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Entity-based Semantic Association Ranking on the Semantic Web

by S. Narayana, S. Sivaleela, A. Govardhan, G. P. S. Varma
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 69 - Number 20
Year of Publication: 2013
Authors: S. Narayana, S. Sivaleela, A. Govardhan, G. P. S. Varma
10.5120/12091-8339

S. Narayana, S. Sivaleela, A. Govardhan, G. P. S. Varma . Entity-based Semantic Association Ranking on the Semantic Web. International Journal of Computer Applications. 69, 20 ( May 2013), 42-46. DOI=10.5120/12091-8339

@article{ 10.5120/12091-8339,
author = { S. Narayana, S. Sivaleela, A. Govardhan, G. P. S. Varma },
title = { Entity-based Semantic Association Ranking on the Semantic Web },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 69 },
number = { 20 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 42-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume69/number20/12091-8339/ },
doi = { 10.5120/12091-8339 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:30:51.787073+05:30
%A S. Narayana
%A S. Sivaleela
%A A. Govardhan
%A G. P. S. Varma
%T Entity-based Semantic Association Ranking on the Semantic Web
%J International Journal of Computer Applications
%@ 0975-8887
%V 69
%N 20
%P 42-46
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The major focus of today's search engines is efficient retrieval of relevant documents from the web. Recently Semantic Web has received greater interest in industry and academia and retrieving relevant information over huge amounts of Semantic Meta data is becoming popular. In particular discovering and ranking complex relationships between two entities over Semantic Meta data became a challenging research topic. Semantic Associations capture complex relationships between two entities in an RDF knowledge base. Given two entities, there exist a huge number of Semantic Associations between entities. Moreover these associations pass through one more intermediate entity. Hence ranking of associations is required in order to get relevant associations. This paper proposes an approach to discover and rank Semantic Associations between two entities based on the user interest. User interest is captured by selecting one or more entities from the user interface. The effectiveness of the ranking method is demonstrated using Spearman Foot rule coefficient. The results show that the proposed ranking is highly correlated with human ranking.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Semantic Web Semantic Association Complex relationship RDF Ontology

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