Knowledge graphs beyond the hype: Getting knowledge in and out of graphs and databases
What exactly are knowledge graphs, and what’s with all the hype about them? Learning to tell apart hype from reality, defining different types of graphs, and picking the right tools and database for your use case is essential if you want to be like the Airbnbs, Amazons, Googles, and LinkedIns of the world.
Knowledge graphs are hyped. We can officially say this now, since Gartner included knowledge graphs in the 2018 hype cycle for emerging technologies. Though we did not have to wait for Gartner — declaring this as the “Year of the Graph” was our opener for 2018. Like anyone active in the field, we see the opportunity, as well as the threat in this: With hype comes confusion.
Knowledge graphs are real. They have been for the last 20 years at least. Knowledge graphs, in their original definition and incarnation, have been about knowledge representation and reasoning. Things such as controlled vocabularies, taxonomies, schemas, and ontologies have all been part of this, built on a Semantic Web foundation of standards and practices.
So, what’s changed? How come the likes of Airbnb, Amazon, Google, LinkedIn, Uber, and Zalando sport knowledge graphs in their core business? How come Amazon and Microsoft joined the crowd of graph database vendors with their latest products? And how can you make this work?