Knowledge Graphs Done Right – Tips for Implementing Semantic AI in Business
“Alexa…how’s the weather today?”
For the past decade or so, Knowledge Graphs have been sneaking into our daily lives, be it through voice assistants (such as Alexa, Siri or Google Assistant), intuitive search results or even personalized shopping experiences through online store recommenders. We are constantly interacting with Knowledge Graphs on a daily basis. However, Knowledge Graphs are still a mystery to most and because of its seamless entrance into our lives – most of us aren’t even aware of how dependent we are on the technology, or worse – how we have come to expect a certain quality and standard that we are now accustomed to.
So what are the Google, Amazon and Microsoft giants of the world doing in order to ensure that you are getting the fastest and most accurate touch points on a daily basis?
These enterprise giants have been bringing together all of the data that they have accumulated over the years, and have used Knowledge Graphs to harness the power of their data by connecting all of their data sets in a meaningful way.
Organizing data, for most of us, means Excel spreadsheets and folders upon folders. Knowledge graph technology, however organizes data like our brain – through context and relations. By connecting your data, you (and also machines) are able to gain context within your knowledge; helping you to make informed decisions based on all of the information you already have. An increasing number of enterprises have established large teams of data scientists to make sure that the searches and recommenders they provide are helping users make the right decisions on a daily basis.
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