Re: stratml vs cl

Thank you Dave for mentioning logical consistency. When you leave out the word logical it becomes consistency which is the key factor in any domain of discourse on science.
Category theory addresses consistency.
And consistency is what we need to apply to percepts in order to transform them into conceptual structures. Logic enters into the arena of natural language to formalize and create conceptual structures.
Biological systems indeed do NOT use logic, but the sensory systems that deal with percepts do need consistency and consistency is hardwired.
Adrian Bejan, the creator of constructal theory has an interesting take on the physics that also seem to dictate how biological entities react to their environment.
The non-formal, non-logical realm of knowledge representation is the arena of philosophers dealing with perception and conceptualization, quantum physicists, string theorists, Buddhist philosophers, psychologists and neuro scientists.
Category theory again plays a crucial role as an organizing framework.
And this is my field of interest and research.
And Dave is right, for practical applications we need only use category theory, conceptual structures. And in the debate about the ethical use of AI we need to determine whether AI must be able to emulate humans and e.g. be able to pass the Turing test or not.
The AI conceptual diagram used by the European Union is well thought, because if we apply the condition that we use consistency and implicit formal logical frameworks to model everything we end up with excluding the ability to pass the Turing test and be able to emulate humans in non-rational ways.
Reacting upon sensory input and acting upon this is as I said is hard wired into all living things, yet does not require formal systems and thus logic.
And I do not even want to enter into discussion about whether it is possible at all to formalize in a logical way the physical and thought processes underlying sensory perception and its "natural translation into language and formal systems.
That is the battle ground of Buddhists, philosophers, quantum and string theorist and those scientists in pursuit of Grand Unifying Theories and Theories of Everything.

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    On Friday, January 10, 2020, 7:48:43 AM AST, Dave Raggett <dsr@w3.org> wrote:  
 
 


On 10 Jan 2020, at 04:16, Paola Di Maio <paoladimaio10@gmail.com> wrote:
Dave

Is a formal KR really needed?  There is no evidence that biological systems use formal KR as opposed to other forms of computation.


This is an important question. It would probably require an essay, for which I do not have time. I ll try to be very brief- what doe we mean by formal?  (different levels of formalization?)- I think what we need is enough formality to support a) logic /reasoningb)robustness/repeatability/reliability consistencyc) verifiability/proof that a) is correct to some extent
I gave a talk once that was aiming to say natural language is sufficiently formalto enable abc, but not sure I fully managed to put my point across as crisply as i would have likedworkshop pagehttp://www.cs.stir.ac.uk/events/network-analysis/  
My slideshttp://www.cs.stir.ac.uk/events/network-analysis/slides/dimaio-analysis.pdf  

(I am indebted to Sowa for explaining this at length on ontolog forum) 
Regarding biological systems, we really dont know enough, I d say and biological systemsmay use different forms of communication than language as we know ituntil we evolve to communicate without language, some degree of formalization may be necessary/beneficial
The crux for me is consistency. ability to express intent and to follow through and verify it ETCfor this we normally require some degree of formalization. but if you can find a way Dave to achieve logical consistency without formalization I d be very interested:-)

Whilst there is general agreement on the value of graph representations, Industry is showing a lot more interest in Property Graphs than in RDF. This has two corollaries: the first is that Property Graphs are allegedly easier to work with, and the second is that formal semantics and logical deduction (at centre stage for the Semantic Web) are not important for the majority of industry use cases. 
As you hinted at, logical consistency can be considered in terms of robustness, repeatability, reliability and consistency over use cases of interest.  Learning is about adapting to new use cases which don’t quite fit the existing model.  An example is extending data types for people’s names to allow for accented characters in people’s names, or to allow for more than one family name (as is the case in Spain).  Today, adding support for such extensions involves contacting the IT department, as the semantics are implicit in the data queries embedded in application code, and hence require talking with programmers to make the changes.
Natural language semantics are established through usage by a community of language speakers. The meanings often change over time as new patterns of usage appear. Trying to formalise this would be both challenging and rather futile.  A better plan is to model how people learn new meanings from what they read and hear in conversations with other people or through listening to media. Formal languages have a role to play where the context is clearly defined and relatively static. However, for AI, those conditions typically don’t hold.
Best regards,
Dave Raggett <dsr@w3.org> http://www.w3.org/People/Raggett
W3C Data Activity Lead & W3C champion for the Web of things