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Hybrid Fuzzy Neural Search Retrieval System

Author

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  • Rawan Ghnemat

    (Computer Science Department, King Hussein Faculty for Computing Sciences, Princess Sumaya University for Technology, Amman, Jordan)

  • Adnan Shaout

    (The Electrical and Computer Engineering Department, The University of Michigan – Dearborn, Dearborn, MI, USA)

Abstract

Search engines are crucial for information gathering systems (IGS). New challenges face search engines concerning automatic learning from user requests. In this paper, a new hybrid intelligent system is proposed to enhance the search process. Based on a Multilayer Fuzzy Inference System (MFIS), the first step is to implement a scalable system to relay logical rules in order to produce three classifications for search behavior, user profiles, and query characteristics from analysis of navigation log files. These three outputs from the MFIS are used as inputs for the second step, an Adaptive Neuro-Fuzzy Inference System (ANFIS). The training process of the ANFIS replaced the rules by adjusting the weights in order to find the most relevant result for the search query. This proposed system, called MFIS-ANFIS, is implemented as an experimental system. The system performance is evaluated using quantitative and comparative analysis. MFIS-ANFIS aimed to be the core of intelligent and reliable search process.

Suggested Citation

  • Rawan Ghnemat & Adnan Shaout, 2016. "Hybrid Fuzzy Neural Search Retrieval System," International Journal of Enterprise Information Systems (IJEIS), IGI Global, vol. 12(3), pages 1-16, July.
    • Handle: RePEc:igg:jeis00:v:12:y:2016:i:3:p:1-16
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