J.T.: (Ph.D. Tesis) a contribution to database flexible querying: Fuzzy quantified queries evaluation, Novemver 2005

    Google Scholar 

  5. Prade, H., Testemale, C.: Generalizing database relational algebra for the treatment of incomplete/uncertain information and vague queries. Inf. Sci. 34, 113–143 (1984)

    Article  MathSciNet  Google Scholar 

  6. Umano, M., Hatono, I., Tamura, H.: Fuzzy databaase systems. In: Proceedings of the IEEE International Joint Conference on Fuzzy Systems, vol. 5, pp. 35–36 (1995)

    Google Scholar 

  7. Moreno, J.M., Aciego, M.O.: On first-order multiadjoint logic programming (2002)

    Google Scholar 

  8. Konstantinou, N., Spanos, M.C., Solidakis, E., Mitrou, N.: VisAVis: an approach to an intermediate layer between ontologies and relational database contents. In: Proceedings of the 2006 CAISE Third International Workshop on Web Information system Modeling (WISM) (2006)

    Google Scholar 

  9. Martínez-Cruz, C., Noguera, J.M., Vila, M.A.: Flexible queries on relational databases using fuzzy logic and ontologies. Inf. Sci. 366, 150–164 (2016)

    Article  MathSciNet  Google Scholar 

  10. Takahashi, Y.: A fuzzy query language for relational databases. IEEE Trans. Syst. Man. Cyb. 21, 1576–1579 (1991)

    Article  Google Scholar 

  11. Vojtas, P.: Fuzzy logic programming. Fuzzy Set. Syst. 124(3), 361–370 (2001)

    Article  MathSciNet  Google Scholar 

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

    Google Scholar 

  13. Li, D., Liu, D.: A Fuzzy Prolog Database System. Wiley, New York (1990)

    Google Scholar 

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

    Google Scholar 

  15. Morcillo, P., Moreno, G.: Floper, a fuzzy logic programming environment for research. In: Gij (ed.) Proceedings of VIII Jornadas sobre Programacion y Lenguajes (PROLE 2008), vol. 10, pp. 259–263 (2008)

    Google Scholar 

  16. Bobillo, F., Straccia, U.: fuzzyDL: an expressive fuzzy description logic reasoner. In: International Conference on Fuzzy Systems (FUZZ08), , pp. 923–930. IEEE Computer Society (2008)

    Google Scholar 

  17. Guadarrama, S., Muñoz, S., Vaucheret, C.: Fuzzy prolog: a new approach using soft constraints propagation. Fuzzy Sets Syst. 144(1), 127–150 (2004). https://doi.org/10.1016/j.fss.2003.10.017

    Article  MathSciNet  MATH  Google Scholar 

  18. Vaucheret, C., Guadarrama, S., Muñoz-Hernández, S.: Fuzzy prolog: a simple general implementation using CLP(R). In: Baaz, M., Voronkov, A. (eds.) (LPAR). Lecture Notes in Artificial Intelligence, vol. 2514, pp. 450–464. Springer (2002)

    Google Scholar 

  19. Muñoz Hernández, S., Pablos-Ceruelo, V., Strass, H.: RFuzzy: syntax, Semantics and Implementation Details of a Simple and Expressive Fuzzy Tool over Prolog. Inf. Sci. 181(10), 1951–1970 (2011). https://doi.org/10.1016/j.ins.2010.07.033

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  21. 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, vol. 3, Benidorm (Alicante), Spain, June 2011, pp. 918–929 (2011)

    Google Scholar 

  22. Pablos-Ceruelo, V., Muñoz Hernández, S.: Getting answers to fuzzy and flexible searches by easy modelling of real-world knowledge. In: Proceedings of 5th International Joint Conference on Computational Intelligence, pp. 265–275 (2013). https://doi.org/10.5220/0004555302650272.

  23. The CLIP lab: The Ciao Prolog Development System. https://www.clip.dia.fi.upm.es/Software/Ciao

  24. Medina, J., Ojeda-Aciego, M., Vojtas, P.: A multi-adjoint approach to similarity-based unification. Electr. Notes Theor. Comput. Sci. 66, 70–85 (2002)

    Article  Google Scholar 

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

    Google Scholar 

  26. Medina, J., Ojeda-Aciego, M., Vojtas, P.: Multi-adjoint logic programming with continuous semantics. In: Proceedings of the 6th International Conference on Logic Programming and Nonmonotonic Reasoning, series LPNMR 2001, pp. 351–364. Springer, London (2001)

    Google Scholar 

  27. Medina, J., Ojeda-Aciego, M., Vojtas, P.: A procedural semantics for multi-adjoint logic programming. In: Proceedings of Progress in Artificial Intelligence, pp. 290–297 (2001)

    Google Scholar 

  28. Medina, J., Ojeda-Aciego, M., Vojtas, P.: Similarity-based unification: a multi-adjoint approach. Fuzzy Set. Syst. 146(1), 43–62 (2004)

    Article  MathSciNet  Google Scholar 

  29. Pablos-Ceruelo, V., Muñoz-Hernández, S.: FleSe: a tool for posing flexible and expressive (fuzzy) queries to a regular database. In: Proceedings of 11th International Conference on Distributed Computing and Artificial Intelligence, pp. 157–164 (2014)

    Google Scholar 

  30. Deedar, M.H., Muñoz-Hernández, S.: Allowing users to create similarity relations for their flexible searches over databases. In: Artificial Intelligence and Soft Computing, pp. 526–541. Springer, Cham (2019)

    Google Scholar 

  31. Deedar, M.H., Muñoz-Hernández, S.: User-friendly interface for introducing fuzzy criteria into expressive searches. In: Intelligent Systems and Applications, pp. 982–997. Springer, Cham (2020)

    Google Scholar 

  32. Deedar, M.H., Muñoz-Hernández, S.: Extending a flexible searching tool for multiple database formats. In: Emerging Trends in Electrical, Communications, and Information Technologies, pp. 25–35. Springer (2020)

    Google Scholar 

  33. Deedar, M.H., Muñoz-Hernández, S.: UFleSe: user-friendly parametric framework for expressive flexible searches. Can. J. Electr. Comput. Eng. 43(4), 235–250 (2020)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, Campus de Montegancedo s/n, 28660, Boadilla del Monte, Madrid, Spain

    Mohammad Halim Deedar & Susana Muñoz-Hernández

Authors
  1. Mohammad Halim Deedar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Susana Muñoz-Hernández
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Halim Deedar .

Editor information

Editors and Affiliations

  1. Institute for Advanced Systems Engineering, University of Central Florida, Orlando, FL, USA

    Tareq Ahram

  2. Campus du Moulin de la Housse, Université de Reims Champagne-Ardenne GRESPI, Reims Cedex 2, France

    Redha Taiar

  3. Directrice Hôpitaux Universitaires de Strasbourg, IFMK, Strasbourg, France

    Fabienne Groff

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Deedar, M.H., Muñoz-Hernández, S. (2021). Personalizing Fuzzy Search Criteria for Improving User-Based Flexible Search. In: Ahram, T., Taiar, R., Groff, F. (eds) Human Interaction, Emerging Technologies and Future Applications IV. IHIET-AI 2021. Advances in Intelligent Systems and Computing, vol 1378. Springer, Cham. https://doi.org/10.1007/978-3-030-74009-2_24

Download citation

Publish with us

Policies and ethics

Search

Navigation