Knowledge graph for IoT

We’re surrounded by the internet-of-things thousands of sensors relaying data from cars factories and stores these sensors and the data they produce ensure safe driving food safety and efficient production however there is much more we can do with the large amount of information that is generated at IBM research we’re developing AI technologies to connect and understand data at ways never seen before.


We combine machine learning with knowledge graph reasoning to enhance the data with layers of semantic abstraction this gives us new natural user interfaces that allow us to gain new insights from the data a lot of the data that’s coming from us is IOT based and that’s Internet of Things based so that’s machine to machine data.


When you get a lot of data thrown at you it ends up being a very difficult task of trying to extract real insight from that data set the only way we’re going to solve this problem is to teach the machines to make sense of the data so we can ask it questions and expect reasonable answers we developed a knowledge graph for IOT the system knows all about IOT and understands the meaning of different types of data it works in a similar way to the way our brains think.


Imagine a temperature sensor in a building an office worker tells the system hey it’s too hot in here it analyzes the speech is text and extracts the concepts hot and here it understands that hot is a concept for heat it quickly accesses the knowledge graph to find all assets in the area it then uses AI analytics and sensor data to determine if the temperature is actually too high it realizes the office area is located close to a window it checks the illumination sensor automatic blinds and wetter to see.


If there is high sunlight causing excessive temperature and finds nothing unusual it tries a different branch in its knowledge graph it finds an AC unit and uses AI Diagnostics to find the heating valve switched on so we can actually identify the problem and then commission work automatically to the relevant people to go and fix that problem it is a scalable solution that enables they or T to learn behavior to understand operation and to self diagnose problems.


While making human machine interaction more natural and intuitive it’s a general-purpose artificially intelligent multi-dimensional knowledge graph for IOT it combines reasoning with machine learning to analyze and understand very large data sets in real time this enables new insight from complex systems at scale.


As found on Youtube

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