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World Digital Libraries
Year : 2024, Volume : 17, Issue : 2
First page : ( 139) Last page : ( 152)
Print ISSN : 0974-567X. Online ISSN : 0975-7597.
Article DOI : 10.18329/09757597/2024/17210

Information retrieval and taxonomy: A comparative analysis of select search engines

Dr Hadagali G. S.1, Hiremath G. R.2

1Professor Department of Library and Information Science, Karnatak University, Dharwad, Karnataka - 580 003, (E): gururajhadagali123@gmail.com

2Department of Library and Information Science, Karnatak University, Dharwad, Karnataka - 580 003, (E): geetarh72@gmail.com

Online published on 27 March, 2025.

Abstract

The study that forms the subject matter of this article focused on the comparative analysis of some select search engines based on the information retrieval process and the taxonomy concepts. The major amount of literature was sourced from the related websites. The websites like searchenginewatch.com, search engine showdown.com, and searchenginez.com, etc. were a major help in the study, as they provided all the major search engines’ reviews and necessary links. The study indicated that the Google is the most popular search engine and ranks first because of its advanced algorithms and search techniques, such as PageRank, query understanding and natural language processing; all the three search engines include featured snippets, image packs, video carousels, and advertising and the Google employs more IR models (11) followed by the Bing (7) and the Yahoo (6). The present study would help users to appreciate and select a search engine appropriate to their specific search needs. Such studies also facilitate the web search engine developers to suggest improvements in the web search engines. The study considered the top three search engines based on the eBizMBA Rankings such as the Google, the Bing and the Yahoo. The parameters used in the study are: IR models, techniques used, search features, taxonomical features, special features, and search techniques.

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Keywords

Bing, Google, Information-retrieval models, Search engine optimization, Search engines, Taxonomy, Yahoo.

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