Implementation of meta search engine with different rank aggregation algorithms

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.

Implementation of meta search engine with different rank aggregation algorithms

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.

Meta search engine

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.

Meta search engine

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.

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

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.

Meta Search Engine using Semantic Similarity and Correlation Coefficient

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.