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Reseach Article

Discovering Relevant Semantic Associations using Relationship Weights

by S. Narayana, G. P. S. Varma, A. Govardhan
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

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 = { },
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

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.

  1. Aleman-Meza Bonerges, Halaschek-Wiener Christian, Arpinar IB, Ramakrishnan Cartic, Sheth Amit (2005). Ranking Complex Relationships on the Semantic Web. IEEE Internet Computing 9(3); 37-44. Doi:10. 1109/MIC. 200. 63.
  2. Aleman-Meza, B. , Halaschek, C. , Arpinar, I. B. , and Sheth, A. "Context-Aware Semantic Association Ranking. " First International Workshop on Semantic Web and Databases, Berlin, Germany, 33-50
  3. Sheth, A. P. , Aleman-Meza, B. , Arpinar, I. B. , Halaschek, C. , Ramakrishnan, C. , Bertram, C. , Warke, Y. , Avant, D. , Arpinar, F. S. , Anyanwu, K. , and Kochut, K. (2005a). "Semantic Association Identification and Knowledge Discovery for National Security Applications. " Journal of Database Management, 16(1), 33-53.
  4. Halaschek, C. , Aleman-Meza, B. , Arpinar, I. B. , and Sheth, A. P. "Discovering and Ranking Semantic Associations over a Large RDF Metabase. " 30th International Conference on Very Large Data Bases, Toronto, Canada.
  5. V Viswanathan, K Ilango: Ranking semantic relationships between two entities using personalization in contest specification. Informain Sciences, Elsevier, 207 (2012) 35-49.
  6. Anyanwu Kemafor, Angela Maduko, Sheth Amit. SemRank: ranking complex relationship search results on the Semantic Web, in: Proc. of the 14th International World Wide Web Conference, ACM Press, 2005, pp. 117–127.
  7. Anyanwu Kemafor, Sheth Amit. ?-operator: Discovering and Ranking Semantic Associations on the Semantic Web, ACM SIGMOD Record, v. 31 n. 4, December 2002.
  8. Shahdad Shariatmadari, Ali Mamat, Ibrahim Hamidah, Mustapha Norwati (2008). SwSim:Discovering semantic similarity association in semantic web. Proceedings of International Symposium on ITSim 2008, 1-4.
  9. Myungjin Lee, Wooju Kim. Semantic association search and rank method based on spreading activation for the Semantic Web, in: IEEE International Conference on Industrial Engineering and Engineering Management, 2009, pp. 1523.
  10. Myungjin Lee, Wooju Kim, Sangun Park. Searching and ranking method of relevant resources by user intention on the Semantic Web, Expert Systems with Applications 39 (2012) 4111–4121.
  11. M. -E. Vidal, L. Rashid, L. Ibabez, J. Rivera, H. Rodrogiez, E. Ruckhaus, A ranking-based approach to discover semantic association between linked data, in: The 2nd International Workshop on Inductive Reasoning and Machine learning for the Semantic Web, 2010, pp. 18-29.
  12. Berners-Lee, T. , Hendler, J. , and Lassila, O. (2001). "The Semantic Web - A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities. " Scientific American, 284(5), 34.
  13. Lassila Ora and Swick R. Resource Description Framework (RDF) Model and Syntax Specification, W3C Recommendation. 1999.
  14. Brickley D and Guha RV. Resource Description Framework (RDF) Schema Specification 1. 0, W3C Candidate Recommendation. 2000.
  15. Web Ontology Language, http://www. w3. org/2004/OWL/, 2004.
  16. Aleman-Meza, B. , Halaschek, C. , Sheth, A. , Arpinar, I. B. , and Sannapareddy, G. "SWETO: Large-Scale Semantic Web Test-bed. " 16th International Conference on Software Engineering and Knowledge Engineering (SEKE2004): Workshop on O1ntology in Action, Banff, Canada, 490-493.
  17. Guha, R. V. , and McCool, R. (2003). "TAP: A Semantic Web Test-bed. " Journal of Web Semantics, 1(1), 81-87.
  18. Opencyc. http://sw. opencyc. org
  19. Sheth, A. P. , Arpinar, I. B. , and Kashyap, V. (2003). "Relationships at the Heart of Semantic Web: Modelling, Discovering and Exploiting Complex Semantic Relationships. " Enhancing the Power of the Internet Studies in Fuzziness and Soft Computing, M. Nikravesh, B. Azvin, R. Yager, and L. A. Zadeh, eds. , Springer-Verlag. 2003.
  20. Fabrizio Lamberti, Member, IEEE, Andrea Sanna, and Claudio Demartini. A Relation-Based Page Rank Algorithm for Semantic Web Search Engines, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 21, NO. 1, JANUARY 2009, pp. 123-136.
Index Terms

Computer Science
Information Sciences


Complex Relationship OWL RDF RDFS Semantic Web Semantic Association.