Using NSGA-III for optimising biomedical ontology alignment
To support semantic inter-operability between the biomedical information systems, it is necessary to determine the correspondences between the heterogeneous biomedical concepts, which is commonly known as biomedical ontology matching. Biomedical concepts are usually complex and ambiguous, which make...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-04-01
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| Series: | CAAI Transactions on Intelligence Technology |
| Subjects: | |
| Online Access: | https://digital-library.theiet.org/content/journals/10.1049/trit.2019.0014 |
| _version_ | 1818966540556435456 |
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| author | Xingsi Xue Jiawei Lu Jiawei Lu Junfeng Chen |
| author_facet | Xingsi Xue Jiawei Lu Jiawei Lu Junfeng Chen |
| author_sort | Xingsi Xue |
| collection | DOAJ |
| description | To support semantic inter-operability between the biomedical information systems, it is necessary to determine the correspondences between the heterogeneous biomedical concepts, which is commonly known as biomedical ontology matching. Biomedical concepts are usually complex and ambiguous, which makes matching biomedical ontologies a challenge. Since none of the similarity measures can distinguish the heterogeneous biomedical concepts in any context independently, usually several similarity measures are applied together to determine the biomedical concepts mappings. However, the ignorance of the effects brought about by different biomedical concept mapping's preference on the similarity measures significantly reduces the alignment's quality. In this study, a non-dominated sorting genetic algorithm (NSGA)-III-based biomedical ontology matching technique is proposed to effectively match the biomedical ontologies, which first utilises an ontology partitioning technique to transform the large-scale biomedical ontology matching problem into several ontology segment-matching problems, and then uses NSGA-III to determine the optimal alignment without tuning the aggregating weights. The experiment is conducted on the anatomy track and large biomedic ontologies track which are provided by the Ontology Alignment Evaluation Initiative (OAEI), and the comparisons with OAEI's participants show the effectiveness of the authors' approach. |
| first_indexed | 2024-12-20T13:34:32Z |
| format | Article |
| id | doaj.art-c74576823a1e4cc2986fd73d7e9aa7e0 |
| institution | Directory Open Access Journal |
| issn | 2468-2322 |
| language | English |
| last_indexed | 2024-12-20T13:34:32Z |
| publishDate | 2019-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | CAAI Transactions on Intelligence Technology |
| spelling | doaj.art-c74576823a1e4cc2986fd73d7e9aa7e02022-12-21T19:39:00ZengWileyCAAI Transactions on Intelligence Technology2468-23222019-04-0110.1049/trit.2019.0014TRIT.2019.0014Using NSGA-III for optimising biomedical ontology alignmentXingsi Xue0Jiawei Lu1Jiawei Lu2Junfeng Chen3College of Information Science and Engineering, Fujian University of TechnologyCollege of Information Science and Engineering, Fujian University of TechnologyCollege of Information Science and Engineering, Fujian University of TechnologyCollege of IOT Engineering, Hohai UniversityTo support semantic inter-operability between the biomedical information systems, it is necessary to determine the correspondences between the heterogeneous biomedical concepts, which is commonly known as biomedical ontology matching. Biomedical concepts are usually complex and ambiguous, which makes matching biomedical ontologies a challenge. Since none of the similarity measures can distinguish the heterogeneous biomedical concepts in any context independently, usually several similarity measures are applied together to determine the biomedical concepts mappings. However, the ignorance of the effects brought about by different biomedical concept mapping's preference on the similarity measures significantly reduces the alignment's quality. In this study, a non-dominated sorting genetic algorithm (NSGA)-III-based biomedical ontology matching technique is proposed to effectively match the biomedical ontologies, which first utilises an ontology partitioning technique to transform the large-scale biomedical ontology matching problem into several ontology segment-matching problems, and then uses NSGA-III to determine the optimal alignment without tuning the aggregating weights. The experiment is conducted on the anatomy track and large biomedic ontologies track which are provided by the Ontology Alignment Evaluation Initiative (OAEI), and the comparisons with OAEI's participants show the effectiveness of the authors' approach.https://digital-library.theiet.org/content/journals/10.1049/trit.2019.0014genetic algorithmsmedical computingontologies (artificial intelligence)medical information systemssimilarity measuresontology partitioning techniquelarge-scale biomedical ontology matching problemontology segment-matching problemsNSGA-IIIanatomy trackOntology Alignment Evaluation Initiativebiomedical ontology alignmentbiomedical information systemsheterogeneous biomedical conceptsnondominated sorting genetic algorithm-III-based biomedical ontology matchingbiomedical concept mapping |
| spellingShingle | Xingsi Xue Jiawei Lu Jiawei Lu Junfeng Chen Using NSGA-III for optimising biomedical ontology alignment CAAI Transactions on Intelligence Technology genetic algorithms medical computing ontologies (artificial intelligence) medical information systems similarity measures ontology partitioning technique large-scale biomedical ontology matching problem ontology segment-matching problems NSGA-III anatomy track Ontology Alignment Evaluation Initiative biomedical ontology alignment biomedical information systems heterogeneous biomedical concepts nondominated sorting genetic algorithm-III-based biomedical ontology matching biomedical concept mapping |
| title | Using NSGA-III for optimising biomedical ontology alignment |
| title_full | Using NSGA-III for optimising biomedical ontology alignment |
| title_fullStr | Using NSGA-III for optimising biomedical ontology alignment |
| title_full_unstemmed | Using NSGA-III for optimising biomedical ontology alignment |
| title_short | Using NSGA-III for optimising biomedical ontology alignment |
| title_sort | using nsga iii for optimising biomedical ontology alignment |
| topic | genetic algorithms medical computing ontologies (artificial intelligence) medical information systems similarity measures ontology partitioning technique large-scale biomedical ontology matching problem ontology segment-matching problems NSGA-III anatomy track Ontology Alignment Evaluation Initiative biomedical ontology alignment biomedical information systems heterogeneous biomedical concepts nondominated sorting genetic algorithm-III-based biomedical ontology matching biomedical concept mapping |
| url | https://digital-library.theiet.org/content/journals/10.1049/trit.2019.0014 |
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