On 14 Sep 2007, at 14:12, Chimezie Ogbuji wrote:
[snip]
But I don't know of a way (at least a standard way) to go from first
order horn (without function symbols) to Datalog under LP semantics
for beyond ground entailments. My intuition sez it'd be pretty bad.
I would agree about this being p
On Thu, 2007-09-13 at 21:54 +0100, Bijan Parsia wrote:
> Yes. It's not a mapping from DL to LP. But from DL to Horn Logic.
(restricted) DL -> definite Horn -> definite Logic Programming (ground,
fact-forming entailments only)
> > It is defined as a function whose input are DL expressions
> > (an
On Sep 13, 2007, at 6:25 PM, Chimezie Ogbuji wrote:
On Thu, 2007-09-13 at 16:44 +0100, Bijan Parsia wrote:
I trimmed the ccs since I get scared if I have to scroll a cc list.
Thanks, I have a bad habit of not doing that :)
Eh. Not really. First of all, "domain of discourse" has a couple of
Also, what would be great is to get a concrete real world example
which
illustrates the above. The example given by Kavitha, I believe has
a SQL
translation. Getting such examples are crucial to showing the value
of the web.
Vipul, just to clarify, which example are you referring to when
On Thu, 2007-09-13 at 16:44 +0100, Bijan Parsia wrote:
> I trimmed the ccs since I get scared if I have to scroll a cc list.
Thanks, I have a bad habit of not doing that :)
> Eh. Not really. First of all, "domain of discourse" has a couple of
> specific technical meanings so we should be a bit
I trimmed the ccs since I get scared if I have to scroll a cc list.
On 13 Sep 2007, at 15:21, Chimezie Ogbuji wrote:
On Wed, 2007-09-12 at 14:42 +0100, Xiaoshu Wang wrote:
Chimezie Ogbuji wrote:
[snip]
But please also see how dangerous such practice will be: "Ian
Horrocks1,
Bijan Parsia,
On Wed, 2007-09-12 at 14:42 +0100, Xiaoshu Wang wrote:
> Chimezie Ogbuji wrote:
> >
> > In SPARQL, the combined use of FILTER/!/BOUND effectively gives you a
> > mechanism for matching records with non-monotonic mechanisms without an
> > entailment regime. This is how we are able to *explicitly*
> The data complexity of EL++ suggest strongly that a sensible
> reduction to SQL is unlikely (i.e., you'll need datalogesque rules as
> well).
[VK] The interesting question in my mind then is what is the additional
functionality achieved by these datalogesque rules that are not present in
SQL?
On Sep 12, 2007, at 4:30 PM, Kashyap, Vipul wrote:
[snip]
In terms of whether you can do this using SQL querying
alone, based on our experience, its unlikely. The problem is that
the types of clinical exclusion and inclusion criteria we saw on
clinicalTrials.gov cannot be easily reduced to SQL
> We perform a more simplistic set difference wherein we first find all
> patients that satisfy the inclusion criteria and then exclude
> patients that satisfy the non-negated exclusion crtieria. Sorry for
> the terse explanation, we describe our methodology in detail in this
> draft appearing in
> Just a quick correction -- the SHER reasoner is different from the
> CEL reasoner, because it is built on
> the standard tableau algorithm (internally SHER uses Pellet). It
> supports the SHIN subset of DL
> (in OWL DL terms, no nominals).
[VK] Thanks for the clarification. Now it falls into p
Kashyap, Vipul wrote:
I guess the issue then becomes for which data items/decision
criteria is
negation explicitly asserted (MRSA) vs it needs to be inferred (drugs)
Also, is it the case that one can make this statement about all
labs without
loss of generality? Or can this be said only in
At 08:06 AM 9/12/2007, Kavitha Srinivas wrote:
1. Yes, as Chintan said, in the case where you had explicit
negations in the data (e.g., the lab data rules out the presence of a
certain infectious agent), you clearly want to use open world
reasoning. However, if someone is not explicitly asser
[VK] It will be great if you could share specific examples of some
criteria that
were not expressible in SQL. We can then incorporate those into the
use
case and help make a case for SW technologies. On the other hand,
taking a quick
look at the SHER project at IBM, looks like you are using
> However, if someone is not explicitly asserted to be on
> some prescription drug, it is fair to assume that they are not taking
> the drug (closed world assumption).
[VK] The key issue is how well this assumption is likely to work in practice.
Guess we need some experimentation to get at this.
1. Yes, as Chintan said, in the case where you had explicit
negations in the data (e.g., the lab data rules out the presence of a
certain infectious agent), you clearly want to use open world
reasoning. However, if someone is not explicitly asserted to be on
some prescription drug, it i
Chimezie Ogbuji wrote:
In SPARQL, the combined use of FILTER/!/BOUND effectively gives you a
mechanism for matching records with non-monotonic mechanisms without an
entailment regime. This is how we are able to *explicitly* ask for the
absence of an assertion based only on what the RDF dataset
> Agree. The assumption is that the user will choose whether it is
> closed world or open world. The key point that we've observed in
> terms of our clinical trials matching work using ontologies is that
> you need BOTH options to be available to correctly translate the
> exclusion criteria into
On Wed, 2007-09-12 at 09:31 +0100, Xiaoshu Wang wrote:
> You SHOULD not choose and you have to use open world reasoning because
> how someone can tell which part of the world is closed and which part is
> not.
Sorry, Xiaoshu, but I don't agree that you *have* to use open world
reasoning. That
> You SHOULD not choose and you have to use open world reasoning because
> how someone can tell which part of the world is closed and which part is
> not.
[VK] I think this is a good design principle we should consider when creating a
solution to the use case.
Open World Assumption + Local Clos
Chintan Patel wrote:
Regarding negation of exclusion criteria, it is interesting that you
mention open versus closed world reasoning. We have found that
depending on the underlying clinical data being queried, we might need
to choose between open and closed world reasoning.
You SHOULD not choo
> For example, in pharmacy data, if the patient record does not mention
> a drug, we can be reasonably sure that the patient is not on that
> drug -- a case for closed world reasoning, whereas for other datasets
> such as lab or radiology, often things are explicitly asserted to be
> negative if
Hi Alan,
Regarding negation of exclusion criteria, it is interesting that you
mention open versus closed world reasoning. We have found that
depending on the underlying clinical data being queried, we might
need to choose between open and closed world reasoning.
For example, in pharmacy
My guess (and some based from my own experience), is that it supports the way a
lot of non-technical clinicians like to organize the process: 1) what I want, 2)
what I must avoid.
[VK] That's a great insight and should probably guide the design of the
information system when it comes to displaying
: Andersson, Bo H; Landen Bain; Rachel Richesson; public-semweb-lifesci hcls;
[EMAIL PROTECTED]; Stanley Huff; Yan Heras; Oniki, Tom (GE Healthcare,
consultant); Joey Coyle; Bron W. Kisler; Ida Sim
Subject: [BIONT-DSE] Inclusion versus exclusion criteria
Something that I remain confused about is why
Something that I remain confused about is why there are two
categories of criteria, when, on the face of it, the negation of an
exclusion criterion is an inclusion criterion.
So I'm wondering, is there something between the lines? Is there
something other than the negation? Perhaps kind o
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