Pat, (01)
>Can you point to where I can find out more? (02)
http://www.pr-owl.org is the web page my ex-student Paulo Costa
created for PR-OWL, the probabilistic ontology language. PR-OWL is
an OWL upper ontology in which you can augment ontologies with
probabilities using a first-order probabilistic logic called MEBN
(Multi-Entity Bayesian Networks). MEBN is one of a generation of
first-order probabilistic languages that were developed in the late
90's and early 00's. Basically, these languages can be viewed as
ways to specify a probability distribution over interpretations of a
first-order theory. We (I and students and colleagues too numerous to
mention but without which this work would never have happened)
started working on MEBN in the mid 90's while trying to build
probability models for DARPA, after realizing we needed greater
expressiveness than Bayesian networks could offer. For the theory,
you can read
http://ite.gmu.edu/~klaskey/papers/Laskey_MEBN_Logic.pdf,
which is currently under revision for AIJ. (Except that I spend too
darned much time writing to this forum and need to spend more cycles
finishing up the revisions!) (03)
A student at University of Brasilia is developing an open-source MEBN
reasoner, which saves in PR-OWL format and has a GUI that allows you
to enter graphical probability models. (04)
For the past two years, I have co-organized workshops at the
International Semantic Web Conference on uncertainty. See
http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS//Vol-218/ and
http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS//Vol-173/ (05)
Another workshop is planned this year in Korea, although I'm not
organizing it because I'm co-chairing a W3C Experimental Group on
Uncertainty Reasoning for the World Wide Web
http://www.w3.org/2005/Incubator/urw3/.
We are developing a set of use cases for uncertainty in the World
Wide Web. The ones we've done so far are pretty sketchy, but they
can be found on our wiki at
http://www.w3.org/2005/Incubator/urw3/wiki/FrontPage.
Only XG members can modify the wiki, but anyone can read them and
comment to the public email list. Anyone who works for a W3C member
can join; if you are interested and don't work for a member, you can
ask to join as an invited expert. We started in March and our
charter lasts for a year, so it wouldn't be too late to join. (06)
The wiki also includes a sketchy reference list; we know it's
incomplete, and would be glad to be told of things we've left off. (07)
>> (The state of usability of the tools is
>>another matter!) My colleagues, students and I have built several
>>toy probabilistic ontologies.
>
>I want to see them !! (08)
The Star Trek ontology from Paulo's dissertation can be downloaded
from http://www.pr-owl.org. You can open it in Protege. It imports
the PR-OWL upper ontology. It's explained in Paulo's dissertation,
which is also available at pr-owl.org. (09)
Several people are using PR-OWL to develop ontologies; I'm consulting
on some of the efforts. (010)
There is a group at University of Maryland that has developed a
language called Bayes-OWL that enables you to specify a Bayesian
network in OWL. Bayes-OWL uses standard Bayesian networks; hence,
isn't sufficiently expressive for the full range of ontology
development needs. (011)
Related work is KEEPER, developed by IET, Inc. IET (where a number of
my major partners-in-crime currently or formerly reside) has a rich
probabilistic modeling language called Quiddity*Suite, that allows
you to put probabilities on the values that fill a slot in a frame,
including probabilities on reference slots. The probability
distribution for a slot can depend on the values of other slots in
the same frame, or on slots of related frames (e.g., the probability
that I'll get an A in a course can depend on the teaching ability of
the teacher of the course). This results in a very expressive
probabilistic language. You can make instances of frames, specify
values for some of their slots, and use Bayesian inference to obtain
probabilities of the slots whose values aren't specified. KEEPER
allows you to build an OWL ontology that can be translated into
Quiddity frames. Quiddity*Suite is being integrated with the Netica
Bayesian network package. See http://www.norsys.com. (012)
>> I've worked with colleagues to build
>>artifacts that were called "probability models" but had features that
>>one associates with ontologies, i.e, were built in expressive
>>probabilistic languages with have types and individuals, subtypes and
>>inheritance, attributes and relations. I wouldn't call any of these
>>"well-engineered probabilistic ontologies" for the reason that they
>>were built for clients to solve specific problems, and had to make
>>compromises because there were no good upper ontologies for some of
>>the things the system had to do, and there was no time to build them,
>>so hacks were devised that worked for the purpose of the specific
>>problem but would give a principled ontologist a queasy feeling.
>
>Compared to what is out there, Im sure they will be models of
>clarity. Seriously, is it possible to see what kind of thing you
>have been doing along these lines? Offline and informally, of
>course, if you prefer. I am SERIOUSLY interested in understanding
>this. (013)
There are lots of papers on my web site that give an idea of the kind
of thing I've been doing. My web site is horribly disorganized --
people keep telling me I should make some kind of organizational
scheme instead of just a long list of papers -- but that would take
time I'd rather spend writing emails to Ontolog Forum. :-)
* http://ite.gmu.edu/~klaskey/papers/Wright_Laskey_Credibility.pdf
describes what could be called an oversimplified start at an ontology
for source credibility. It's based on Dave Schum's extensive
research on modeling credibility of sources. My former student Ed
Wright developed these ideas further into a demo that shows how you
can fuse evidence from multiple sources, incorporating source
credibility, to draw interesting conclusions about naval scenarios.
ONR declined to fund a proposal for follow-on work that included,
among other things, turning this into a real ontology.
* http://ite.gmu.edu/~klaskey/papers/BRIMS04_InsiderThreat.pdf
describes a project devoted to detecting insider threats in
information systems -- specifically people in highly secure
environments who are looking at documents they are cleared to see but
aren't related to what they are "supposed" to be doing. (I know: you
wouldn't want somebody looking over your shoulder like that, but it's
part of the deal you sign up for when you work in a highly secure
environment.) We built an organization and task ontology
(non-probabilistic), a system usage ontology (non-probabilistic) and
an insider behavior ontology (probabilistic). These mediated the
interaction between a system that captured user document accesses, a
data mining system (one student's doctoral dissertation) that
categorized the relevance of documents to topics, and the
probabilistic reasoner -- Quiddity*Suite -- that assessed the
likelihood that a user was a threat (another student's doctoral
thesis). ARDA pulled our funding two months before we were going to
conduct an experiment in which we would have given a bunch of
students a research task, given half of them a surreptitious research
task, required both groups to write reports on their "real" task and
also required the second group to write a clandestine report on their
surreptitious task, and performed a blind test to assess our ability
to catch the students doing the clandestine task. ARDA had major
funding cuts that year, and cut everything that they didn't see as
just about ready for deployment. We were far too researchy.
* http://ite.gmu.edu/~klaskey/papers/LaskeyLevitt_4708-30.pdf
describes a probability model (wouldn't call it an ontology, but it
could be extended to one) that models a bioterrorist anthrax attack.
We did this one for fun. It was unfunded.
* http://ite.gmu.edu/~klaskey/papers/Levitt_Laskey_Inf_JP.pdf is a
pre-publication version of a paper later published in the Cardozo Law
Review. It describes a probability model of murders, again that
could be extended into a bona fide ontology, applied to a murder case
that actually occurred in France. (Now you see what I meant above
when I said partners-in-crime -- my co-author is the CEO of IET.) To
be truthful, the model was engineered to be applied to this
particular murder case -- that's why I would get queasy calling it an
ontology. But we worked hard at abstracting it into a generic model.
This paper was also done for fun. (Ask my family about the nights I
was up during the wee hours during our beach vacation.) (014)
This is what I been doing with a large chunk of my time for the past
n years -- engineering probability models to specific problems, but
trying to develop them so they will be extendable, and also trying to
develop the necessary theory to do this right. In my experience,
funding agencies balk at the labor-intensive task of building out
real ontologies, especially in today's climate where every dollar of
government funding is going to things that can be deployed tomorrow.
I agree that the job is tedious and labor intensive -- but that means
we need to fund the basic research that's necessary to make it less
tedious! This seems to be a very hard sell. (015)
>>I expect this situation to dramatically change over the next decade.
>
>Oh come on, in a decade I might be dead. (016)
I hope not! The world would be a lot less fun without your crochety
emails to look forward to! :-) (017)
>I want to know about it NOW :-) (018)
How's this for starters? (019)
Kathy (020)
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