Wuhan Univ. J. Nat. Sci.
Volume 27, Number 3, June 2022
|Page(s)||240 - 254|
|Published online||24 August 2022|
CLC number: TP 182
Improving the Efficiency of Multi-Objective Grasshopper Optimization Algorithm to Enhance Ontology Alignment
School of Computer Science, Wuhan University, Wuhan
430072, Hubei, China
† To whom correspondence should be addressed. E-mail: firstname.lastname@example.org
Ontology alignment is an essential and complex task to integrate heterogeneous ontology. The meta-heuristic algorithm has proven to be an effective method for ontology alignment. However, it only applies the inherent advantages of meta-heuristics algorithm and rarely considers the execution efficiency, especially the multi-objective ontology alignment model. The performance of such multi-objective optimization models mostly depends on the well-distributed and the fast-converged set of solutions in real-world applications. In this paper, two multi-objective grasshopper optimization algorithms (MOGOA) are proposed to enhance ontology alignment. One is ε-dominance concept based GOA (EMO-GOA) and the other is fast Non-dominated Sorting based GOA (NS-MOGOA). The performance of the two methods to align the ontology is evaluated by using the benchmark dataset. The results demonstrate that the proposed EMO-GOA and NS-MOGOA improve the quality of ontology alignment and reduce the running time compared with other well-known metaheuristic and the state-of-the-art ontology alignment methods.
Key words: ontology alignment / multi-objective grasshopper optimization algorithm / ε-dominance / fast non-dominated sorting / knowledge integration
Biography: LV Zhaoming, male,Ph. D. candidate, research direction:knowledge engineering, ontology matching and swarm intelligence. E-mail:email@example.com
Foundation item: Supported by the Ministry of Education-China Mobile Joint Fund Project (MCM2020J01)
© Wuhan University 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.