Why Hypergraphs? by Linas Vepstas.
From the post:
OpenCog uses hypergraphs to represent knowledge. Why? I don’t think this is clearly, succinctly explained anywhere, so I will try to do so here. This is a very important point: I can’t begin to tell you how many times I went searching for some whiz-bang logic programming system, or inference engine, or theorem-prover, or some graph re-writing engine, or some probabilistic programming system, only to throw up my hands up and realize that, after many wasted hours, none of them do what I want. If you’re interested in AGI, then let me assure you: they don’t do what you want, either. So, what do I want them to do, and why?
Well, lets begin easy: with graph re-writing systems. These days, almost everyone agrees that a great way to represent knowledge is with graphs. The structure IsA(Cat, Animal) looks like a graph with two vertexes, Cat and Animal, and a labelled edge, IsA, between them. If I also know that IsA(Binky, Cat), then, in principle, I should be able to deduce that IsA(Binky, Animal). This is a simple transitive relationship, and the act of logical deduction, for this example, is a simple graph re-write rule: If you see two IsA edges in a row, you should draw a third IsA edge between the first and the last vertex. Easy, right?
So perhaps you’d think that all logic induction and reasoning engines have graph rewrite systems at their core, right? So you’d think. In fact, almost none of them do. And those that do, do it in some internal, ad hoc, non-public, undocumented way: there’s no API, its not exposed externally; its not an ‘official’ part of the system for you to use or tinker with.
You know how I feel about AI triumphalism so I won’t bother to repeat the rant.
However, the hypergraph part of this work looks interesting. Whatever your views on AI.
A good place to start would be the OpenCog Development page.