Hi Linas, 

How do you propose to learn an ontology from the data -- also, what purpose 
would, in your opinion, the learned ontology serve. Or stated differently, 
in what way are you thinking to engender higher-level cognitive 
capabilities via machine learned bundled neuron (and implicit ontologies, 
perhaps).

thank you,

Daniel


On Wednesday, 19 April 2017 03:40:47 UTC+3, linas wrote:
>
>
>
> On Tue, Apr 18, 2017 at 3:22 PM, Alex <alexand...@gmail.com <javascript:>> 
> wrote:
>
>> Maybe we can solve the problem about modelling classes (and using OO and 
>> UML notions for knowledge representation) with the following (pseudo)code
>>
>> - We can define ConceptNode "Object", that consists from the set or 
>> properties and functions
>>
>> - We can require that any class e.g. Invoice is the inherited from the 
>> Object:
>>   IntensionalInheritanceLink
>>     Invoice
>>     Object
>>
>> - We can require that any more specifica class, e.g. VATInvoice is the 
>> inherited from the more general class:
>>   IntensionalInheritanceLink
>>     VATInvoice
>>     Invoice
>>
>> - We can require that any instance is inherited from the concrete class:
>>   ExtensionalInheritanceLinks
>>     invoice_no_2314
>>     VATInvoice
>>
>
> If you wish, you can do stuff like that. opencog per se is agnostic about 
> how you do this, you can do it however you want. The proper way to do this 
> is discussed in many places; for example here: 
> https://en.wikipedia.org/wiki/Upper_ontology 
>
> I'm not particularly excited about building ontologies by hand, its much 
> more interesting (to me) to understand how they can be learned 
> automatically, from raw data. 
>
>>
>> But I don't know yet what can and what can not be the parent for 
>> extensional and intensional inheritance. Can an entity be extensionally 
>> inherited from the more complex object or it can be extensionally inherited 
>> from empty set-placeholder only. When we introduce notion of set, then the 
>> futher question always arise - does OpenCog make distinction between sets 
>> and proper classes?
>>
>
> Why? This "distinction" only matters if you want to implement set theory. 
> My pre-emptive strike to halt this train of thought is this: Why would you 
> want to implement set theory, instead of, say, model theory or universal 
> algebra, or category theory, or topos theory?  why the heck would 
> distinguishing a set-theoretical-set from a set-theoretical-proper-class 
> matter? (which oh by the way is similar but not the same thing as a 
> category-theoretic-proper-class...)
>
> You've got multiple ideas going here, at once: the best way to hand-craft 
> some ontology; the best theoretical framework to do it in; the philosophy 
> of knowledge representation in general... and, my personal favorite: how do 
> I get the machine to do this automatically, without manual intervention?
>   
>
>>
>> There is second problem as well - there is only one - mixed 
>> InheritanceLink. One can use SubsetLink for the extensional inheritance 
>> (still it feels strange), but there is certainly necessary syntactic sugar 
>> for intensional inheritance, because it is hard to write and read 
>> SubsetLink of property sets again and again (
>> http://wiki.opencog.org/w/InheritanceLink).
>>
>
> If the machine has learned an ontology with a million subset links in it, 
> no human being is ever going to read or want to read that network. It'll be 
> like looking at a bundle of neurons: the best you can do is say "oh wow, a 
> bundle of neurons!"
>
> --linas 
>
>>
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