In terms of practicalities, AI and AGI share two letters in common, and that's about it. OpenCog / CogPrime is at core nothing more than an interface language specification built on hypergraphs which is capable of storing inputs, outputs, and trace data for any kind of narrow AI application. It is most importantly a platform for integrating narrow AI techniques. (If you read any of the official documentation, you'll find most of it covers the specific narrow AI components they've selected, and the specific interconnect networks they are deploying. But those are secondary details to the more important contribution: the universal hypergraph language of the atomspace.)

So when you say:

I am not familiar with OpenCog, but I do not see how it can address these sorts of issues.

It doesn't really make sense. OpenCog solves these issues in the same way: through traditional text parsing and logical inference techniques. What's different is that the inputs, outputs, and the way in which these components are used are fully specified inside of the system, in a data structure that is self-modifying. Think LISP: code is data (albeit using a weird hypergraph language instead of s-expressions), data is code, and the machine has access to its own source code.

That's mostly what AGI is about: the interconnects and reflection layers which allow an otherwise traditional narrow AI program to modify itself in order to adapt to circumstances outside of its programmed expertise.