Hi Ivan, 

Your work sounds very exciting ... would be great to hear more about it. 

I think one issue with the approach you are describing is that you have to 
assume the knowledge of a second language and a mapping, in principle, from 
the first to the second. 

I think systems that aim to self-learn (unsupervised) try to omit such an 
a-priori mapping because it would (presumably) make the knowledge capture 
process non-scalable. 

So, you end up with a system that tries to self learn meaning of system A 
on its own terms (and via "meta-cognitive" strategies derived from the 
machine learning approach at hand- which are by definition meaning 
agnostic) ...  so i wonder where is the meaning in this kind of machine . 
-- if the semantic graph is actually constructed out of the machine learned 
parse of natural language text without a predefined mapping to a semantic 
graph (which is what ones want to build in the first place).

I think this is essentially what confuses me -- if i managed to explain it 
correctly ... .

Daniel


On Friday, 14 April 2017 14:07:08 UTC+3, Alex wrote:
>
> Hi!
>
> What is the best texbook (most relevant to Opencog Node and Link Types) in 
> Knowledge representation? I am aware about books about PLN and egineering 
> AGI (and I am reading them and they are relevant to probabilisti reasoning 
> side of knowledge represenatation), but I feel that e.g. concepts of 
> inheritance (extensional and intensional) as adopted by OpenCog Atomsapce 
> is coming from earlier work - so from what work? I would like to see this 
> work, to include it into broader context. I have adapted to UML, ER, OO 
> design and I am still struggling to model knowledge using OpenCog nodes and 
> links. That is why I am seeking more books to dive into this line of 
> thinkin.
>
> I am reading now:
> Knowledge Representation and Reasoning (The Morgan Kaufmann Series in 
> Artificial Intelligence) 
> <https://www.amazon.co.uk/Knowledge-Representation-Reasoning-Artificial-Intelligence/dp/1558609326/ref=sr_1_1?s=books&ie=UTF8&qid=1492167755&sr=1-1&keywords=knowledge+representation>17
>  
> Jun 2004
> by Ronald Brachman and Hector Levesque Dr.
>

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