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Title	Semantic Matching Through Knowledge Graphs: A Smart City Case
ID_Doc	48240
Authors	Voelz A.; Amlashi D.M.; Lee M.
Year	2023
Published	Lecture Notes in Business Information Processing, 482
DOI	http://dx.doi.org/10.1007/978-3-031-34985-0_10
Abstract	Using selected building blocks of the Semantic Web vision to enhance domain-specific modeling methods is advancing as a new research area. The study at hand aims to contribute to this novel area of research by proposing a Semantic Matching Model that serves as a foundation for matching the purpose of a certain modeling method with the intention of future users. Within this matching model, Linked Open Data is utilized to semantically align user inputs in the form of words or phrases with resources from the Semantic Web, which can then form the basis for semantic enrichment of model instances. An experimental proof-of-concept is provided in the form of a smart city-related implementation scenario. In this scenario, tour models are first matched with relevant information from the Semantic Web and then translated into semantic-rich knowledge graphs that enable the publication of the enriched models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Author Keywords	Conceptual Modeling; Domain-Specific Modeling Methods; Knowledge Graph; Linked Open Data; Semantic Matching

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