Abstract
AbstractThis 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.
Highlights
Today’s World-Wide Web has billions of web pages, but the vast majority of them is readable by human
The main contributions can be summarized as follows: 1. We present a new framework, called Clustering for Ontology Matching-based Instances (COMI), which adopts clustering techniques to decompose the set of instances of the given ontologies
This paper presented two new frameworks, called COMI and Pattern mining for Ontology Matching-based Instances (POMI), which are cluster-based and pattern mining-based approaches, to solve the ontology matching problem
Summary
Today’s World-Wide Web has billions of web pages, but the vast majority of them is readable by human (in HTML format). The researchers created the Semantic Web, where ontologies describe the semantics of data. When data is in the form of ontologies, machines can better understand semantics and locate and integrate data for a wide variety of tasks. On the Semantic Web, data comes from many different ontologies, and processing information through ontologies is not possible without knowing the semantic links between them. It can be applied to several real-world problems, such as biomedical data,[1] e-learning,[2] and Natural Language Processing.[3] Cities are rapidly growing as they strive to accommodate more than 2.5 billion smart citizens by 2050. Heterogeneous data in smart cities is rapidly growing in volume and types, which makes ontology matching play an important role in smart-city semantic modeling to improve city planning knowledge
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