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Discovering Relevant Semantic Associations using Relationship Weights

by S. Narayana, G. P. S. Varma, A. Govardhan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 87 - Number 15
Year of Publication: 2014
Authors: S. Narayana, G. P. S. Varma, A. Govardhan
10.5120/15283-3913

S. Narayana, G. P. S. Varma, A. Govardhan . Discovering Relevant Semantic Associations using Relationship Weights. International Journal of Computer Applications. 87, 15 ( February 2014), 15-21. DOI=10.5120/15283-3913

@article{ 10.5120/15283-3913,
author = { S. Narayana, G. P. S. Varma, A. Govardhan },
title = { Discovering Relevant Semantic Associations using Relationship Weights },
journal = { International Journal of Computer Applications },
issue_date = { February 2014 },
volume = { 87 },
number = { 15 },
month = { February },
year = { 2014 },
issn = { 0975-8887 },
pages = { 15-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume87/number15/15283-3913/ },
doi = { 10.5120/15283-3913 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:05:59.665446+05:30
%A S. Narayana
%A G. P. S. Varma
%A A. Govardhan
%T Discovering Relevant Semantic Associations using Relationship Weights
%J International Journal of Computer Applications
%@ 0975-8887
%V 87
%N 15
%P 15-21
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Accessing relevant pages is the primary focus of the search techniques of Web 2. 0 whereas accessing relevant semantic associations is the main focus of search techniques of Web 3. 0 called the Semantic Web. Discovering relevant semantic associations is especially useful in many applications such as National Security, Business Intelligence, Pharmacy, and Genetics. Semantic associations are the complex relationships between two entities such as people, places, events, publications, organizations etc. They lend meaning to information, making it understandable and actionable, and provide new and possibly unexpected insights. One of the criteria to find relevant semantic associations is based on context which captures user's domain of interest. Existing methods defines context based on the concepts or regions selected from the ontology at the schema level but not based on user interested relationships. Due to this, sometimes user gets too many associations which further require a search for relevant associations. To overcome this problem, this paper proposes a method to define the context both based on user interested concepts and the relationships so that user can get more relevant associations. To experiment the proposed method, SWETO ontology has been used and the results show that the proposed method discovers more relevant semantic associations.

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Index Terms

Computer Science
Information Sciences

Keywords

Complex Relationship OWL RDF RDFS Semantic Web Semantic Association.

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