Pat and Pat, (01)
I've been traveling for the past week, and I haven't had the time
to comment on (or even read) my email. But I finally have a bit
of time to make some comments. (02)
PC> The core issue is whether, in the early years, people develop
> a common ontology (obviously, not a formal one) and an associated
> vocabulary that is so close among individuals that it allows highly
> accurate communication *when restricted to the most basic concepts*. (03)
Much closer to the core is whether logic and ontology are fundamental
to the way people think. My guess (and the guess of many psychologists
and anthropologists) is that the prelinguistic years are spent in
building sensory-motor mechanisms that are closer to the mechanisms
of our fellow mammals -- especially the great apes -- than to anything
we have been programming on a digital computer. (04)
I do believe that logic and ontology are important, but not for the
most basic thinking processes of children (and adults). Following
are the slides of three lectures I gave last week that develop some
ideas related to that theme: (05)
http://www.jfsowa.com/talks/semtech1.pdf
Semantic Technology (06)
http://www.jfsowa.com/talks/semtech2.pdf
Logic, Ontology, and Analogy (07)
http://www.jfsowa.com/talks/semtech3.pdf
The Goal of Language Understanding (08)
Following are some excerpts from those slides that address issues
related to this thread. (09)
John
________________________________________________________________________ (010)
Concluding slide from the lecture on Semantic Technology: (011)
Technologies to Consider (012)
Natural languages:
* The ultimate knowledge representation languages.
* Capable of representing anything in human experience.
* Highly flexible and adaptable to changing circumstances.
* But not easy to implement on digital computers. (013)
Formal logics and ontologies:
* Precise and implementable on digital computers.
* Can be translated to natural languages.
* But inflexible, brittle, and uncompromising. (014)
Statistical methods:
* Flexible, robust, and designed to handle uncertainty.
* But there is an open-ended variety of different methods.
* Not clear how to relate them to language, logic, and ontology. (015)
Research issue: Find suitable combinations of the above.
________________________________________________________________________ (016)
Slide #5 from the lecture on Logic, Ontology, and Analogy: (017)
Notations for Representing Knowledge (018)
Writing a precise statement of knowledge in any language, even
one’s own native language, is not easy. (019)
As Whitehead said, "the problem is to discriminate precisely
what we know vaguely." (020)
Informal notations are useful, but many steps are needed to
convert them to a formal specification. (021)
Controlled English, as in CLCE, is easy for humans to read, but
humans require considerable training before they can write it. (022)
Conceptual graphs are an intermediate notation that can be used
in informal methods that can be made precise by systematic
steps.
________________________________________________________________________ (023)
Concluding slide from that lecture: (024)
Conclusions (025)
No evidence of formal logic as a prerequisite for learning,
understanding, or speaking a natural language. (026)
Common logical operators -- and, or, not, if-then, some, every -- are
present in every NL. But they are used in many different senses, which
include classical first-order logic as an important special case. (027)
Reasoning by analogy is fundamental. Induction, deduction, and abduction
are important, highly disciplined special cases. (028)
But analogy is a more general reasoning method, which can be used even
with images, prior to any version of language. (029)
No evidence of a highly axiomatized ontology for any natural language. (030)
But many important commonalities result from common human nature,
experience, and activities. (031)
Formal, logic-based systems with deeply axiomatized ontologies have been
fragile and limited in their coverage of natural language texts. (032)
Analogy-based systems with loosely defined terminologies can be far
more robust and efficient for many applications.
________________________________________________________________________ (033)
Slide #7 from the lecture on The Goal of Language Understanding (034)
Image-like Mental Models (035)
Modeling hypothesis by Kenneth Craik: (036)
If the organism carries a small-scale model of external
reality and of its own possible actions within its head,
it is able to carry out various alternatives, conclude
which is the best of them, react to future situations
before they arise, utilize the knowledge of past events
in dealing with the present and the future, and in every
way react in a fuller, safer, and more competent manner
to the emergencies which face it. (037)
The amount of information represented in an image is much
larger than any description in language or logic. (038)
And it is rarely expressed in words, even by adults. (039)
Mental models could be simulated as "virtual reality."
________________________________________________________________________ (040)
Concluding slide from that lecture: (041)
Conclusions (042)
Deductive methods are good when there are widely applicable theories,
as in physics, engineering, and established accounting procedures. (043)
When there are no reliable theories, analogical reasoning is necessary. (044)
Even when good theories are available, analogical reasoning can be a
valuable supplement for handling exceptions. (045)
Analogical reasoning can also be used at the metalevel to find mappings
between different theories and ontologies. (046)
But we are still very far from representing the level of language and
learning of a three-year-old child. (047)
Much more work is needed, especially in representing and processing
image-like mental models. (048)
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