Thanks Michael, (01)
My comments are below,
-Rich (02)
Sincerely,
Rich Cooper
EnglishLogicKernel.com
Rich AT EnglishLogicKernel DOT com
9 4 9 \ 5 2 5 - 5 7 1 2 (03)
Hello Rich, (04)
>Here is a recent patent I found a few
minutes ago
>which to some degree could be applicable,
though
>it is addressed to generating ontologies
for
>business applications: (05)
If I understand this correctly, it
describes a method of generating an
OWL ontology from an annotated XSD file.
All terms in the generated ontology
are already present in the XSD file or the
annotations. XML files using that
XSD can then be converted to triples using
that ontology. (06)
An approach like this enables you to query
the data with SPARQL - which may
be a progress over what you could do
before. But using reasoning or rules
for some advantage probably requires a
much bigger investment of time. (07)
Yes, this patent was simply one that shows
generation of an ontology. I am personally more
interested in the methods of generating ontology
from data than the specific use of OWL/XSD/SPARQL,
but those techniques are useful for the purpose of
seeing how these XML technologies are available. (08)
For example, the EHR technology is based on XML
structuring, and EHRs are required to meet the
specs on the CCR (continuing care record) and CCD
(continuing care document) so that there are
standard XML forms of the EHR. That means the EHR
information can be relied upon to provide SOAP
(subjective, objective, assessment and plan)
medical record in an XML supported document for
each patient. (09)
> By that description, I am envisioning a
system
> that searches (think And/Or graph
search) for the
> best explanation in a logical
combination of
> evidence fragments. (010)
Something like this ? (011)
http://www.aaai.org/Papers/Workshops/2006/WS-06-08
/WS06-08-010.pdf (012)
Thanks, that is a very interesting paper which I
have just put on my reading list. I like the way
they flexibly shift between belief systems and
search cones as explanations of the And/Or
solution forest. (013)
> So a discovery system that browses
through all
> that data could put together logical
combinations
> of evidence from each case, and produce
millions
> of explanations as logical combinations
of
> evidence terminals. Its that "logical
> combinations of evidence terminals" that
I am
> calling (perhaps inelegantly) an
ontology. (014)
What do you do with the millions of
"explanations" ? If you do not reason with
them, I would not see them as part of an
ontology. Isn't the system supposed to
yield the best "explanation" instead of
millions ? (015)
I was referring to creating an ontology using
automated methods. That means each ontology
explanation of the database of phenomenon stands
as an explanation of the phenomenon. If there are
millions of such explanatory ontologies, there is
value in finding the ones that appear consistently
in explanatory ontologies that also have specific
other explanations. That consistent pairing of
explanatory chunks can help users understand how
the pairing works, perhaps leading to new science
compared to doing without that pairing. (016)
> The reason I call it an ontology, even
though it
> was not put together by humans, is
because it
> represents the tightest explanation of
the
> evidence in Occam's sense. (017)
I would say what you mean is the smallest
set of ground facts with a
correlation to a given desease. If you
mean this, you can hardly call it
an ontology. (018)
I don't mean that. I mean the explanatory power
of automatically generated ontologies, supported
by ghastly large amounts of evidence, provides the
foundation for understanding phenomenon that are
far more complex than humans can generate on our
own. We would be more focused on the
understanding of such pairings of ontology phrases
than we could be otherwise; we would not have to
use human hours to produce potentially useful
leads, but instead be able to use the obsessive
power of the computing system to generate more
focused leads. (019)
> I am suggesting that perhaps we should
jettison the
> human generation of the ontology and
substitute a
> method for automatic generation of the
ontology
> without concern for whether a human
understands
> the reasoning at any intuitive level. (020)
I do not see any real reasoning in the
system you describe. (021)
I didn't intend to claim reasoning in the system,
other than the logical combination of
explanations. The reasoning is in the humans, who
would have less overwhelming databases, instead
using the generated explanation logic, i.e., the
automatically generated ontologies. (022)
Regards, (023)
Michael Brunnbauer (024)
Thanks for your thoughts, and especially the
reference you posted, (025)
-Rich (026)
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