It takes knowledge to even know they're flawed, noisy, and disconnected. There's no reason to "correct" anything, unless you have knowledge that applying previously "understood" data has in fact produced deficient results in some application.

That's reinforcement learning -- an algorithm, which requires accurate knowledge acquisition, to be effective.