Optimizing Biomedical Ontology Alignment through a Compact Multiobjective Particle Swarm Optimization Algorithm Driven by Knee Solution
Nowadays, most real-world decision problems consist of two or more incommensurable or conflicting objectives to be optimized simultaneously, so-called multiobjective optimization problems (MOPs). Usually, a decision maker (DM) prefers only a single optimum solution in the Pareto front (PF), and the...
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Wiley
2020-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2020/4716286 |
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| author | Xingsi Xue Xiaojing Wu Junfeng Chen |
| author_facet | Xingsi Xue Xiaojing Wu Junfeng Chen |
| author_sort | Xingsi Xue |
| collection | DOAJ |
| description | Nowadays, most real-world decision problems consist of two or more incommensurable or conflicting objectives to be optimized simultaneously, so-called multiobjective optimization problems (MOPs). Usually, a decision maker (DM) prefers only a single optimum solution in the Pareto front (PF), and the PF’s knee solution is logically the one if there are no user-specific or problem-specific preferences. In this context, the biomedical ontology matching problem in the Semantic Web (SW) domain is investigated, which can be of help to integrate the biomedical knowledge and facilitate the translational discoveries. Since biomedical ontologies often own large-scale concepts with rich semantic meanings, it is difficult to find a perfect alignment that could meet all DM’s requirements, and usually, the matching process needs to trade-off two conflict objectives, i.e., the alignment’s recall and precision. To this end, in this work, the biomedical ontology matching problem is first defined as a MOP, and then a compact multiobjective particle swarm optimization algorithm driven by knee solution (CMPSO-K) is proposed to address it. In particular, a compact evolutionary mechanism is proposed to efficiently optimize the alignment’s quality, and a max-min approach is used to determine the PF’s knee solution. In the experiment, three biomedical tracks provided by Ontology Alignment Evaluation Initiative (OAEI) are used to test CMPSO-K’s performance. The comparisons with OAEI’s participants and PSO-based matching technique show that CMPSO-K is both effective and efficient. |
| format | Article |
| id | doaj-art-0f199b39e32648858b8fc5f167ed0478 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-0f199b39e32648858b8fc5f167ed04782025-02-03T06:43:39ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2020-01-01202010.1155/2020/47162864716286Optimizing Biomedical Ontology Alignment through a Compact Multiobjective Particle Swarm Optimization Algorithm Driven by Knee SolutionXingsi Xue0Xiaojing Wu1Junfeng Chen2Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, ChinaCollege of Information Science and Engineering, Fujian University of Technology, Fuzhou 350118, ChinaCollege of IOT Engineering, Hohai University, Changzhou 213022, ChinaNowadays, most real-world decision problems consist of two or more incommensurable or conflicting objectives to be optimized simultaneously, so-called multiobjective optimization problems (MOPs). Usually, a decision maker (DM) prefers only a single optimum solution in the Pareto front (PF), and the PF’s knee solution is logically the one if there are no user-specific or problem-specific preferences. In this context, the biomedical ontology matching problem in the Semantic Web (SW) domain is investigated, which can be of help to integrate the biomedical knowledge and facilitate the translational discoveries. Since biomedical ontologies often own large-scale concepts with rich semantic meanings, it is difficult to find a perfect alignment that could meet all DM’s requirements, and usually, the matching process needs to trade-off two conflict objectives, i.e., the alignment’s recall and precision. To this end, in this work, the biomedical ontology matching problem is first defined as a MOP, and then a compact multiobjective particle swarm optimization algorithm driven by knee solution (CMPSO-K) is proposed to address it. In particular, a compact evolutionary mechanism is proposed to efficiently optimize the alignment’s quality, and a max-min approach is used to determine the PF’s knee solution. In the experiment, three biomedical tracks provided by Ontology Alignment Evaluation Initiative (OAEI) are used to test CMPSO-K’s performance. The comparisons with OAEI’s participants and PSO-based matching technique show that CMPSO-K is both effective and efficient.http://dx.doi.org/10.1155/2020/4716286 |
| spellingShingle | Xingsi Xue Xiaojing Wu Junfeng Chen Optimizing Biomedical Ontology Alignment through a Compact Multiobjective Particle Swarm Optimization Algorithm Driven by Knee Solution Discrete Dynamics in Nature and Society |
| title | Optimizing Biomedical Ontology Alignment through a Compact Multiobjective Particle Swarm Optimization Algorithm Driven by Knee Solution |
| title_full | Optimizing Biomedical Ontology Alignment through a Compact Multiobjective Particle Swarm Optimization Algorithm Driven by Knee Solution |
| title_fullStr | Optimizing Biomedical Ontology Alignment through a Compact Multiobjective Particle Swarm Optimization Algorithm Driven by Knee Solution |
| title_full_unstemmed | Optimizing Biomedical Ontology Alignment through a Compact Multiobjective Particle Swarm Optimization Algorithm Driven by Knee Solution |
| title_short | Optimizing Biomedical Ontology Alignment through a Compact Multiobjective Particle Swarm Optimization Algorithm Driven by Knee Solution |
| title_sort | optimizing biomedical ontology alignment through a compact multiobjective particle swarm optimization algorithm driven by knee solution |
| url | http://dx.doi.org/10.1155/2020/4716286 |
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