Optimal Query Expansion Based on Hybrid Group Mean Enhanced Chimp Optimization Using Iterative Deep Learning

Abstract

The internet is surrounded by uncertain information which necessitates the usage of natural language processing and soft computing techniques to extract the relevant documents. The relevant results are retrieved using the query expansion technique which is mainly formulated using the machine learning or deep learning concepts in the existing literature. This paper presents a hybrid group mean-based optimizer-enhanced chimp optimization (GMBO-ECO) algorithm for pseudo-relevance-based query expansion, whereby the actual queries are expanded with their related keywords. The hybrid GMBO-ECO algorithm mainly expands the query based on the terms that have a strong interrelationship with the actual query. To generate the word embeddings, a Word2Vec paradigm is used which learns the word association from large text corpora. The useful context in the text is identified using the improved iterative deep learning framework which determines the user’s intent for the current web search. This step reduces the mismatch of the words and improves the performance of query retrieval. The weak terms are eliminated and the candidate query terms for optimal query expansion are improved via an Okapi measure and cosine similarity techniques. The proposed methodology has been compared to the state-of-the-art methods with and without a query expansion approach. Moreover, the proposed optimal query expansion technique has shown a substantial improvement in terms of a normalized discounted cumulative gain of 0.87, a mean average precision of 0.35, and a mean reciprocal rank of 0.95. The experimental results show the efficiency of the proposed methodology in retrieving the appropriate response for information retrieval. The most common applications for the proposed method are search engines.

An automation query expansion strategy for information retrieval by using fuzzy based grasshopper optimization algorithm on medical datasets

“…The IAOCOOT model offered a PR curve score of 0.85 when evaluated using the WordNet, Wikipedia, and Text REtrieval Conference datasets. Kumar et al 37 also developed an optimal query expansion scheme using the hybrid group mean‐based optimizer‐enhanced chimp optimization (GMBO‐ECO) algorithm for PRF‐based query expansion. The Word2Vec strategy is employed here to learn the word representations from the large datasets.…”

“…These authors 35–37,40–44 mainly applied the GOA algorithm due to its ability to preserve the feasibility of newly generated solutions. When compared to the PSO and BI algorithms, the GOA algorithm improves the capability of the FLC in handling high‐dimensional problems and overcoming local minima issues.…”

“…Kumar et al 37 also developed an optimal query expansion scheme using the hybrid group mean-based optimizer-enhanced chimp optimization (GMBO-ECO) algorithm for PRF-based query expansion. The Word2Vec strategy is employed here to learn the word representations from the large datasets.…”

“…Fuzzy Logic Controller gains wide popularity due to its application in different complicated design mechanisms such as the Wireless Sensor Networks (WSN), 40 ground motion prediction, 41 Medical image fusion, 42 disease dataset feature selection, 43 big data sonar classification, 44 and vibration control of complex systems. 35 These authors [35][36][37][40][41][42][43][44] mainly applied the GOA algorithm due to its ability to preserve the feasibility of newly generated solutions. When compared to the PSO and BI algorithms, the GOA algorithm improves the capability of the FLC in handling high-dimensional problems and overcoming local minima issues.…”