2020 Was The “Year of the Knowledge Graph”
2020 was undeniably the “Year of the Knowledge Graph.”
2020 was the year that Gartner put Knowledge Graphs at the peak of its hype cycle.
It was the year where 10% of the papers published at EMNLP referenced “knowledge” in their titles.
It was the year over 1000 engineers, enterprise users, and academics came together to talk about Knowledge Graphs at the 2nd Knowledge Graph Conference.
There are good reasons for this grass-roots trend, as it isn’t any one company that is pushing this trend (ahem, I’m looking at you, Cognitive Computing), but rather a broad coalition of academics, industry vertical practitioners, and enterprise users that generally deal with building intelligent information systems.
Knowledge graphs represent the best of how we hope the “next step” of AI looks like: intelligent systems that aren’t black boxes, but are explainable, that are grounded in the same real-world entities as us humans, and are able to exchange knowledge with us with precise common vocabularies. It’s no coinincidence that in the same year that marked the peak of the deep learning revolution (2012), Google introduced the Google Knowledge Graph as a way to provide interpretability to its otherwise opaque search ranking algorithms.
The Risk Of Hype: Touted Benefits Don’t Materialize