[1]申建朝,戴可涛,薛醒思,等.基于改进遗传规划算法的本体匹配方法[J].福建理工大学学报,2025,23(04):344-353.[doi:10.3969/j.issn.2097-3853.2025.04.006]
　SHEN Jianchao,DAI Ketao,XUE Xingsi,et al.Ontology matching method based on improved genetic programming algorithm[J].Journal of Fujian University of Technology;,2025,23(04):344-353.[doi:10.3969/j.issn.2097-3853.2025.04.006]
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基于改进遗传规划算法的本体匹配方法()
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《福建理工大学学报》[ISSN:2097-3853/CN:35-1351/Z]

卷:
第23卷
期数:
2025年04期
页码:
344-353
栏目:
出版日期:
2025-08-25

文章信息/Info

Title:
Ontology matching method based on improved genetic programming algorithm
作者:
申建朝; 戴可涛; 薛醒思; 李何; 吕青
潞安化工集团慈林山煤业有限公司夏店煤矿
Author(s):
SHEN Jianchao;  DAI Ketao;  XUE Xingsi;  LI He;  LYU Qing
Xiadian Coal Mine of Lu’an Chemical Group Cilinshan Coal Industry Co., Ltd.,
关键词:
本体匹配; 遗传规划; 精英选择; 种群多样性增强
Keywords:
ontology matching;  genetic programming;  elite selection;  population diversity enhancement
分类号:
TP301.6
DOI:
10.3969/j.issn.2097-3853.2025.04.006
文献标志码:
A
摘要:
受到遗传规划(genetic programming, GP)在特征构建领域成功应用的启发,提出一种基于改进遗传规划算法的本体匹配方法(ESMPD-GP)。为了解决GP个体简化压力问题、避免算法在进化过程中陷入局部最优,ESMPD-GP 采用了两种新的算法组件,即双目标精英选择机制和种群多样性增强策略。前者通过同时考虑个体适应度和编辑距离,选出种群中多样性最强的个体集合;后者通过创建额外种群引入优秀解简化压力,增加种群多样性、防止早熟收敛。基于国际本体匹配竞赛上Benchmark和Anatomy 测试集的实验结果表明,所提方法在Benchmark 上的f-measure 平均值为0.96,优于现有先进方法。
Abstract:
Inspired by the successful application of genetic programming (GP) in the field of feature construction, an ontology matching method based on improved genetic programming algorithm (ESMPD-GP) is proposed. In order to solve the problem of GP individual simplification pressure and avoid the algorithm falling into local optimum in the evolution process, ESMPD-GP adopts two new algorithm components, namely, dual-objective elite selection mechanism and population diversity enhancement strategy. The former selects the individual set with the strongest diversity in the population by considering both individual fitness and edit distance. The latter introduces excellent solutions to simplify the pressure by creating additional populations, increasing population diversity and preventing premature convergence. Experimental results based on Benchmark and Anatomy test sets on the Ontology Alignment Evaluation Initiative (OAEI) show that the proposed method outperforms existing state-of-the-art methods with an average f-measure of 0.96 on Benchmark.

参考文献/References:

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更新日期/Last Update: 2025-08-25