Optimizing Ontology Alignment through Linkage Learning on Entity Correspondences
Data heterogeneity is the obstacle for the resource sharing on Semantic Web (SW), and ontology is regarded as a solution to this problem. However, since different ontologies are constructed and maintained independently, there also exists the heterogeneity problem between ontologies. Ontology matchin...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5574732 |
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| author | Xingsi Xue Chaofan Yang Chao Jiang Pei-Wei Tsai Guojun Mao Hai Zhu |
| author_facet | Xingsi Xue Chaofan Yang Chao Jiang Pei-Wei Tsai Guojun Mao Hai Zhu |
| author_sort | Xingsi Xue |
| collection | DOAJ |
| description | Data heterogeneity is the obstacle for the resource sharing on Semantic Web (SW), and ontology is regarded as a solution to this problem. However, since different ontologies are constructed and maintained independently, there also exists the heterogeneity problem between ontologies. Ontology matching is able to identify the semantic correspondences of entities in different ontologies, which is an effective method to address the ontology heterogeneity problem. Due to huge memory consumption and long runtime, the performance of the existing ontology matching techniques requires further improvement. In this work, an extended compact genetic algorithm-based ontology entity matching technique (ECGA-OEM) is proposed, which uses both the compact encoding mechanism and linkage learning approach to match the ontologies efficiently. Compact encoding mechanism does not need to store and maintain the whole population in the memory during the evolving process, and the utilization of linkage learning protects the chromosome’s building blocks, which is able to reduce the algorithm’s running time and ensure the alignment’s quality. In the experiment, ECGA-OEM is compared with the participants of ontology alignment evaluation initiative (OAEI) and the state-of-the-art ontology matching techniques, and the experimental results show that ECGA-OEM is both effective and efficient. |
| format | Article |
| id | doaj-art-00df8f7213114a52a36596fa71d252f8 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-00df8f7213114a52a36596fa71d252f82025-02-03T01:04:13ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/55747325574732Optimizing Ontology Alignment through Linkage Learning on Entity CorrespondencesXingsi Xue0Chaofan Yang1Chao Jiang2Pei-Wei Tsai3Guojun Mao4Hai Zhu5Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, Fujian 350118, ChinaFujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, Fujian 350118, ChinaFujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, Fujian 350118, ChinaDepartment of Computer Science and Software Engineering, Swinburne University of Technology, John Street, Hawthorn, Victoria 3122, AustraliaSchool of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, Fujian 350118, ChinaSchool of Network Engineering, Zhoukou Normal University, Zhoukou, Henan 466001, ChinaData heterogeneity is the obstacle for the resource sharing on Semantic Web (SW), and ontology is regarded as a solution to this problem. However, since different ontologies are constructed and maintained independently, there also exists the heterogeneity problem between ontologies. Ontology matching is able to identify the semantic correspondences of entities in different ontologies, which is an effective method to address the ontology heterogeneity problem. Due to huge memory consumption and long runtime, the performance of the existing ontology matching techniques requires further improvement. In this work, an extended compact genetic algorithm-based ontology entity matching technique (ECGA-OEM) is proposed, which uses both the compact encoding mechanism and linkage learning approach to match the ontologies efficiently. Compact encoding mechanism does not need to store and maintain the whole population in the memory during the evolving process, and the utilization of linkage learning protects the chromosome’s building blocks, which is able to reduce the algorithm’s running time and ensure the alignment’s quality. In the experiment, ECGA-OEM is compared with the participants of ontology alignment evaluation initiative (OAEI) and the state-of-the-art ontology matching techniques, and the experimental results show that ECGA-OEM is both effective and efficient.http://dx.doi.org/10.1155/2021/5574732 |
| spellingShingle | Xingsi Xue Chaofan Yang Chao Jiang Pei-Wei Tsai Guojun Mao Hai Zhu Optimizing Ontology Alignment through Linkage Learning on Entity Correspondences Complexity |
| title | Optimizing Ontology Alignment through Linkage Learning on Entity Correspondences |
| title_full | Optimizing Ontology Alignment through Linkage Learning on Entity Correspondences |
| title_fullStr | Optimizing Ontology Alignment through Linkage Learning on Entity Correspondences |
| title_full_unstemmed | Optimizing Ontology Alignment through Linkage Learning on Entity Correspondences |
| title_short | Optimizing Ontology Alignment through Linkage Learning on Entity Correspondences |
| title_sort | optimizing ontology alignment through linkage learning on entity correspondences |
| url | http://dx.doi.org/10.1155/2021/5574732 |
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