Semantic Enrichment of IoT Stream Data On-demand

Abstract

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.

The Architecture of Mass Customization-Social Internet of Things System: Current Research Profile

“…This method uses a deep neural network combined with self-attention to generate a knowledge graph. Karim et al [45] have proposed DESERT, a query engine that can decompose and semantically enrich streaming data on demand. It can effectively integrate IOT data from different streaming data sources.…”

“…It also belongs to the field of knowledge discovery. In terms of the knowledge graphs, the studies [40,45] have shown that, by generating various types of knowledge graphs, people can better analyze data and understand user needs. The above literature also involves two other aspects; for example, Gómez-Berbís et al [41] have proposed SEDIT, based on knowledge atlas technology.…”