| Title |
Synapse : Towards Linked Data for Smart Cities using a Semantic Annotation Framework |
| ID_Doc |
38025 |
| Authors |
An, J; Kumar, S; Lee, J; Jeong, S; Song, J |
| Title |
Synapse : Towards Linked Data for Smart Cities using a Semantic Annotation Framework |
| Year |
2020 |
| Published |
|
| DOI |
|
| Abstract |
Existing research in the smart city domain concentrates on data collection and storage from numerous sensors deployed in cities. The absence of metadata such as the meaning of the collected data, the context and relationships with other data, has hindered interoperability among intelligent services in the smart city. Linked data is an important concept which can improve various smart city services by adding and connecting metadata to all data existing in smart city. In this paper, we propose a semantic annotation framework, Synapse, that can add meaning to data and annotate relationships between data using ontologies. The Synapse framework provides annotations that make it easier to add a Common Ontology and metadata that can be used across multiple smart city services. Synapse provides the ability to index and annotate using ontology schema and smart city data providing the search and traversal mechanisms for semantic-based linked data. To support temporal associations, Synapse uses data time-stamps in the ontology and utilizes it as linked data. Synapse ontologies define the relationship between common concept and functions such as zones, evaluations, and observations required in smart cities, and provides a dynamic structure with which ontologies can be derived for specific domains. In order to verify the feasibility of Synapse, ontology and annotation technology was successfully applied to 90,240 data points collected from smart parking services and air quality services. |
| Author Keywords |
Smart City; Semantics,Linked Data; Internet-of-Things (IoT); Interoperability |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000627822200080 |
| WoS Category |
Computer Science, Theory & Methods; Engineering, Electrical & Electronic |
| Research Area |
Computer Science; Engineering |
| PDF |
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