Optimizing Ontology Alignment through Improved NSGA-II
Joint Authors
Xue, Xingsi
Huang, Yikun
Jiang, Chao
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-06-19
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Over the past decades, a large number of complex optimization problems have been widely addressed through multiobjective evolutionary algorithms (MOEAs), and the knee solutions of the Pareto front (PF) are most likely to be fitting for the decision maker (DM) without any user preferences.
This work investigates the ontology matching problem, which is a challenge in the semantic web (SW) domain.
Due to the complex heterogeneity between two different ontologies, it is arduous to get an excellent alignment that meets all DMs’ demands.
To this end, a popular MOEA, i.e., nondominated sorting genetic algorithm (NSGA-II), is investigated to address the ontology matching problem, which outputs the knee solutions in the PF to meet diverse DMs’ requirements.
In this study, for further enhancing the performance of NSGA-II, we propose to incorporate into NSGA-II’s evolutionary process the monkey king evolution algorithm (MKE) as the local search algorithm.
The improved NSGA-II (iNSGA-II) is able to better converge to the real Pareto optimum region and ameliorate the quality of the solution.
The experiment uses the famous benchmark given by the ontology alignment evaluation initiative (OAEI) to assess the performance of iNSGA-II, and the experiment results present that iNSGA-II is able to seek out preferable alignments than OAEI’s participators and NSGA-II-based ontology matching technique.
American Psychological Association (APA)
Huang, Yikun& Xue, Xingsi& Jiang, Chao. 2020. Optimizing Ontology Alignment through Improved NSGA-II. Discrete Dynamics in Nature and Society،Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1153507
Modern Language Association (MLA)
Huang, Yikun…[et al.]. Optimizing Ontology Alignment through Improved NSGA-II. Discrete Dynamics in Nature and Society No. 2020 (2020), pp.1-8.
https://search.emarefa.net/detail/BIM-1153507
American Medical Association (AMA)
Huang, Yikun& Xue, Xingsi& Jiang, Chao. Optimizing Ontology Alignment through Improved NSGA-II. Discrete Dynamics in Nature and Society. 2020. Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1153507
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1153507