“Computing with Words”-Based Concept Retrieval

  • Bushra SiddiqueEmail author
  • M. M. Sufyan Beg
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 141)


Concept retrieval aims to extract documents that are semantically similar to the query. In view of the communication gap between the user and the system at the interface level in keyword-based search systems, user input in Natural Language (NL) is preferable. However, due to the inherent limitations of the natural languages, processing NL queries is challenging. Existing information retrieval system with conceptual search capabilities, formulate a keyword-based query out of the NL query which still might not fully reflect the concept expressed by the user. In such a scenario, the application of Computing with Words (CWW) computation is natural. In the literature, CWW techniques are applied in various processes of IR systems. In this paper, we propose a novel CWW-based paradigm for an extended idea of concept retrieval systems which are capable of returning a set of objects which satisfy the concept/constraint expressed in the user input NL description. The paper demonstrates the applicability of CWW computation for realizing concept retrieval task and thus highlights a new domain for further research.


  1. 1.
    Berzal, F., Martin-Bautista, M.J., Vila, M.A., Larsen, H.L.: Computing with words in information retrieval. In: IFSA World Congress and 20th NAFIPS International Conference, 2001, Joint 9th, pp. 3088–3092. IEEE (2001)Google Scholar
  2. 2.
    Herrera, F., Herrera-Viedma, E., Martınez, L.: A fusion approach for managing multi-granularity linguistic term sets in decision making. Fuzzy Sets Syst. 114(1), 43–58 (2000)CrossRefGoogle Scholar
  3. 3.
    Herrera-Viedma, E., López-Herrera, A.G.: A model of an information retrieval system with unbalanced fuzzy linguistic information. Int. J. Intell. Syst. 22(11), 1197–1214 (2007)CrossRefGoogle Scholar
  4. 4.
    Herrera-Viedma, E., López-Herrera, A.G., Luque, M., Porcel, C.: A fuzzy linguistic irs model based on a 2-tuple fuzzy linguistic approach. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 15(02), 225–250 (2007)CrossRefGoogle Scholar
  5. 5.
    Herrera-Viedma, E., Peis, E., Morales-del Castillo, J.M., Alonso, S., Anaya, K.: A fuzzy linguistic model to evaluate the quality of web sites that store XML documents. Int. J. Approx. Reason. 46(1), 226–253 (2007)CrossRefGoogle Scholar
  6. 6.
    Kostek, B.: “Computing with words” concept applied to musical information retrieval. Electr. Notes Theor. Comput. Sci. 82(4), 141–152 (2003)CrossRefGoogle Scholar
  7. 7.
    Martinez, L., Barranco, M.J., Pérez, L.G., Espinilla, M.: A knowledge based recommender system with multigranular linguistic information. Int. J. Comput. Intell. Syst. 1(3), 225–236 (2008)CrossRefGoogle Scholar
  8. 8.
    Pal, D., Mitra, M., Datta, K.: Improving query expansion using wordnet. J. Assoc. Inf. Sci. Technol. 65(12), 2469–2478 (2014)CrossRefGoogle Scholar
  9. 9.
    Singh, J., Sharan, A.: A new fuzzy logic-based query expansion model for efficient information retrieval using relevance feedback approach. Neural Comput. Appl. 28(9), 2557–2580 (2017)CrossRefGoogle Scholar
  10. 10.
    Wang, T.C., Chang, T.H.: Forecasting the probability of successful knowledge management by consistent fuzzy preference relations. Expert Syst. Appl. 32(3), 801–813 (2007)CrossRefGoogle Scholar
  11. 11.
    Zadeh, L.A.: PRUF - a meaning representation language for natural languages. Int. J. Man-Mach. Stud. 10(4), 395–460 (1978)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Zadeh, L.A.: A new direction in AI: toward a computational theory of perceptions. AI Mag. 22(1), 73–84 (2001)zbMATHGoogle Scholar
  13. 13.
    Zadeh, L.A.: From computing with numbers to computing with words: from manipulation of measurements to manipulation of perceptions. In: The Dynamics of Judicial Proof, pp. 81–117. Springer, Berlin (2002)Google Scholar
  14. 14.
    Zadeh, L.A.: Precisiated natural language (PNL). AI Mag. 25(3), 74 (2004)Google Scholar
  15. 15.
    Zadrozny, S., Kacprzyk, J.: Computing with words for text processing: an approach to the text categorization. Inf. Sci. 176(4), 415–437 (2006)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer EngineeringZHCET, Aligarh Muslim UniversityAligarhIndia

Personalised recommendations