scholarly journals Implementation of meta search engine with different rank aggregation algorithms

2021 ◽  
Author(s):  
Sharjeel Arshad
Keyword(s):  
Performance Improvement ◽  
Search Engine ◽  
Search Engines ◽  
Rank Aggregation ◽  
Meta Search Engine ◽  
Single List ◽  
Meta Search

The development of a Meta Search engine has been described and the ranking of the queries has been accomplished by implementing four rank aggregation algorithms. Meta Search Engine is used to combine different lists for the same query by different search engines into a single list so as to return the most relevant results with a wider coverage in the quickest possible time. The performance improvement is achieved by testing four rank aggregation algorithms namely: 1) Linear 2) Exponential 3) Borda Fuse 4) Condorcet Fuse. The efficiency of each algorithm in terms of accuracy and time has been compared.

scholarly journals Implementation of meta search engine with different rank aggregation algorithms

2021 ◽  
Author(s):  
Sharjeel Arshad
Keyword(s):  
Performance Improvement ◽  
Search Engine ◽  
Search Engines ◽  
Rank Aggregation ◽  
Meta Search Engine ◽  
Single List ◽  
Meta Search

The development of a Meta Search engine has been described and the ranking of the queries has been accomplished by implementing four rank aggregation algorithms. Meta Search Engine is used to combine different lists for the same query by different search engines into a single list so as to return the most relevant results with a wider coverage in the quickest possible time. The performance improvement is achieved by testing four rank aggregation algorithms namely: 1) Linear 2) Exponential 3) Borda Fuse 4) Condorcet Fuse. The efficiency of each algorithm in terms of accuracy and time has been compared.

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.

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.

Towards Improving Meta-Search through Exploiting an Integrated Search Model

2011 ◽  
Vol 10 (04) ◽  
pp. 379-391
Author(s):  
Mohammed Maree ◽  
Saadat M. Alhashmi ◽  
Mohammed Belkhatir
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
General Purpose ◽  
Search Model ◽  
Domain Specific ◽  
Meta Search Engine ◽  
Meta Search ◽  
Results Merging ◽  
Multiple Domain

Meta-search engines are created to reduce the burden on the user by dispatching queries to multiple search engines in parallel. Decisions on how to rank the returned results are made based on the query's keywords. Although keyword-based search model produces good results, better results can be obtained by integrating semantic and statistical based relatedness measures into this model. Such integration allows the meta-search engine to search by meanings rather than only by literal strings. In this article, we present Multi-Search+, the next generation of Multi-Search general-purpose meta-search engine. The extended version of the system employs additional knowledge represented by multiple domain-specific ontologies to enhance both the query processing and the returned results merging. In addition, new general-purpose search engines are plugged-in to its architecture. Experimental results demonstrate that our integrated search model obtained significant improvement in the quality of the produced search results.

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 Recommendation-Based Meta-Search Engine for Suggesting Relevant Documents Links

2020 ◽  
Vol 16 (4) ◽  
pp. 86-99
Author(s):  
A. Salman Ayaz ◽  
Jaya A Venkat ◽  
Zameer Gulzar
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Irrelevant Information ◽  
Training Data ◽  
Data Set ◽  
Web Based ◽  
The Third ◽  
Meta Search Engine ◽  
Meta Search ◽  
Higher Education Organizations

The information available online is mostly present in an unstructured form and search engines are indispensable tools especially in higher education organizations for obtaining information from the Internet. Various search engines were developed to help learners to retrieve the information but unfortunately, most of the information retrieved is not relevant. The main objective of this research is to provide relevant document links to the learners using a three-layered meta-search architecture. The first layer retrieves information links from the web based on the learner query, which is then fed to the second layer where filtering and clustering of document links are done based on semantics. The third layer, with the help of a reasoner, categorizes information into relevant and irrelevant information links in the repository. The experimental study was conducted on a training data set using web queries related to the domain of sports, entertainment, and academics. The results indicate that the proposed meta-search engine performs well as compared to another stand-alone search engine with better recall.

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.

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.

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