SUO: RE: Re: Architecture of an intelligent ontology development algorithm
Jon, I still like the advice you gave a few iterations ago,
so I snipped it out of some recent emails and reapplied it
as below:
Jon Awbrey wrote:
<snip/>
> > Rich,
> >
> > I would have thought that a fairer summary of what's been said here
> > all summer long on many different threads is that there is really no
> > such thing as a hypothesis-free algorithm for discovery -- actually,
> > that's more like a summary of what's been discovered about discovery
> > over the last few thousand summers, but who's counting? So I think
> > that a better question might be something along the following lines:
> >
> > How are concept-driven (analytic, axiomatic, rationalist,
> top-down) methods
> > and data-driven (synthetic, contingent, empiricist,
> bottom-up) procedures
> > best to be integrated in human inquiry, or in the
> reconstitutions thereof,
> > given that the distinction between analytic and synthetic
> is more relational,
> > interpretive, or "situated" than it is absolute, invariant,
> or "essential"?
<snip/>
This succinctly states the core idea of how an automated or even
computer-aided tool could proceed to generate useful ontologies.
Hypothesis formation is based on learned conceptualizations from
past experience. I'm still reading your refernce to "anticipatory" systems
( http://www.anticipation.info/texte/rosen/anticipatorysystems_rosen.pdf )
but I like the general tone of a faster-than-real-time model M
guiding a real time situated system S in modifying its behavior.
However, the linear system analogy is a bit antiquated, and the
term "hypothesis formation" (or "abduction" as you Peirceans
prefer) isn't conceptually compatible with a linear system model.
I like the GA and GP models better. Using symbolic expressions
rather than linear projections seems more intuitive and natural
for the areas I like to concentrate on. Forming new expressions
from combinations of older expressions makes it possible to trace
the formation of hypotheses to experiences, much like requirements
can be made traceable to designs using a graph model. So a GP
model is probably most natural.
Then there's the issue of where data comes from to drive the
formation of hypotheses. For an automated system to reach "good"
hypotheses, it should have "good" data - data from naturally
occuring intelligences. So I subscribe to the idea of data mining
of English text that has been well written, well annotated, and
reviewed by various interested parties.
It seems to me that the nominal task of the SUO group could be best
addressed in producing such a tool (automated, or computer-aided,...)
to browse English sentences, data models, UML designs, CGs, or
other data we would consider useful for the purpose. At least
that approach avoids the legislative extremes, because we can
argue about what data to use, what methods of representation,
and how the system should function instead of what the ontology
itself is.
We seem to be running dry on ontologies themselves.
JMHO,
Rich