scholarly journals Research on Mechanism and Challenges in Meta Search Engines

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.

scholarly journals Meta Search Engine using Semantic Similarity and Correlation Coefficient

2020 ◽  
Vol 9 (9) ◽  
pp. 643-647
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Search Space ◽  
The Other ◽  
Ranking Algorithm ◽  
Search Results ◽  
Ranking Algorithms ◽  
Meta Search Engine ◽  
User Query ◽  
Meta Search

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.

scholarly journals A Study of Various Result Merging Strategies for a Meta Search Engine

2018 ◽  
Vol 7 (3.6) ◽  
pp. 255
Author(s):  
R R. Sathiya ◽  
A G. Jayasree ◽  
Raghuvamsi Tangirala ◽  
Damerla Prasanna
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Single View ◽  
Meta Search Engine ◽  
Result Merging ◽  
Meta Search ◽  
Merging Techniques ◽  
Day By Day

As the amount of data is growing day by day, the sources for these data are also growing simultaneously and to search through this very data, we need the use of search engines. Since each search engine is limited to its confined set of data, it would be even better to make use of a Meta search engine which will give us more relevant results than the ones obtained from any single search engine. It acts as an interface that provides the user with a single view from the various underlying search engines. The data is collected from these underlying search engines after they are accessed with the processed query from the Meta search engine. The collected data is merged using an algorithm and the algorithm will be a major factor in giving the best possible results. In this paper, we are going to discuss about the various existing metasearch engines and the different merging techniques and their approaches.

scholarly journals Meta search engine

2021 ◽  
Author(s):  
Kwok-Pun Chan
Keyword(s):  
Foreign Language ◽  
Search Engine ◽  
Waiting Time ◽  
Search Engines ◽  
Time Frame ◽  
Search Result ◽  
Meta Search Engine ◽  
Meta Search ◽  
Biased Information

Meta search engines allow multiple engine searches to minimize biased information and improve the quality of the results it generates. However, existing meta engine applications contain many foreign language results, and only run on Windows platform. The meta search engine we develop will resolve these problems. Our search engine will run on both Windows and Linus platforms, and has some desirable properties: 1) users can shorten the search waiting time if one of the search engines is down 2) users can sort the result titles in an alphabetic or relevancy order. Current meta search websites only allow users to sort results by relevancy. Our search engine allows users to do an alphabetical search from the previous relevancy search result, so that the users can identify the required title within a shorter time frame.

Finding information on the World Wide Web: A specialty meta–search engine for the academic community

First Monday ◽  
2005 ◽  
Author(s):  
Yaffa Aharoni ◽  
Ariel Frank ◽  
Snunith Shoham
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Information Needs ◽  
Academic Community ◽  
Medical Community ◽  
Basic Premise ◽  
Research Areas ◽  
Meta Search Engine ◽  
Meta Search ◽  
The Web

The Web is continuing to grow rapidly and search engine technologies are evolving fast. Despite these developments, some problems still remain, mainly, difficulties in finding relevant, dependable information. This problem is exacerbated in the case of the academic community, which requires reliable scientific materials in various specialized research areas. We propose that a solution for the academic community might be a meta–search engine which would allow search queries to be sent to several specialty search engines that are most relevant for the information needs of the academic community. The basic premise is that since the material indexed in the repositories of specialty search engines is usually controlled, it is more reliable and of better quality. A database selection algorithm for a specialty meta–search engine was developed, taking into consideration search patterns of the academic community, features of specialty search engines and the dynamic nature of the Web. This algorithm was implemented in a prototype of a specialty meta–search engine for the medical community called AcadeME. AcadeME’s performance was compared to that of a general search engine — represented by Google, a highly regarded and widely used search engine — and to that of a single specialty search engine — represented by the medical Queryserver. From the comparison to Google it was found that AcadeME contributed to the quality of the results from the point of view of the academic user. From the comparison to the medical Queryserver it was found that AcadeMe contributed to relevancy and to the variety of the results as well.

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