Open AccessProximity Matrix Completion and Ranking Ant Colony Optimization technique in Semantic web
Author(s) -
Rubin Thottupurathu Jose,
Dr Sojan Lal Poulose
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c4021.098319
Subject(s) - computer science , ranking (information retrieval) , flexibility (engineering) , information retrieval , semantic web , data mining , ant colony optimization algorithms , query optimization , matrix completion , artificial intelligence , statistics , mathematics , physics , quantum mechanics , gaussian
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%