Re: neural networks being purported as KR?

Thank you Paola for pointing this out.
Again I must beat the drum. 

Knowledge is much more than extracting structure from facts and data. If I just recall that the collection of facts is subject to the uncertainty principle, any structure deduced cannot be complete, and the application of free will, and/or axiom of choice create a dichotomy, knowledge is much more.
We are limited by our sensory apparatus, our hard wiring in our human brain, including the shortcuts made when processing visual data, and the limitations of natural language.
I agree that knowledge reasoning should be fairly straightforward, but making the jump from KR to knowledge itself implies we come up with some consistent many worlds modeling scheme in which the virtual, mathematical and (many interpretations of) the physical world coexist, reconciling incompleteness, uncertainty principle, sensory limitations and application of free will and choice.
A convergence of efforts by string theorists, researchers in human brain cognitive and biological structure fields, theoretical physicists and mathematicians working on finite groups, category theory, algebraic topology and logical structures for consistent super theories, and an odd mix of linguists and philosophers (including Buddhists) is doing just that.
But they are far from a consensus.
The point I am trying to make is that KR is more than semantics and ontologies and knowledge graphs, graphs, category theory diagrams and Feynmann diagrams and any other visualization tools we use.
The implicate order David Bohm theoreticized underlying quantum reality and the reality of our physical world, cannot be captured by some mix of formal logic, semantic structures, ontologies or computable frameworks.
And we we want someday A(G)I to be able to grasp human knowledge in general, we must create a growth path towards formal structures which have meta-layers above (knowledge) graphs, formal logic and ontologies.
Mathematically speaking, using formal logic, ontologies and generalized graphs is necessary but insufficient for this general formal structure.
And now I must add that deep learning and machine learning also fall short in terms of KR'
If we let computer scientists, logicians, mathematicians and software engineers try to come up with KR which is fit for the AI we envision we will need for future applications we will fail miserably.
We need neuroscientist, and specialists in the field of cognitive sciences, biologists and even psychologists, and philosophers and physicists to help us complete the general framework for knowledge, and to establish which parts can be effectively captured in a formal fashion, which provide suitable technologies and tools for KR.
Mike Bergman did a nice expose on knowledge graphs at:A Common Sense View of Knowledge Graphs

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A Common Sense View of Knowledge Graphs

This article, based on a comprehensive history and definitions of the concept, provides a common-sense view of h...
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But historically even mandalas qualify as knowledge graphs, in a very stylized way. And they can be used to visualize very complex mathematical structures without the use of edges or arrows, thus removing the time component associated with the transition the edge or arrow represents, making knowledge representation in a time-independent fashion possible.



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    On Thursday, July 25, 2019, 11:52:23 PM ADT, Paola Di Maio <paola.dimaio@gmail.com> wrote:  
 
 Sorry to bang on this topic, but its the task at hand at the moment
I just found an article, which is good scientific survey then  purports NN as a type of KR(casually sneaks in NN as the latest KR)
This is published in a Springer peer reviewed publication and my makes all of my hairs stand up on my head
This is the kind of rubbish that without further qualification is being passed downas the latest research, and  which the future generations of AI scientists are being fed-
wonder if anyone else has a problem with this proposition(sign of the times?)I am doing my best within my means to identify and contain this peril
Articlehttps://link-springer-com.nls.idm.oclc.org/article/10.1007/s00170-018-2433-8  

A survey of knowledge representation methods and applications in machining process planning

The machining process is the act of preparing the detailed operating instructions for changing an engineering design into an end product, which involves the removal of material from the part. Today, machining ...

Xiuling Li, Shusheng Zhang, Rui Huang… in The International Journal of Advanced Manu… (2018)