Replying from my laptop. I agree, there needs to be a way to represent relationships between information. I personally don't think knowledge graphs will be the

Replying from my laptop. I agree, there needs to be a way to represent relationships between information. I personally don't think knowledge graphs will be the ones to do it, not because they dont work, but because of how imperfect they are, in the data quality sense. See this paper here: "DIFFERENTIABLE REASONING OVER A VIRTUAL KNOWLEDGE BASE" https://openreview.net/pdf?id=SJxstlHFPH https://openreview.net/pdf?id=SJxstlHFPH Which is a recent effort, among many, by google research to build a model that can view a document as a knowledge graph, instead of explicitly tying pieces of the document to the graph, the idea to is to create a graph from the document. This is paper is a bit different from that, they do input a knowledge graph for training, but I think the idea and track of where they are headed has a ton of room to evolve. The trick is that transformer models have unlocked the ability to understand the text, so all of this "quasi knowledge graph extraction" that i was just explaining, is only recently possible! There's no research on it, because the baseline understanding of tokens has been too primitive. This is why there is so much room to grow, BERT has unlocked new methods, it can be used as a base for a ton of new NLP. Just to emphasize again, I'm not saying what I outlined above will be a good way to do it, just that ideas like this could only be tested recently. There's a million new ways to spin this problem.