Hybrid Fuzzy Neural Search Retrieval System

Hybrid Fuzzy Neural Search Retrieval System

Rawan Ghnemat (Princess Sumaya University for Technology, Jordan) and Adnan Shaout (The University of Michigan – Dearborn, USA)
Copyright: © 2017 |Pages: 16
DOI: 10.4018/978-1-5225-1908-9.ch020
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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.
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In Web mining (Jiang, Pei, & Li, 2013) the miner tries to benefit from the data created through the sessions of surfing the Web or behavior of this surfing. Although Web content and structure mining use real and primary data on the Web. Web usage mining extracts also from the secondary data originated as a result of the user interactions during Web sessions. Information in Web usage covers data from server access logs, proxy server logs, browser logs, to user profiles, registration data, user sessions or transactions, cookies, user queries, bookmark data, mouse clicks and scrolls, and any other data as the results of interactions (Umagandhi & Kumar, 2013).

Intelligent Web mining supported by semantic based search leads to a useful pattern for better search process (Bollegala, Matsuo, & Ishizuka, 2011). This area of research addresses the optimization of the structure and the connection of the Web sites (Yang, Sun, Tang, Ma, & Li, 2015).

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