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A neuro-fuzzy algorithm for query expansion and information retrieval

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Abstract

Query Expansion (QE) has developed as a critical solution to address the perennial challenges of search accuracy and relevance in the information retrieval domain. In this article, a novel optimized neuro-fuzzy-based QE expansion framework was designed using a recurrent neural network (RNN) and fuzzy logic system (FLS). The document corpus and related queries are collected from standard sites and fed into the system. The dataset was pre-processed, and feature extraction was performed using the Term Frequency and Inverse Document Frequency (TF-IDF) technique. The RNN in the QE module estimates the relevance probability relative to the input query, and the FLS was designed to make decisions regarding the query expansion. Finally, the dragonfly optimization algorithm (DOA) was utilized to optimize the performances of the neuro-fuzzy module. The presented framework was experimentally trained and tested with the CISI dataset, and the results are estimated. Furthermore, a comparative assessment was performed using existing techniques to verify the results of the proposed work. The comparative analysis proves that the proposed model attained greater results than the conventional models in terms of precision, recall, f-measure, and MAP.

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Authors and Affiliations

  1. Department of CSE, Uttarakhand Technical University, Sudhowala, Uttarakhand, 248007, India

    Kanika mittal

  2. Department of CSE, B.T. Kumaon Institute of Technology, Dwarahat, Uttarakhand, 263653, India

    Kunwar Singh Vaisla

  3. Department of CSE, Netaji Subash University of Technology, Geeta Colony, Delhi, 110031, India

    Amita Jain

Authors
  1. Kanika mittal
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  2. Kunwar Singh Vaisla
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  3. Amita Jain
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Correspondence to Kanika mittal.

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mittal, K., Vaisla, K.S. & Jain, A. A neuro-fuzzy algorithm for query expansion and information retrieval. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19662-2

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  • DOI: https://doi.org/10.1007/s11042-024-19662-2

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