Matching heterogeneous ontologies with adaptive evolutionary algorithm
An ontology provides a formal description on the domain concepts and their relationships. Due to the subjectivity of ontology engineers, one concept might be expressed in various ways, yielding the so-called ontology heterogeneity problem, and ontology matching is a ground method to address this pro...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2021.1991278 |
| _version_ | 1797684085078884352 |
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| author | Xingsi Xue Haolin Wang Xin Zhou Guojun Mao Hai Zhu |
| author_facet | Xingsi Xue Haolin Wang Xin Zhou Guojun Mao Hai Zhu |
| author_sort | Xingsi Xue |
| collection | DOAJ |
| description | An ontology provides a formal description on the domain concepts and their relationships. Due to the subjectivity of ontology engineers, one concept might be expressed in various ways, yielding the so-called ontology heterogeneity problem, and ontology matching is a ground method to address this problem. Ontology matching technique uses the similarity measure to determine the correspondences between two heterogeneous ontology entities. In order to improve the quality of ontology alignment, it is necessary to combine different kinds of similarity measures, and how to optimize the aggregating weights is called the ontology meta-matching problem. Tin this work, a heuristic evaluating metric on the ontology alignment is presented to evaluate the ontology alignment's quality, and a mathematical model on ontology meta-matching problem is constructed. Then, an Adaptive Evolutionary Algorithm (AEA) is proposed to effectively solve this problem. In particular, when the elite solution remains unchanged, AEA adaptively activates three independent exploring strategies, which, respectively use the adaptive selection, crossover and mutation operators based on the population diversity metric. In the experiment, we compare AEA among EA based matching technique and the state-of-the-art ontology matching technique, and the experimental results show its effectiveness. |
| first_indexed | 2024-03-12T00:24:30Z |
| format | Article |
| id | doaj.art-9cbaa82abf3d44f4b5a49e83771c7a91 |
| institution | Directory Open Access Journal |
| issn | 0954-0091 1360-0494 |
| language | English |
| last_indexed | 2024-03-12T00:24:30Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj.art-9cbaa82abf3d44f4b5a49e83771c7a912023-09-15T10:47:59ZengTaylor & Francis GroupConnection Science0954-00911360-04942022-12-0134181182810.1080/09540091.2021.19912781991278Matching heterogeneous ontologies with adaptive evolutionary algorithmXingsi Xue0Haolin Wang1Xin Zhou2Guojun Mao3Hai Zhu4Fujian University of TechnologyFujian University of TechnologyTaiyuan University of TechnologyFujian University of TechnologyZhoukou Normal UniversityAn ontology provides a formal description on the domain concepts and their relationships. Due to the subjectivity of ontology engineers, one concept might be expressed in various ways, yielding the so-called ontology heterogeneity problem, and ontology matching is a ground method to address this problem. Ontology matching technique uses the similarity measure to determine the correspondences between two heterogeneous ontology entities. In order to improve the quality of ontology alignment, it is necessary to combine different kinds of similarity measures, and how to optimize the aggregating weights is called the ontology meta-matching problem. Tin this work, a heuristic evaluating metric on the ontology alignment is presented to evaluate the ontology alignment's quality, and a mathematical model on ontology meta-matching problem is constructed. Then, an Adaptive Evolutionary Algorithm (AEA) is proposed to effectively solve this problem. In particular, when the elite solution remains unchanged, AEA adaptively activates three independent exploring strategies, which, respectively use the adaptive selection, crossover and mutation operators based on the population diversity metric. In the experiment, we compare AEA among EA based matching technique and the state-of-the-art ontology matching technique, and the experimental results show its effectiveness.http://dx.doi.org/10.1080/09540091.2021.1991278ontology heterogeneityontology meta-matchingheuristic evaluating metricadaptive evolutionary algorithm |
| spellingShingle | Xingsi Xue Haolin Wang Xin Zhou Guojun Mao Hai Zhu Matching heterogeneous ontologies with adaptive evolutionary algorithm Connection Science ontology heterogeneity ontology meta-matching heuristic evaluating metric adaptive evolutionary algorithm |
| title | Matching heterogeneous ontologies with adaptive evolutionary algorithm |
| title_full | Matching heterogeneous ontologies with adaptive evolutionary algorithm |
| title_fullStr | Matching heterogeneous ontologies with adaptive evolutionary algorithm |
| title_full_unstemmed | Matching heterogeneous ontologies with adaptive evolutionary algorithm |
| title_short | Matching heterogeneous ontologies with adaptive evolutionary algorithm |
| title_sort | matching heterogeneous ontologies with adaptive evolutionary algorithm |
| topic | ontology heterogeneity ontology meta-matching heuristic evaluating metric adaptive evolutionary algorithm |
| url | http://dx.doi.org/10.1080/09540091.2021.1991278 |
| work_keys_str_mv | AT xingsixue matchingheterogeneousontologieswithadaptiveevolutionaryalgorithm AT haolinwang matchingheterogeneousontologieswithadaptiveevolutionaryalgorithm AT xinzhou matchingheterogeneousontologieswithadaptiveevolutionaryalgorithm AT guojunmao matchingheterogeneousontologieswithadaptiveevolutionaryalgorithm AT haizhu matchingheterogeneousontologieswithadaptiveevolutionaryalgorithm |