If you get 5 streams of bits and four of them correlate with some epsilon number of new bits, and the fifth has 10x epsilon new bits, one of things is likely true. Either the odd stream out is corrupted or the stream has changed.
Apply that to news stories where the ontology (O) and knowledge graph (KG) are combined to "error correct" the content of the story. "Error bits" would be defined as relationships that don't match what the ontology says can be true, or facts that are different than those facts already in the knowledge base. You can flag the odd news story as lacking alignment with the existing KG&O, further you can quantify that lack of alignment fairly precisely.
So if the news story has 0 bits of information it correlates very strongly with other existing stories. If it has many bits of information it contrasts with other existing stories. At some number of stories on the same topic you can begin to reason about which are more likely 'true' and which are more likely 'false.'
To suggest a specific example, imagine a person living inside WWII-era Germany correlating a propaganda news article with every other news article they have read, all of which are also propaganda. Correlation-based news epistemology isn't even based on a prevailing social consensus, it's based on the consensus of broadcasters.
If we had a system like this that worked, we could at least leverage it to be able to quantify the coherence of a much larger set of inputs and with many controlled corpora at once.
It also fails when there are few streams to compare, as when a new story first breaks. Or when there is coordination of actors.
My suggestion is not that it is fail safe or perfect, only that is a useful application of an ontology which disputes the original author's thesis that they are overrated.
All of these things are hallmarks of fake news: it is claimed to be new (hence "news",) is not published very widely (you can't compare a aquariusrising.biz article on Green Space Aliens to a CNN article on the same, because there isn't a CNN article on that), and it's usually coordinated between copycat opportunists. Even worse, none of these things are distinguishable from a grassroots report unless you know the true news a-priori. This better not be a mechanism to ban everything that isn't on CNN.com.
You might be able to say that fake news doesn't align with true facts that aren't part of the facts being reported as news. That is you can tell that "the color of the sky unexpectedly changed from green to yellow yesterday" is fake news, but you can't use true facts to tell that ""the color of the sky unexpectedly changed from blue to yellow yesterday" is fake news. (I mean, yes, you might still use true facts about atmospheric composition, light scattering, and the spectrum of solar output, but you can't use "the sky is blue".)