Re: abstract vs concrete concepts. upper ontolologies

How's this -- A Practical Plan to Apply the Logic of Philosophy to AI -- for a non sequitur?

If there's interest in fleshing out the plan with performance indicators and stakeholder roles, ChatGPT stands ready to help us do so.
Owen Amburhttps://www.linkedin.com/in/owenambur/
 

    On Friday, June 27, 2025 at 10:47:06 AM EDT, Milton Ponson <rwiciamsd@gmail.com> wrote:   

 If we take into consideration the fact that philosophy is characterized generally by having five branches, that is (1) metaphysics, (2) epistemology,  (3) logic, (4) ethics and (5) aesthetics we can conclude that the abstract concepts can be categorized accordingly.
For completeness sake we can add philosophy of science, which roughly said looks at how methodologies evolve and shape rational thinking and empirical science.
Logic, linguistics and mathematics form the core to capture abstract concepts in formal systems that lend themselves for computation, as is obvious in LLMs.
But philosophy for practical purposes is a better organizing instrument for knowledge representation,  exactly because it includes but is not identical to epistemology.
And because language is the instrument of communication for philosophy,  we are back to my premise to use semiotics,  symbol sets, pictograms, (petro)glyphs and alphabets as the basis for abstraction.
Because empirical science generates (spectral) data, these must described as well both quantitatively and qualitatively.
And because we are trying to create open, inclusive, accountable, explainable,  trustworthy,  ethical and safe artificial intelligence that is life-centric (not human-centric, but safe for all life on Earth), the first four mentioned branches of philosophy form the basis for organizing knowledge and its representation.
Of these four the first three have already been studied in extenso, it is now only recently that ethics has joined the fray.
Again, I am working on producing several articles on both the organizing instruments and formal systems for (abstract) knowledge representation.
I hope I have clarified my approach, I am a Godelian mathematician, which means I acknowledge that universal theories of everything that are consistent and complete do not exist, but we can construct formal systems that can satisfy some requirements we may have for explaining empirical data, and allow computation.
And since metaphysics,  epistemology,  logic and ethics cover pretty much everything we want to capture in knowledge representation,  this should suffice for all fields of human activity and Science, Technology,  Engineering and Mathematics in which we want to utilize artificial intelligence. 
Milton PonsonRainbow Warriors Core FoundationCIAMSD Institute-ICT4D Program+2977459312PO Box 1154, OranjestadAruba, Dutch Caribbean

On Fri, Jun 27, 2025, 09:36 Paola Di Maio <paola.dimaio@gmail.com> wrote:


To understand why, in trying to capture the Knowledge Representation domain, which can be so elusivewe start with upper/top level ontologies,
There is a distinction between abstract vs concrete concepts
The majority of words in English *check the other languages? *estimated around 70 percent, refer to abstract concepts
These cannot be represented visually
Yet, these abstract concepts convey essential meaning and semantics for concrete terms 
abstraction is essential to intelligent reasoning

Upper Ontologies represent abstract categories require to add semantic dimensions to concrete terms and conceptsPDM

1. Existence and Reality

being / nonbeing

existence / inexistence

possibility / necessity

actuality / potentiality

________________________________

2. Time

duration

moment / instant

past / present / future

frequency

continuity / interruption

________________________________

3. Space and Place

position / location

distance / proximity

direction

extent / limit

movement through space

________________________________

4. Quantity and Measurement

number

magnitude

degree

proportion

comparison