This paper investigates the semantic modeling of smart cities and proposes two ontology matching frameworks, called Clustering for Ontology Matching-based Instances (COMI) and Pattern mining for Ontology Matching-based Instances (POMI). The goal is to discover the relevant knowledge by investigating the correlations among smart city data based on clustering and pattern mining approaches. The COMI method first groups the highly correlated ontologies of smart-city data into similar clusters using the generic k-means algorithm. The key idea of this method is that it clusters the instances of each ontology and then matches two ontologies by matching their clusters and the corresponding instances within the clusters. The POMI method studies the correlations among the data properties and selects the most relevant properties for the ontology matching process. To demonstrate the usefulness and accuracy of the COMI and POMI frameworks, several experiments on the DBpedia, Ontology Alignment Evaluation Initiative, and NOAA ontology databases were conducted. The results show that COMI and POMI outperform the state-of-the-art ontology matching models regarding computational cost without losing the quality during the matching process. Furthermore, these results confirm the ability of COMI and POMI to deal with heterogeneous large-scale data in smart-city environments.

中文翻译：

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语义建模的本体匹配方法：以智慧城市为例

本文研究了智慧城市的语义建模，并提出了两个本体匹配框架，称为基于本体匹配的实例的聚类（COMI）和基于本体匹配的实例的模式挖掘（POMI）。目标是通过基于聚类和模式挖掘方法调查智慧城市数据之间的相关性来发现相关知识。COMI 方法首先使用通用 k-means 算法将智能城市数据的高度相关本体分组到相似的集群中。该方法的关键思想是它对每个本体的实例进行聚类，然后通过匹配它们的簇和簇内的相应实例来匹配两个本体。POMI 方法研究数据属性之间的相关性，并为本体匹配过程选择最相关的属性。为了证明 COMI 和 POMI 框架的有用性和准确性，对 DBpedia、Ontology Alignment Evaluation Initiative 和 NOAA 本体数据库进行了几次实验。结果表明，COMI 和 POMI 在计算成本方面优于最先进的本体匹配模型，而不会在匹配过程中损失质量。此外，这些结果证实了 COMI 和 POMI 在智慧城市环境中处理异构大规模数据的能力。结果表明，COMI 和 POMI 在计算成本方面优于最先进的本体匹配模型，而不会在匹配过程中损失质量。此外，这些结果证实了 COMI 和 POMI 在智慧城市环境中处理异构大规模数据的能力。结果表明，COMI 和 POMI 在计算成本方面优于最先进的本体匹配模型，而不会在匹配过程中损失质量。此外，这些结果证实了 COMI 和 POMI 在智慧城市环境中处理异构大规模数据的能力。