Hello Rich, (01)
IMO, if your system is supposed to come up with something that really can be
called an ontology or an explanation of a phenomenon, it should have a search
tree that is so big it will never produce anything useful. (02)
Regards, (03)
Michael Brunnbauer (04)
On Sun, May 12, 2013 at 05:14:39PM -0700, Rich Cooper wrote:
> Thanks Michael,
>
> My comments are below,
> -Rich
>
> Sincerely,
> Rich Cooper
> EnglishLogicKernel.com
> Rich AT EnglishLogicKernel DOT com
> 9 4 9 \ 5 2 5 - 5 7 1 2
>
> Hello Rich,
>
> >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:
>
> 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.
>
> 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.
>
> 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.
>
> 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.
>
> > 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.
>
> Something like this ?
>
>
> http://www.aaai.org/Papers/Workshops/2006/WS-06-08
> /WS06-08-010.pdf
>
> 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.
>
> > 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.
>
> 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 ?
>
> 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.
>
> > 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.
>
> 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.
>
> 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.
>
> > 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.
>
> I do not see any real reasoning in the
> system you describe.
>
> 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.
>
> Regards,
>
> Michael Brunnbauer
>
> Thanks for your thoughts, and especially the
> reference you posted,
>
> -Rich
> (05)
>
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