And here is a patent that can support
query formation for the And/Or search process:
-Rich
Sincerely,
Rich Cooper
EnglishLogicKernel.com
Rich AT EnglishLogicKernel DOT com
9 4 9 \ 5 2 5 - 5 7 1 2
From: ontolog-forum-bounces@xxxxxxxxxxxxxxxx
[mailto:ontolog-forum-bounces@xxxxxxxxxxxxxxxx] On Behalf Of Rich Cooper
Sent: Saturday, May 11, 2013 1:26
PM
To: '[ontolog-forum]
'
Subject: Re: [ontolog-forum] Genetic discovery using ontology
mapping ofobservations
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:
A PDF of the patent file is attached, or
you can look it up at Patent2PDF or at the USPTO by number 8214401, or even at
google patents.
This one is just an example; I am finding
lots more in my patent search for this kind of thing.
-Rich
Sincerely,
Rich Cooper
EnglishLogicKernel.com
Rich AT EnglishLogicKernel DOT com
9 4 9 \ 5 2 5 - 5 7 1 2
From: ontolog-forum-bounces@xxxxxxxxxxxxxxxx
[mailto:ontolog-forum-bounces@xxxxxxxxxxxxxxxx] On Behalf Of Rich Cooper
Sent: Saturday, May 11, 2013 12:50
PM
To: '[ontolog-forum]
'
Subject: Re: [ontolog-forum] Genetic discovery using ontology
mappingofobservations
Dear John and Michael,
RC
> Could a genetic
ontology be useful for mapping the disease biochemistry
> and environmental
exposures to genetic profiles? Perhaps such an ontology
> could be constructed
automatically, step by step, through identifying
> subjects with known
genetic spectrum and known environmental exposures
> versus diagnosed
conditions.
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.
There are probably lots of diseases that require multiple
facts (genes, environmental facts, diagnoses). There certainly are lots
of XML based EHR data files now, and with the 2013 deadline on EHR usage set by
Bush already here, there will be millions of them in the near future.
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.
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. Whether the explanations are comprehensible in
medical terms is another story - one I am willing to ignore for generating the
ontology.
MB
> But why should
ontology-based tools be better than the tools already used?
> You would have to
have a close look at those tools and the problems they
> solve to answer that
question.
The reason I think such an automated generator of ontologies
might be useful is precisely because there is so much evidence available, that
humans can't possibly keep up with it all. Therefore the human generated
ontology is less likely to happen than the
automated one.
JS
>Yes, that is the
fundamental question. Scientists in >every field have
>developed highly
sophisticated tools for analyzing and >reasoning
about
>their subjects.
>
>In almost all cases,
their tools are far more precise, >detailed,
>and sophisticated
than the tools currently available for >ontology.
>The Cyc tools are
rather sophisticated, but they offer >little
help
>for the
sciences. And compared to Cyc, OWL is >pathetic.
Yes but. All these tools have been built centered
around human comprehensibility of the ontology so generated. It is
intended to record and automate human logic, not the logic of evidence at
hand that drives the design of these tools. 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. Instead, the human would
simply follow the evidence trail generated
by the auto-ontology, and then pursue attempts to implement the solutions
generated rather than to try to understand them.
Once an auto-ontology generated solution is seen to work in experimental
confirmation, then the human analysts can begin trying to understand why the
logical combination of terminal evidence acts as it does to produce the disease
state or to eliminate it. So
the human learning of why it works comes AFTER the ontology has been generated,
not before.
In the first decades of the 1900s, Somebody Yates invented
the Yates transform. It worked so well that
later people invented signal processing algorithms based on the Yates
transform. That is the sort of thing I am suggesting here. But with
ontologies, not with signals.
-Rich
Sincerely,
Rich Cooper
EnglishLogicKernel.com
Rich AT EnglishLogicKernel DOT com
9 4 9 \ 5 2 5 - 5 7 1 2
-----Original Message-----
From: ontolog-forum-bounces@xxxxxxxxxxxxxxxx [mailto:ontolog-forum-bounces@xxxxxxxxxxxxxxxx]
On Behalf Of John F Sowa
Sent: Saturday, May 11, 2013 4:57 AM
To: ontolog-forum@xxxxxxxxxxxxxxxx
Subject: Re: [ontolog-forum] Genetic
discovery using ontology mapping ofobservations
Rich and Michael,
I agree with Michael's answer to Rich's question, but I'd
like to add
a few more comments.
RC
> Could a genetic ontology be useful for mapping the
disease biochemistry
> and environmental exposures to genetic profiles?
Perhaps such an ontology
> could be constructed automatically, step by step,
through identifying
> subjects with known genetic spectrum and known
environmental exposures
> versus diagnosed conditions.
MB
> But why should ontology-based tools be better than the
tools already used?
> You would have to have a close look at those tools and
the problems they
> solve to answer that question.
Yes, that is the fundamental question. Scientists in
every field have
developed highly sophisticated tools for analyzing and
reasoning about
their subjects.
In almost all cases, their tools are far more precise,
detailed,
and sophisticated than the tools currently available for
ontology.
The Cyc tools are rather sophisticated, but they offer little
help
for the sciences. And compared to Cyc, OWL is pathetic.
MB
> A data model is not an ontology because it serves the
needs of a specific
> application. But what needs does an ontology
serve? This data model /
> ontology distinction puzzles me more and more.
I don't blame you for being puzzled. The simplest explanation
is that
they came from different sources. The term 'data model'
originated
in the "database wars" of the 1970s. There
were three competing
technologies, each with its preferred "data
model": hierarchical
(IMS), network (Codasyl DBTG), and relational.
The goal of the conceptual schema was to allow software to
access
data in any format, independent of the way it was
stored. In fact,
its goals were similar to the original goals by Tim
Berners-Lee
for the Semantic Web.
But the same mentality that dragged the SW down to little
more than
YADM (Yet Another Data Model), the hopes for the conceptual
schema
were never realized.
John
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