Hybrid Fuzzy Neural Search Retrieval System

Rawan Ghnemat, Adnan Shaout

Source Title: International Journal of Enterprise Information Systems (IJEIS)12(3)

ISSN: 1548-1115|EISSN: 1548-1123|EISBN13: 9781466689305|DOI: 10.4018/IJEIS.2016070105

MLA

Ghnemat, Rawan, and Adnan Shaout. "Hybrid Fuzzy Neural Search Retrieval System." IJEIS vol.12, no.3 2016: pp.1-16. http://doi.org/10.4018/IJEIS.2016070105

APA

Ghnemat, R. & Shaout, A. (2016). Hybrid Fuzzy Neural Search Retrieval System. International Journal of Enterprise Information Systems (IJEIS), 12(3), 1-16. http://doi.org/10.4018/IJEIS.2016070105

Chicago

Ghnemat, Rawan, and Adnan Shaout. "Hybrid Fuzzy Neural Search Retrieval System," International Journal of Enterprise Information Systems (IJEIS) 12, no.3: 1-16. http://doi.org/10.4018/IJEIS.2016070105

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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.