The classification of search results in the meta-search engine

This paper aims to provide an intelligent way to query and rank the results of a Meta Search Engine. A Meta Search Engine takes input from the user and produces results which are gathered from other search engines. The main advantage of a Meta Search Engine over methodical search engine is its ability to extend the search space and allows more resources for the user. The semantic intelligent queries will be fetching the results from different search engines and the responses will be fed into our ranking algorithm. Ranking of the search results is the other important aspect of Meta search engines. When a user searches a query, there are number of results retrieved from different search engines, but only several results are relevant to user's interest and others are not much relevant. Hence, it is important to rank results according to the relevancy with user query. The proposed paper uses intelligent query and ranking algorithms in order to provide intelligent meta search engine with semantic understanding.

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Research on Mechanism and Challenges in Meta Search Engines

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i1057.0789s219 ◽

2019 ◽

Vol 8 (9S2) ◽

pp. 281-284

Keyword(s):

Neural Networks ◽

Search Engine ◽

Search Engines ◽

Clustering Technique ◽

Single Interface ◽

Search Results ◽

Meta Search Engine ◽

Result Merging ◽

Meta Search ◽

Merging Algorithms

A Meta Search Engine (MSE) produces results gathered from other search engine (SE) on a given query. In brief MSEs have single interface corresponding to multiple searches. MSE employs their own algorithm to display search results. This paper reviews existing Meta Search Engines like Yippy, eTools.ch, Carrot2, qksearch and iBoogie commonly used for searching. This paper surveys and analysed the working of different result merging algorithms. Current research reviews MSE based on different approaches like clustering technique. Few MSEs are employing Neural networks for searching. Further it also discusses problem in existing MSEs.

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A Text Similarity Meta-Search Engine Based on Document Fingerprints and Search Results Records

2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology ◽

10.1109/wi-iat.2011.27 ◽

2011 ◽

Cited By ~ 4

Author(s):

Felipe Bravo-Marquez ◽

Gaston L'Huillier ◽

Sebasti´n A. Rios ◽

Juan D. Vel´squez

Keyword(s):

Search Engine ◽

Text Similarity ◽

Search Results ◽

Meta Search Engine ◽

Meta Search

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The Research of a Multi-Language Supporting Description-Oriented Clustering Algorithm on Meta-Search Engine Result

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.151.549 ◽

2012 ◽

Vol 151 ◽

pp. 549-553

Author(s):

Wu Ling Ren ◽

Li Juan Liu

Keyword(s):

Search Engine ◽

Clustering Algorithm ◽

Search Results ◽

Meta Search Engine ◽

Search Engine Result ◽

Linear List ◽

Data Source ◽

Meta Search ◽

Chinese And English ◽

Great Burden

Search engine has adopted a variety of techniques to improve the accuracy of information retrieval, but the way of a linear list of search engine results, which mixes unrelated documents with relevant documents, has brought user great burden. This article commits to build clustering of search results, which is based on meta search engine techniques. We use all the popular search engine as a data source, then after a certain pre-processing of the source search engine, hierarchical clustering results is formed and returned to the query users. we propose a multi-language supporting, label first clustering algorithm, which we named DCFC algorithm. This algorithm supports both Chinese and English query, focuses on generating human readable labels, shows search results in hierarchical structure.

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