Semantic Enrichment of IoT Stream Data On-demand

• Published in: 2018 IEEE 12th International Conference on Semantic Computing (ICSC) pp. 33 - 40
• Main Authors: Karim, Farah, Al Naameh, Ola, Lytra, Ioanna, Mader, Christian, Vidal, Maria-Esther, Auer, Soren
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-01-2018
• Subjects: Data Factorization; Internet Of Things; Ontologies; Query Execution; Resource description framework; Semantics; Semantification; Sensor phenomena and characterization; Stream Data; Temperature measurement; Temperature sensors
• DOI: 10.1109/ICSC.2018.00014

Connecting the physical world to the Internet of Things (IoT) allows for the development of a wide variety of applications. Things can be searched, managed, analyzed, and even included in collaborative games. Industries, health care, and cities are exploiting IoT data-driven frameworks to make these organizations more efficient, thus, improving the lives of citizens. For making IoT a reality, data produced by sensors, smart phones, watches, and other wearables need to be integrated; moreover, the meaning of IoT data should be explicitly represented. However, the Big Data nature of IoT data imposes challenges that need to be addressed in order to provide scalable and efficient IoT data-driven infrastructures. We tackle these issues and focus on the problems of describing the meaning of IoT streaming data using ontologies and integrating this data in a knowledge graph. We devise DESERT, a SPARQL query engine able to on-Demand factorizE and Semantically Enrich stReam daTa in a knowledge graph. Resulting knowledge graphs model the semantics or meaning of merged data in terms of entities that satisfy the SPARQL queries and relationships among those entities; thus, only data required for query answering is included in the knowledge graph. We empirically evaluate the results of DESERT on SRBench, a benchmark of Streaming RDF data. The experimental results suggest that DESERT allows for speeding up query execution while the size of the knowledge graphs remains relatively low.