Some scholars believe that integrating knowledge graphs with AI / ML algorithms will help with accuracy and interpretability.
Gary Marcus, a prominent figure in AI, is on a mission to instill a breath of fresh air to a discipline he sees as in danger of stagnating. Knowledge graphs, the 20-year old hype, may have something to offer there.
Read the ZDNet article for more details
Alan Turing Institute in London, UK has an ongoing research in applicability of knowledge graphs in machine learning and human AI.
Stanford has a recent article about applying knowledge graphs to AI
Techopedia also has an article about AI and knowledge graphs, with a nice summary:
There is still an enormous untapped potential in combining KGs and ML. For example, a massive amount of knowledge (like resource description frameworks RDFs), which is represented on the Internet (e.g. Wikipedia), is available essentially for free, and is not being leveraged by current AI systems. A hybrid KG and ML system can hugely benefit from this knowledge to better understand the world, to organize and infer missing knowledge.
And the list can go on and on, with articles about knowledge graphs getting some renewed attention in AI.
As soon as we get data from DApps in the Logosphere knowledge graphs, we are planning to collaborate with the universities and AI/ML practitioners to create exciting new powerful AI/ML algorithms.