A Novel Android Application Design Based on Fuzzy Ontologies to Carry Out Local Based Group Decision Making Processes

  • J. A. Morente Molinera
  • R. Wikström
  • C. Carlsson
  • F. J. Cabrerizo
  • I. J. Pérez
  • E. Herrera-Viedma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9880)


The appearance of Web 2.0 and mobile technologies, the increase of users participating on the Internet and the high amount of available information have created the necessity of designing tools capable of making the most out of this environment. In this paper, the design of an Android application that is capable of aiding some experts in carrying out a group decision making process in Web 2.0 and mobile environments is presented. For this purpose, Fuzzy Ontologies are used in order to deal with the high amount of information available for the users. Thanks to the way that they deal with the information, they are used in order to retrieve a small set of alternatives that the users can utilize in order to carry out group decision making processes with a feasible set of valid alternatives.


Group decision making Fuzzy ontologies Decision support system 



This paper has been developed with the financing of FEDER funds in TIN2013-40658-P and Andalusian Excellence Project TIC-5991.


  1. 1.
    Fuchs, C., Boersma, K., Albrechtslund, A., Sandoval, M.: Internet and surveillance: the challenges of Web 2.0 and social media, 16 (2013)Google Scholar
  2. 2.
    Kacprzyk, J.: Group decision making with a fuzzy linguistic majority. Fuzzy Sets Syst. 18(2), 105–118 (1986)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Maedche, A.: Ontology Learning for the Semantic Web, vol. 665. Springer Science and Business Media, Heidelberg (2012)zbMATHGoogle Scholar
  4. 4.
    Yager, R.R.: On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Trans. Syst. Man Cybern. 18(1), 183–190 (1988)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Yager, R.R.: Quantifier guided aggregation using OWA operators. Int. J. Intell. Syst. 11(1), 49–73 (1996)CrossRefGoogle Scholar
  6. 6.
    Herrera, F., Herrera-Viedma, E.: Aggregation operators for linguistic weighted information. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 27(5), 646–656 (1997)CrossRefGoogle Scholar
  7. 7.
    Chiclana, F., Herrera, F., Herrera-Viedma, E.: Integrating three representation models in fuzzy multipurpose decision making based on fuzzy preference relations. Fuzzy Sets Syst. 97(1), 33–48 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Herrera, F., Herrera-Viedma, E., Verdegay, J.L.: A sequential selection process in group decision making with a linguistic assessment approach. Inf. Sci. 85(4), 223–239 (1995)CrossRefzbMATHGoogle Scholar
  9. 9.
    Alonso, S., Pérez, I.J., Cabrerizo, F.J., Herrera-Viedma, E.: A linguistic consensus model for web 2.0 communities. Appl. Soft Comput. 13(1), 149–157 (2013)CrossRefGoogle Scholar
  10. 10.
    Calegari, S., Ciucci, D.: Fuzzy ontology, fuzzy description logics and fuzzy-OWL. In: Masulli, F., Mitra, S., Pasi, G. (eds.) WILF 2007. LNCS (LNAI), vol. 4578, pp. 118–126. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Baader, F.: The Description Logic Handbook: Theory, Implementation and Applications. Cambridge University Press, Cambridge (2003)zbMATHGoogle Scholar
  12. 12.
    Lukasiewicz, T., Straccia, U.: Managing uncertainty and vagueness in description logics for the semantic web. Web Semant.: Sci. Serv. Agents World Wide Web 6(4), 291–308 (2008)CrossRefGoogle Scholar
  13. 13.
    Bobillo, F.: Managing Vagueness in Ontologies. University of Granada, Granada (2008)Google Scholar
  14. 14.
    Carlsson, C., Mezei, J., Brunelli, M.: Fuzzy ontology used for knowledge mobilization. Int. J. Intell. Syst. 28, 52–71 (2013)CrossRefGoogle Scholar
  15. 15.
    Morente-Molinera, J.A., Al-hmouz, R., Morfeq, A., Balamash, A.S., Herrera-Viedma, E.: A decision support system for decision making in changeable and multi-granular fuzzy linguistic contexts. J. Multiple-Valued Logic Soft Comput. 26(3–5), 485–514 (2016)MathSciNetGoogle Scholar
  16. 16.
    Shen, F., Xu, J., Xu, Z.: An outranking sorting method for multi-criteria group decision making using intuitionistic fuzzy sets. Inf. Sci. 334, 338–353 (2016)CrossRefGoogle Scholar
  17. 17.
    Cabrerizo, F.J., Chiclana, F., Al-Hmouz, R., Morfeq, A., Balamash, A.S., Herrera-Viedma, E.: Fuzzy decision making and consensus: challenges. J. Intell. Fuzzy Syst. 29(3), 1109–1118 (2015)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • J. A. Morente Molinera
    • 1
    • 5
  • R. Wikström
    • 2
    • 3
  • C. Carlsson
    • 2
    • 3
  • F. J. Cabrerizo
    • 5
  • I. J. Pérez
    • 4
  • E. Herrera-Viedma
    • 5
  1. 1.Department of EngineeringUniversidad Internacional de la Rioja (UNIR)Logroño, La RiojaSpain
  2. 2.Laboratory of Industrial ManagementAbo Akademi UniversityAboFinland
  3. 3.Institute for Advanced Management Systems ResearchAbo Akademi UniversityAboFinland
  4. 4.Department of Computer EngineeringUniversity of CádizCádizSpain
  5. 5.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain

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