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Web Documents Ranked using Genetic Algorithm

by Poonam Chahal, Manjeet Singh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 70 - Number 22
Year of Publication: 2013
Authors: Poonam Chahal, Manjeet Singh
10.5120/12199-8313

Poonam Chahal, Manjeet Singh . Web Documents Ranked using Genetic Algorithm. International Journal of Computer Applications. 70, 22 ( May 2013), 18-21. DOI=10.5120/12199-8313

@article{ 10.5120/12199-8313,
author = { Poonam Chahal, Manjeet Singh },
title = { Web Documents Ranked using Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 70 },
number = { 22 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 18-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume70/number22/12199-8313/ },
doi = { 10.5120/12199-8313 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:33:32.571609+05:30
%A Poonam Chahal
%A Manjeet Singh
%T Web Documents Ranked using Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 70
%N 22
%P 18-21
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In recent years, semantic-based application has been using the Genetic Algorithm for solving the problem of information retrieval. The semantic search concept has been widely used in many fields like artificial intelligence, cognitive science, natural language processing, and psychology. In this paper we have proposed a ranking scheme for the semantic web documents by finding the semantic similarity between the documents and the query which is specified by the user using genetic algorithm. The novel approach proposed in this paper considers not only the conceptual level for finding the rank score of a document but also the descriptive level for finding the true semantics of the documents keeping the user view in mind. The combined use of conceptual and descriptive along with the genetic algorithm for providing the optimization for the ranking of the documents has significantly improved the performance of the proposed ranking scheme. We explore all the conceptual and descriptive scores using genetic algorithm for finding relevant relations between the keywords exploring the user's intention and then calculate the fraction of these relations on each web page to determine their relevance with respect to the query provided by the user.

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Index Terms

Computer Science
Information Sciences

Keywords

Semantic Web Ranking Parsing Syntactic Lexical Semantic Similarity

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