Proximity Matrix Completion and Ranking Ant Colony Optimization technique in Semantic web
SRubin Thottupurathu Jose1, Sojan Lal Poulose2
1Rubin Thottupurathu Jose, School of Computer Sciences, M G University, Kottayam, Kerala, India.
2Dr Sojan Lal Poulose, Principal, Mar-Baselious Institute of Technology and Science, Kothamangalam, Kerala, India.
Manuscript received on 1 August 2019. | Revised Manuscript received on 8 August 2019. | Manuscript published on 30 September 2019. | PP: 797-802 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4021098319/19©BEIESP | DOI: 10.35940/ijrte.C4021.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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%.
Index Terms: Proximity Matrix Completion technique, query flexibility, query relevancy, Ranking Ant Colony Optimization and Semantic web.
Scope of the Article: Semantic Web