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The Open world assumption shoe does not always fit - was: RE: [ontolog-forum] Fwd: Ontolog invited speaker session - Dr. Mark Greaves on the Halo Project - Thu 2008.06.19

From: Ogbuji, Chimezie <OGBUJIC@ccf.org>
Date: Thu, 26 Jun 2008 18:31:07 -0400
Message-ID: <2702D0EBA4F0A749968E52E8644184EA01DE9AAC@CCHSCLEXMB59.cc.ad.cchs.net>
To: "Pat Hayes" <phayes@ihmc.us>
cc: "Adrian Walker" <adriandwalker@gmail.com>, "[ontolog-forum]" <ontolog-forum@ontolog.cim3.net>, "John F. Sowa" <sowa@bestweb.net>, welty@watson.ibm.com, semantic_web@googlegroups.com, "public-semweb-lifesci hcls" <public-semweb-lifesci@w3.org>, semanticweb@yahoogroups.com

Hey, Pat.  Comments below
>	I would disagree about this case being the exception.  
>Negation as failure can be validly used to infer from a 
>failure if the data is controlled (which is especially the 
>case with well-designed experiments where it would be 
>irresponsible to to do otherwise).
>What are you referring to by "well-designed experiments"?

"Well-designed experiments" is probably not a useful characterization,
so let me try again.  Let's say you are nurse performing a history and
physical assesment on a patient in order to make entries into his/her
medical record and one of the questions you ask *routinely* is whether
or not the patient has a particular symptom/problem: headaches for
instance.  If the patient says: "no", and you are conforming to default
negation as part of a subsequent analysis, then it would seem sufficient
to not make any assertion about the existence of a headache.  Otherwise,
you would need to be able to either infer that the patient doesn't have
a headache (provably false) *or* have an explicit assertion of absence:


_:a a cpr:patient


_:a a cpr:patient
_:a a cpr:PersonWithoutHeadache

My point is that, in the first model you *can* infer that the patient
doesn't have a headache because the assertion is missing and you *know*
that the question was asked.  The assumption that knowledge is *always*
incomplete seems (to me) to not account for situations where the data is
indeed complete specifically because the process of collecting it is

>OK, but that does not ensure that if P is not asserted, then 
>it is known to be false. In fact, rather the opposite.

But it does ensure this - precisely as part of the requirements at the
point where the data is collected.  My general point is that where you
have decent control over the quality of the data in general or at least
in the process of how it is populated, default negation can be a useful
tool for allowing a person doing analysis to make (safe) assumptions
from the absence of data.

>You can make such inferences validly only when you have some 
>reason to suppose that if the proposition were true, the 
>content would not be missing. 

Right, the reason in this case would be knowledge of the expectations of
how the data is collected.

>I don't mean to deny that such 
>circumstances do exist, in some cases with an explicit warrant 
>for the entailment, but they are certainly not the usual case. 
>The usual case is that your knowledge is incomplete. Our 
>knowledge of almost everything is incomplete.

Certainly, but I'm simply pushing back (slightly) on what often is the
typical argument against non-monotonic inference: the claim that
knowledge is *always* incomplete regardless of how you come about it.

>	without the burden of classic negation, which requires 
>a significant amount of effort

>Nonsense. There is no 'burden' of classical negation. Negation 
>IS classical negation. If you conclude that P is false, and 
>express this using a negation connective, you are using 
>classical negation. (If your *conclusion* from a failure to 
>prove P is that P is not proven, then your reasoning is 
>completely classical also; but then you are only concluding 
>failure, not negation from failure.)

The burden I was refering to, is the difference between the simple (and
controlled) absence of assertions and needing to either conclude that
the assertion is false (via inference) or having to express it
explicitely (i.e. I. versus II. From the above example with the patient
and his/her headache).  Note, I'm pushing back slightly on what often
seems like unadulturated dogma, not trying to be dogmatic myself :)

	(either having a large amounts of assertions about 
>class disjointedness, etc. or requiring explicit assertions 
>about the absence of data)
>You have to say what is true in order to draw reliable 
>conclusions from it. 

Or, alternatively, you control the process of how the data is populated
in order to draw (simple) conclusions that don't require significant
inference. No?

>This can be done in tedious ways or, with 
>the right notations and conventions, more compactly. You are 
>reacting against some of the more tedious notational results 
>of using simple textbook logics. But if you want to be able to 
>infer, from the fact that something is in a class A, that it 
>is not in another class B, then you must have some way to know 
>that A and B are disjoint. Because if they aren't, that 
>conclusion is not valid. No amount of grumbling about 
>classical negation is going to get over that basic fact. 

I'm not grumbling about it, I'm just trying to demonstrate that there
are situations where default negation (by itself) is a responsible means
to conclude the absence of some assertion.

>	to ensure that you can prove  that P is false.

>You have to know somehow. You might just know because someone 
>told you, 
> or because you know that if it were true then you 
>would have it recorded, and you don't. But you have to know 

Of course, I'm suggesting that there are valid, controlled scenarios
where you *do* know this.

Chimezie (chee-meh) Ogbuji
Lead Systems Analyst
Thoracic and Cardiovascular Surgery
Cleveland Clinic Foundation
9500 Euclid Avenue/ W26
Cleveland, Ohio 44195
Office: (216)444-8593

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Received on Thursday, 26 June 2008 22:32:04 UTC

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