Proximity Matrix Completion and Ranking Ant Colony Optimization technique in Semantic web | Zendy

Rubin Thottupurathu Jose | Zendy; P. Sojan Lal | Zendy

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Proximity Matrix Completion and Ranking Ant Colony Optimization technique in Semantic web

Author(s) -

Rubin Thottupurathu Jose,

P. Sojan Lal

Publication year - 2019

Publication title -

international journal of recent technology and engineering (ijrte)

Language(s) - English

Resource type - Journals

ISSN - 2277-3878

DOI - 10.35940/ijrte.c4021.098319

Subject(s) - ranking (information retrieval) , computer science , information retrieval , flexibility (engineering) , semantic web , data mining , ant colony optimization algorithms , artificial intelligence , statistics , mathematics

The semantic web consists of a large number of data that is difficult to retrieve the answer for the user queries. An existing method in the query processing in the semantic web has three main limitations namely, query flexibility, query relevancy or lack of ranking method and high query cost. In this study, Proximity Matrix Completion technique (PMC) is applied to impute the missing data in the dataset that helps to increase the query flexibility and Ranking Ant Colony Optimization (RACO) technique is used to select the relevant features from the dataset and arrange them to increase relevancy. The result shows that the PMC-RACO method has a higher performance compared to the exiting method in semantic web. The mean precision value of the PMC-RACO method in sports data is 87%, while the existing method has the precision value of 83%

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