College of Information Science and Engineering, Fujian University of Technology, Fuzhou 350118, China.
College of IOT Engineering, Hohai University, Nanjing 213022, China.
Comput Intell Neurosci. 2018 Oct 8;2018:2309587. doi: 10.1155/2018/2309587. eCollection 2018.
Over the recent years, ontologies are widely used in various domains such as medical records annotation, medical knowledge representation and sharing, clinical guideline management, and medical decision-making. To implement the cooperation between intelligent applications based on biomedical ontologies, it is crucial to establish correspondences between the heterogeneous biomedical concepts in different ontologies, which is so-called biomedical ontology matching. Although Evolutionary algorithms (EAs) are one of the state-of-the-art methodologies to match the heterogeneous ontologies, huge memory consumption, long runtime, and the bias improvement of the solutions hamper them from efficiently matching biomedical ontologies. To overcome these shortcomings, we propose a compact CoEvolutionary Algorithm to efficiently match the biomedical ontologies. Particularly, a compact EA with local search strategy is able to save the memory consumption and runtime, and three subswarms with different optimal objectives can help one another to avoid the solution's bias improvement. In the experiment, two famous testing cases provided by Ontology Alignment Evaluation Initiative (OAEI 2017), i.e. anatomy track and large biomed track, are utilized to test our approach's performance. The experimental results show the effectiveness of our proposal.
近年来，本体在医学记录注释、医学知识表示和共享、临床指南管理和医学决策等各个领域得到了广泛应用。为了实现基于生物医学本体的智能应用之间的合作，建立不同本体中异构生物医学概念之间的对应关系至关重要，这就是所谓的生物医学本体匹配。虽然进化算法 (EA) 是匹配异构本体的最先进方法之一，但巨大的内存消耗、长时间运行和解决方案的偏差改进阻碍了它们有效地匹配生物医学本体。为了克服这些缺点，我们提出了一种紧凑的协同进化算法来有效地匹配生物医学本体。特别是，具有局部搜索策略的紧凑 EA 能够节省内存消耗和运行时间，并且具有三个不同最优目标的子群能够相互帮助避免解决方案的偏差改进。在实验中，利用 Ontology Alignment Evaluation Initiative (OAEI 2017) 提供的两个著名测试案例，即解剖学轨道和大型生物医学轨道，来测试我们方法的性能。实验结果表明了我们方案的有效性。
