Agree with @PaulHoule, that basic RDF triple style KB's probably aren't sufficient in and of themselves. Buuuttttt... You can "cheat" a bit and use reification

Agree with @PaulHoule, that basic RDF triple style KB's probably aren't sufficient in and of themselves. Buuuttttt... You can "cheat" a bit and use reification so that triples can themselves become the subject of another triple, which lets you create "subjects" like "bayesianPrior" or "strengthOfBelief" or "beliefHeldSinceDate" and other things that you can then wire into your reasoning system, to enable bayesian reasoning, modal logics, temporal logics, etc. Given all of that, can you represent everything you'd need for AGI? Weeeelll... a qualified "maybe" seems appropriate here. The thing is, we don't have AGI, so nobody can say with absolute certainty what we need to enable it. A complete answer to that question would be tantamount to developing AGI which would make the whole discussion moot. But I think it suffices to say that there is "some there, there" in terms of using semantic KB's in conjunction with LLM's to enable richer experiences that have "more intelligent" behavior. Will it get to AGI? No idea. But it's something. There's still a bit of "stuff" to question though. For example, how will an AGI handle paraconsistent logic? That is, logic that deals with contradictions better than classical logic, where a contradiction in the premises allows literally anything to be "proven". Humans deal with (apparent) contradictions more or less seamlessly most of the time, albeit experiencing various degrees of cognitive dissonance when trying to consider mutually contradicting beliefs that are both held as true. Just so long as we don't have to collapse our beliefs down to the level of rigour of classical logic, we can usually deal with contradictory beliefs. Making a machine deal gracefully with that stuff is still an open area of research. Anyway... if you are interested in pursuing this further, one specific project I'm familiar with (but not directly involved with) is PR-OWL or "Probabilistic OWL" which adds Bayesian probability to OWL. https://www.pr-owl.org/ https://www.pr-owl.org/