It's a weighted, labeled hypergraph, where a "symbol" can be represented by
a single node or a network-of-nodes/sub-graph. Ideally, Inference and
Hebbian Learning rules allow new "symbols" to form automatically as new
nodes are created to connect groups of related nodes. The weights of the
nodes are probabilistic truth values.

That's how I know to summarize it; hope it helps. Beware, I'm a hobbyist
lurker, not active on the project currently, and I may be completely wrong
or significantly misleading, with significant non-zero probability. ;-)
Read the wiki for further details and possibly more precision, though
beware the possibility of vastly outdated content, so the real
source-of-truth is the code on github.

All the Best,

Matt

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On Wed, May 3, 2017 at 1:36 AM, Vishnu Priya <[email protected]>
wrote:

> Hello All,
>
>  i read that, Atomspace is a neural-symbolic network. How?
> or is it only a weighted hypergraph? Could any one please explain?
>
> Thanks,
> Vishnu
>
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