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. 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.
In between years, or zwischen den Jahren, is a German expression for the period between Christmas and New Year. This is traditionally a time of year when not much happens, and this playful expression lingers itself in between the literal and the metaphoric. As the first edition of the Year of the Graph newsletter is here, a short retrospective may be due in addition to the usual updates. When we called 2018 the Year of the Graph, we did not have to wait for the Gartners of the world to verify what we saw coming. We can without a doubt say this has been the Year Graphs went mainstream.
Video: Microsoft is building a'world graph' for geographic data Airbnb, Coursera, Docker, GitHub, Twitter, Uber, and, of course, Facebook, where it was invented. These are some of the organizations where people use GraphQL solutions, as presented in last week's GraphQL Europe, and if you're one to be impressed by name-dropping, this should get your attention. GraphQL seems to be spreading like wildfire, and there's a reason for that. As REST APIs are proliferating, the promise of accessing them all through a single query language and hub, which is what GraphQL and GraphQL server implementations bring, is alluring. REST APIs expose application functionality, and all applications use some database in the back end.
Our main program features speakers from the likes of Microsoft, Uber, nVidia, Bayer, GSK, and JP Morgan, and combo tickets are on sale! Need to convince your manager? We got you a kit, start working on it! Do you have experience in data modeling, or in classification, and want to get the boost needed to upgrade to building knowledge graphs? Then going from taxonomies and schemas to knowledge graphs is what you need.
Now, none of that is unresolvable. For the property graph world, agreeing on some common sense serialization format, coming up with a synthesis of existing query languages, and working on a property graph model that will enable schema and semantics definition all look like reasonable steps to take. Good news then: It seems that's exactly what's in the agenda, so we can expect these steps to be implemented.