Regardless of the use case for which it is deployed, the Internet of Things (IoT) is largely based on machine-generated data. This basic IoT characteristic correlates naturally with machine-readable semantic knowledge graph technologies. The knowledge graph approach results in machine intelligence, which is necessary for maximizing the business value of IoT.
At the core of many IoT use cases is machine intelligence. It is predicated on semantic standards such as the Resource Description Framework (RDF). The uniformity of RDF and other semantic standards makes it possible for machines and people to understand data because they are represented in the same way. This quality also supports intelligent machine inferencing about data.
When machine understanding of the significance of data and its context is applied to relevant business objectives in the IoT, the effects are powerful. These effects can be witnessed in use cases such as smart cities, the oil and gas industry, and healthcare. In these use cases, semantic technologies and standards create machine intelligence empowering the IoT and the industrial internet.
Specifically, semantic technologies and standards facilitate interoperability of various IT systems, timely data federation, and intelligent inferencing for predictive maintenance. Consequently, everyone, from medical personnel to industrial internet producers, can leverage machine intelligence to make interventions in time to support organizational objectives.In conclusion, the knowledge graph approach strengthens sophisticated use cases in the industrial internet and IoT as a whole, delivering demonstrable business, civic and patient value.
Click here to read the original article published in Cambridge Semantics.
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