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Semantics and the Internet of Things – More in Common than You Might Think

Take objects in your daily life. Now, equip them with technology that allows them collect data and to communicate with each other, and to us, to make our lives better. This is what’s being called “The Internet of Things”. The Internet of Things (IoT) is the network of physical objects that contain embedded technology to communicate and sense or interact with their internal states or the external environment (this according to Gartner, which reports that there will be nearly 26 billion devices on the Internet of Things by 2020.)

As “the internet of things” gets more attention, especially as governments start dedicating funds for it, semantic technology will provide a big differentiator as we train it not only how to think, but to do so more intelligently. Aggregating and analyzing information and semantically connecting it with things, you may obtain “a smarter perspective” with objects that can tell other sensors what to do and why.

 

Smartphone apps are already helping us do a number of things better and more efficiently, saving on our energy costs at home by controlling our heating and interacting with automatic lighting, air cleaning or water systems.

But what if we could connect things to thoughts? Thanks to semantics, we can. This is one of the most important principles on which is based the “smart city” idea of making available unstructured information such as tweets that give up to the minute notification about events, breaking news and other situations. For example: There are children being forced to panhandle at the corner, a car has been parked in the lot for months, there some tree branches are covering the stop sign…and so on… Think of the value that this integrated and contextual information could have for a number of uses.

This kind of information, properly analyzed and integrated with data from other sources (including things), can fill in the big picture that would otherwise be hidden.

Instead of condensing things through a few keywords or simple phrases of text, we can apply true, contextual meaning in a way that would allow us to understand data unambiguously.

 


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