Knowledge graph evolution: Platforms that speak your language
Knowledge graphs are among the most important technologies for the 2020s. Here is how they are evolving, with vendors and standard bodies listening, and platforms becoming fluent in many query languages
This may come as a shock if you’ve first encountered knowledge graphs in Gartner’s hype cycles and trends, or in the extensive coverage they are getting lately. But here it is: Knowledge graph technology is about 20 years old. This, however, does not mean it’s stagnating — on the contrary.
Gartner predicted that the application of graph processing and graph databases will grow at 100% annually through 2022 to continuously accelerate data preparation and enable more complex and adaptive data science. Graph database vendors seem to verify this across the board: 2019 was a very good year. Having identified knowledge graphs as a key technology for the 2020s, we take a look at how they are evolving.
First, let’s quickly recap those 20 years of history. What we call Knowledge Graphs today has been largely initiated by none other than Tim Berners-Lee in 2001. Berners-Lee, who is also credited as the inventor of the web, published his Semantic Web manifesto in the Scientific American in 2001. The core concepts for Knowledge Graphs have been laid there.
The Semantic Web manifesto was in many ways ahead of its time. Looking back today, we can see some parts of it going strong, while others have faded. Building on a foundation of standards for interoperability, such as Unicode, URIs, and RDF, the core of the vision has always been semantics: instilling meaning in web content.