Integrating oceanographic sensor data using SSN/SOSA ontology
As the deployment of ocean sensors continues to grow, there is a growing need for standardized ways to represent and integrate sensor data from different sources. One approach to achieving this is through the use of Semantic Sensor Network (SSN/SOSA) ontology, which provides a common vocabulary and...
Guardado en:
| Autores principales: | , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/155442 |
| Aporte de: |
| Sumario: | As the deployment of ocean sensors continues to grow, there is a growing need for standardized ways to represent and integrate sensor data from different sources. One approach to achieving this is through the use of Semantic Sensor Network (SSN/SOSA) ontology, which provides a common vocabulary and framework for describing sensors, observations, and their properties. In this paper, we present a method for converting ocean sensor data in CSV format to Resource Description Framework (RDF) using RDFLib library for Phyton and SSN/SOSA ontology. The resulting RDF triples can be stored in a triplestore for querying and analysis, providing a standardized representation of ocean sensor data that can be easily integrated with other data sources. |
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