Open Access Open Access  Restricted Access Subscription or Fee Access

Semantic Relation Based Page Ranking Using Genetic Algorithm and Conceptual Graph

J. Avanija, Dr.K. Ramar


With the massive growth and large volume of the web it is the function of search engine to recover results based on the user preferences. But most of the time the user gets useless pages. The next generation web architecture, semantic web reduces the burden of the user by performing search based on semantics instead of keywords. A conceptual graph (CG) is a notation for logic based on the existential graphs and the semantic networks of artificial intelligence. Conceptual graphs are formally defined in an abstract syntax that is independent of any notation, but the formalism can be represented in several different concrete notations.Conceptual graph representation is used in the conservation of text to semantic relations in order to minimize the edges. In this paper, we propose a relation based page ranking algorithm using genetic algorithm and conceptual graph which can be used along with semantic search engine. The proposed method uses Jena API and GATE tool API and the documents can be recovered based on their annotation features and relations. A preliminary experiment shows that the proposed method generates relevant documents in higher ranking.


Component, Conceptual Graph(CG), Gate Tool API, Jena API, Semantic Web

Full Text:



L.Ding,T.Finn,A.Joshi,R.Pan,R.S.Cost,Y.Peng,P.Reddivari,V.Doshi and J.Sachs, “ Search on the Semantic Web”,ACM int’l conf on information and knowledge management pp 652-659,2004.

Ahu SIeg, Bamshad Mobasher and Robin Burke, “Learning Ontology -Based User Profiles: A Semantic Approach to Personalized Web Search”, IEEE Intelligent Informatics Bulletin, 8,pp.7- 18,2007.

Mehrnoush Shamsfard, Azadeh Nematzadeh and Sarah Motiee, “Rank:An Ontology Based System for Ranking documents”,International Journal of Computer Science, 1,225- 231, 2006.

Sun Kim , Byoung-Tak Zhang, “ Genetic Mining of HTML Structures for effective Web Document Retrieval”, Applied Intelligence, 18, pp.243-256, 2003.

Wang Wei, Payam M.Barjaghi, Andrzej Bargiela, “Semantic enhanced information search and retrieval”,Sixth International Conference on Advance Language and Web Information Technology, pp218-223,2007.

L.Ding,T.Finn,A.Joshi,R.Pan,R.S.Cost,Y.Peng,P.Reddivari,V.Doshi and J.Sachs, “Swoogle:A Search and metadata engine on the Semantic Web”,Computer,vol38,no 10,pp 62-69,Oct 2005.

K.Anyanwu, A.Maduko,and A.Sheth,”SemRank: RankingComplex Relation Search and Results on the semantic web”,Proc 14th int’l conf WWW pp117-127 2005.

R.Guha, R.McCool and E.Miller, “Semantic Search”,Proc 12th Int’l ConfWWW pp700-709 2003.

Yufei Li,Yuan Wang, and xiaotao Huang, “A Relation Based Search Engine in Semantic Web”, IEEE Trans. Knowledge and Data Engg. Vol19.no2,pp 273-282, Feb 2007.

T.Preibe,C.Schlanger and G.Pernul, “A Search Engine for RDF Metadata”,Proc. 15th Int’l Workshop Databse and Expert Systems Applications pp168-172,2004.

B.Aleman-Meza,C Halaschek, I.Arpinar, and A.Sheth, “A Context_Aware Semantic Association Ranking”,Proc.First Int’l Workshop Semantic Web and Databses pp33-50,2003.

Fabrizio Lamberti, Claudio Demartini “A Relation Based Page Rank Algorithm for Semantic Web Search Engines”, IEEE Trans. Knowledge and Data Engg. Vol21.no1,pp 123-136, Jan 2009.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.