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Ruben Verborgh

Linked Sensor Data Generation using Queryable RML Mappings

by Pieter Heyvaert, Ruben Taelman, Ruben Verborgh, and Erik Mannens

As the amount of generated sensor data is increasing, semantic interoperability becomes an important aspect in order to support efficient data distribution and communication. Therefore, the integration and fusion of (sensor) data is important, as this data is coming from different data sources and might be in different formats. Furthermore, reusable and extensible methods for this integration and fusion are required in order to be able to scale with the growing number of applications that generate semantic sensor data. Current research efforts allow to map sensor data to Linked Data in order to provide semantic interoperability. However, they lack support for multiple data sources, hampering the integration and fusion. Furthermore, the used methods are not available for reuse or are not extensible, which hampers the development of applications. In this paper, we describe how the RDF Mapping Language (RML) and a Triple Pattern Fragments (TPF) server are used to address these shortcomings. The demonstration consists of a micro controller that generates sensor data. The data is captured and mapped to RDF triples using module-specific RML mappings, which are queried from a TPF server.

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Published in 2016 in Proceedings of the 15th International Semantic Web Conference: Posters and Demos.

Keywords: Linked Data, Triple Pattern Fragments, RML, communication, research

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