Zhuang Yucheng, Huang Yikun, Liu Wenyu
Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, No. 69 Xuefu South Road, Minhou, Fuzhou 350118, China.
Concord University College, Fujian Normal University, No. 68 Xuefu South Road, Minhou, Fuzhou 350117, China.
Sensors (Basel). 2023 May 25;23(11):5069. doi: 10.3390/s23115069.
Sensor ontology provides a standardized semantic representation for information sharing between sensor devices. However, due to the varied descriptions of sensor devices at the semantic level by designers in different fields, data exchange between sensor devices is hindered. Sensor ontology matching achieves data integration and sharing between sensors by establishing semantic relationships between sensor devices. Therefore, a niching multi-objective particle swarm optimization algorithm (NMOPSO) is proposed to effectively solve the sensor ontology matching problem. As the sensor ontology meta-matching problem is essentially a multi-modal optimization problem (MMOP), a niching strategy is introduced into MOPSO to enable the algorithm to find more global optimal solutions that meet the needs of different decision makers. In addition, a diversity-enhancing strategy and an opposition-based learning (OBL) strategy are introduced into the evolution process of NMOPSO to improve the quality of sensor ontology matching and ensure the solutions converge to the real Pareto fronts (PFs). The experimental results demonstrate the effectiveness of NMOPSO in comparison to MOPSO-based matching techniques and participants of the Ontology Alignment Evaluation Initiative (OAEI).
传感器本体为传感器设备之间的信息共享提供了标准化的语义表示。然而，由于不同领域的设计者在语义层面上对传感器设备的描述各不相同，阻碍了传感器设备之间的数据交换。传感器本体匹配通过在传感器设备之间建立语义关系来实现传感器之间的数据集成与共享。因此，提出了一种小生境多目标粒子群优化算法（NMOPSO）来有效解决传感器本体匹配问题。由于传感器本体元匹配问题本质上是一个多模态优化问题（MMOP），将小生境策略引入到MOPSO中，使算法能够找到更多满足不同决策者需求的全局最优解。此外，在NMOPSO的进化过程中引入了多样性增强策略和基于对立学习（OBL）的策略，以提高传感器本体匹配的质量，并确保解收敛到真实的帕累托前沿（PFs）。实验结果表明，与基于MOPSO的匹配技术和本体对齐评估倡议（OAEI）的参与者相比，NMOPSO是有效的。
