Matching Ontologies through Multi-Objective Evolutionary Algorithm with Relevance Matrix
The ultimate goal of semantic web (SW) is to implement mutual collaborations among ontology-based intelligent systems. To this end, it is necessary to integrate those domain-independent and cross-domain ontologies by finding the correspondences between their entities, which is the so-called ontology...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/12/2077 |
| _version_ | 1827658720130105344 |
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| author | Hai Zhu Xingsi Xue Hongfeng Wang |
| author_facet | Hai Zhu Xingsi Xue Hongfeng Wang |
| author_sort | Hai Zhu |
| collection | DOAJ |
| description | The ultimate goal of semantic web (SW) is to implement mutual collaborations among ontology-based intelligent systems. To this end, it is necessary to integrate those domain-independent and cross-domain ontologies by finding the correspondences between their entities, which is the so-called ontology matching. To improve the quality of ontology alignment, in this work, the ontology matching problem is first defined as a sparse multi-objective optimization problem (SMOOP), and then, a multi-objective evolutionary algorithm with a relevance matrix (MOEA-RM) is proposed to address it. In particular, a relevance matrix (RM) is presented to adaptively measure the relevance of each individual’s genes to the objectives, which is applied in MOEA’s initialization, crossover and mutation to ensure the population’s sparsity and to speed up the the algorithm’s convergence. The experiment verifies the performance of MOEA-RM by comparing it with the state-of-the-art ontology matching techniques, and the experimental results show that MOEA-RM is able to effectively address the ontology matching problem with different heterogeneity characteristics. |
| first_indexed | 2024-03-09T23:08:36Z |
| format | Article |
| id | doaj.art-2aaa679c76674558b50d4d7f0dcac0b1 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T23:08:36Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-2aaa679c76674558b50d4d7f0dcac0b12023-11-23T17:49:18ZengMDPI AGMathematics2227-73902022-06-011012207710.3390/math10122077Matching Ontologies through Multi-Objective Evolutionary Algorithm with Relevance MatrixHai Zhu0Xingsi Xue1Hongfeng Wang2School of Network Engineering, Zhoukou Normal University, Zhoukou 466001, ChinaFujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, ChinaSchool of Network Engineering, Zhoukou Normal University, Zhoukou 466001, ChinaThe ultimate goal of semantic web (SW) is to implement mutual collaborations among ontology-based intelligent systems. To this end, it is necessary to integrate those domain-independent and cross-domain ontologies by finding the correspondences between their entities, which is the so-called ontology matching. To improve the quality of ontology alignment, in this work, the ontology matching problem is first defined as a sparse multi-objective optimization problem (SMOOP), and then, a multi-objective evolutionary algorithm with a relevance matrix (MOEA-RM) is proposed to address it. In particular, a relevance matrix (RM) is presented to adaptively measure the relevance of each individual’s genes to the objectives, which is applied in MOEA’s initialization, crossover and mutation to ensure the population’s sparsity and to speed up the the algorithm’s convergence. The experiment verifies the performance of MOEA-RM by comparing it with the state-of-the-art ontology matching techniques, and the experimental results show that MOEA-RM is able to effectively address the ontology matching problem with different heterogeneity characteristics.https://www.mdpi.com/2227-7390/10/12/2077ontology matchingsparse multi-objective optimization problemmulti-objective evolutionary algorithmrelevance matrix |
| spellingShingle | Hai Zhu Xingsi Xue Hongfeng Wang Matching Ontologies through Multi-Objective Evolutionary Algorithm with Relevance Matrix Mathematics ontology matching sparse multi-objective optimization problem multi-objective evolutionary algorithm relevance matrix |
| title | Matching Ontologies through Multi-Objective Evolutionary Algorithm with Relevance Matrix |
| title_full | Matching Ontologies through Multi-Objective Evolutionary Algorithm with Relevance Matrix |
| title_fullStr | Matching Ontologies through Multi-Objective Evolutionary Algorithm with Relevance Matrix |
| title_full_unstemmed | Matching Ontologies through Multi-Objective Evolutionary Algorithm with Relevance Matrix |
| title_short | Matching Ontologies through Multi-Objective Evolutionary Algorithm with Relevance Matrix |
| title_sort | matching ontologies through multi objective evolutionary algorithm with relevance matrix |
| topic | ontology matching sparse multi-objective optimization problem multi-objective evolutionary algorithm relevance matrix |
| url | https://www.mdpi.com/2227-7390/10/12/2077 |
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