Open AccessMeta Search Engine using Semantic Similarity and Correlation Coefficient
Author(s) -
Naresh Kumar,
Deepak Kumar Sharma,
Nripendra Narayan Das
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.i7261.079920
Subject(s) - search engine , metasearch engine , information retrieval , computer science , ranking (information retrieval) , semantic search , web search query , search oriented architecture , search analytics , search engine indexing , query expansion , spamdexing , rank (graph theory) , data mining , mathematics , combinatorics
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.