Sensor ontology is a standard conceptual model that describes information of sensor device, which includes the concepts of various sensor modules and the relationships between them. The problem of heterogeneity between sensor ontologies is introduced because different sensor ontology engineers have different ways of describing sensor devices and different structures for the construction of sensor ontologies. Addressing the heterogeneity of sensor ontologies contributes to facilitate the semantic fusion of two sensor ontologies, enabling the sharing and reuse of sensor information. To solve the above problem, an ontology meta-matching method is proposed by this paper to find out the correspondence between entities in distinct sensor ontologies. How to measure the degree of similarity between entities with a set of suitable similarity measures and how to better integrate multiple measures to determine the equivalent entities are the challenges of the ontology meta-matching problem. In this paper, two approximate measurement methods of the quality for ontology matching results are designed, and a multi-objective optimization model for the ontology meta-matching problem is constructed based on these methods. Eventually, a multi-objective particle swarm optimization (MOPSO) algorithm is propounded to dispose of the problem and optimize the quality of ontology meta-matching results, which is named density and distribution-based competitive mechanism multi-objective particle swarm algorithm (D\(^{2}\)CMOPSO). The sophistication of the D\(^{2}\)CMOPSO based sensor ontology meta-matching method is verified through experiments. Comparing with other matching systems and advanced systems of Ontology Alignment Evaluation Initiative (OAEI), the proposed method can improve the quality of matching results more effectively.

传感器本体是描述传感器设备信息的标准概念模型，包括各种传感器模块的概念以及它们之间的关系。由于不同的传感器本体工程师描述传感器设备的方式不同，构建传感器本体的结构不同，因此引入了传感器本体之间的异构问题。解决传感器本体的异构性有助于促进两个传感器本体的语义融合，实现传感器信息的共享和重用。针对上述问题，本文提出了一种本体元匹配方法，用于找出不同传感器本体中实体之间的对应关系。如何用一组合适的相似度度量来衡量实体之间的相似度，以及如何更好地整合多个度量来确定等价实体是本体元匹配问题的挑战。本文设计了两种本体匹配结果质量的近似度量方法，并基于这些方法构建了本体元匹配问题的多目标优化模型。最后，提出一种多目标粒子群优化（MOPSO）算法来解决该问题，优化本体元匹配结果的质量，命名为基于密度和分布的竞争机制多目标粒子群算法（D 设计了本体匹配结果质量的两种近似度量方法，并基于这些方法构建了本体元匹配问题的多目标优化模型。最后，提出一种多目标粒子群优化（MOPSO）算法来解决该问题，优化本体元匹配结果的质量，命名为基于密度和分布的竞争机制多目标粒子群算法（D 设计了本体匹配结果质量的两种近似度量方法，并基于这些方法构建了本体元匹配问题的多目标优化模型。最后，提出一种多目标粒子群优化（MOPSO）算法来解决该问题，优化本体元匹配结果的质量，命名为基于密度和分布的竞争机制多目标粒子群算法（D\(^{2}\) CMOPSO)。通过实验验证了基于D \(^{2}\) CMOPSO 的传感器本体元匹配方法的复杂性。与本体对齐评估倡议（OAEI）的其他匹配系统和先进系统相比，该方法可以更有效地提高匹配结果的质量。