Dear colleagues,
Clearly, man-made constructs such as
organizations and machines are designed in an attempt to eliminate
ambiguity and reduce approximation in order to better function or to operate at
all.
John Sowa was nice enough to share his Knowledge
Soup and Analogy papers with me much earlier in this discussion thread. As
a result of reading these fine papers, I am attaching the following typology and
ontology(?) for discussion. I am an advocate for the explication of
generic Knowledge Representations that not only describe concepts and their
contexts, but also represent knowledge as actionable forms that can be
applied to perform varied generic tasks through appropriate inferencing
mechanisms. I was hoping that this forum would at least attempt
to:
- Describe concepts as conceptual primitives or
building blocks within ontologies (beyond RDF),
- Provide alternative knowledge representations in
support of defining and describing upper ontologies, and
- Examine alternative inferencing
approaches to apply and manipulate ontologies in addition to
logic.
Best regards, Tom Beckman
301-920-0715
----- Original Message -----
Sent: Wednesday, February 07, 2007 3:04
PM
Subject: Re: [ontolog-forum] Visual
Complexity
>Attachment converted: betelguese2:mthworl2 1.gif (GIFf/«IC»)
(000E4A31)
>
>
>John, it is your use of "approximation" to
>characterize ALL models without exception here
>that I object
to. Of course, depending on what
>it in the world one is trying to
represent, a
>model *might* necessarily be an approximation,
>especially if one is modeling physical phenomena
>that are
inherently vague or (in effect)
>infinitely complex and hence which
simply cannot
>be represented with 100% accuracy. Consider,
>e.g., modeling a stochastic process or fluid
>flow by means of
probability theory or
>differential equations.
For the record, I
agree, of course. Tarskian
models can be platonic, and can be simplified
"models" (sense 2) of reality. But they can also
be actually made up
from real parts of the real
world.
>However, many physical
situations involve, *at a
>desired level of granularity*, NO vagueness
and
>NO intractable complexity at all, as in my
>previous example
involving faculty and
>administrators at Texas A&M. Many
ontologies
>involve this kind of sharply delineated,
>unambiguously representable information. Your
>diagram
above belies this fact and suggests that
>models are always in some way
false or
>inaccurate. It just ain't
so.
Quite.
Pat
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