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Indeed. I wonder if the networks could learn a more and more general description of the concepts in the hierarchy when going up that hierarchy. E.g. there's a bunch of tiger species [0] each with a specific underlying model, but some traits are common to all tiger species. And some traits are common to, say, carnivores. Could you share parts of the model/network via that hierarchy, e.g. that a tiger inherits some parts of the model for carnivores etc.

For example, can the 'stripe model' or 'fang model' be shared among tigers and carnivores etc.

I tried something similar for my thesis, but this was before the advent of DNNs etc.

[0] http://conceptnet.io/c/en/tiger?rel=/r/IsA&limit=1000

This is spot on. What makes the above probabilities look incorrect is that people are assuming that the algorithm understands the relationship between tiger and animal the same way that humans do. Clearly they are evaluating each independently.

Or even something like a tree of life diagram entered in. (I forget what those are called)