|To:||Ontology Summit 2011 discussion <ontology-summit@xxxxxxxxxxxxxxxx>|
|From:||Antoinette Arsic <aarsic@xxxxxxxxxxx>|
|Date:||Sun, 27 Feb 2011 22:08:57 +0000 (UTC)|
I'm very interested in that paper, John Sowa, as always. I have searched and cannot find in context what LOD stands for. I'd like to learn more about that. Thanks!
Have a great day!
----- Original Message -----
From: "Jans Aasman" <ja@xxxxxxxxx>
To: "Ontology Summit 2011 discussion" <ontology-summit@xxxxxxxxxxxxxxxx>
Sent: Saturday, February 26, 2011 11:36:29 AM
Subject: Re: [ontology-summit] [Making the Case] Barriers to adoption of ontologies
Thanks John: that is a great paper. I might want to add that often in
my talks I get attacked about why I promote Prolog on top of a triple
store so much. The common idea seems to be that a combination of OWL,
SPARQL and some rules seems to be enough to solve all the problems in
the world. However, I find, that if you deal with a task that requires
some temporal relationships, some geospatial reasoning, a lot of
quantitative reasoning, some process and procedural knowledge, and
maybe a tiny bit of uncertainty than suddenly using OWL + SPARQL becomes
a very advanced Martin Garner problem that only 1 % of our community can
solve. In many cases the same task can be solved much more
straightforward with Prolog as a rule and Prolog as a query language.
Anyway: if people in this Forum are interested I can do a talk+demo in
the near future. Jans
On 2/26/2011 5:43 PM, John F. Sowa wrote:
> I'd like to return to Joanne's point about flexibility, but
> not with the approach discussed by Toby Segaran.
> Following is a note I sent to Ontolog Forum, in which I discussed
> another publication by the folks that brought us Watson.
> Watson and systems like it are far more flexible than the currently
> popular ontology tools, and there are enough publications to show
> how they have found a better way.
> There is nothing wrong with having some success stories about OWL
> and related tools. But the unstructured methods used with LOD
> are the real growth path for ontology. Watson is just one example,
> but there are others that show great promise for the future.
> -------- Original Message --------
> Subject: Re: [ontolog-forum] Watson, Medicine, and New Knowledge
> Date: Sat, 26 Feb 2011 10:13:09 -0500
> From: John F. Sowa<sowa@xxxxxxxxxxx>
> To: ontolog-forum@xxxxxxxxxxxxxxxx
> Adrian, Ferenc, Ron, and Jack,
> Before commenting on your notes, I'd like to mention that I came
> across a paper co-authored by David Ferrucci in 2008, which was
> shortly after they began the Watson project:
> The Prolog Interface to the Unstructured Information
> Management Architecture (UIMA)
> Following is the concluding paragraph of that paper:
>> The UIMA generic Prolog annotator allowed us to develop faster and easier pattern
>> matching rules for natural language analysis in a language familiar to our developers
>> and users (i.e., the Prolog language), the Prolog engine being transparent to the UIMA
>> pipeline (i.e., completely integrated in the pipeline), while having access to state-ofthe-
>> art semantics and proving effective on question analysis (i.e., time and results).
>> We implemented interfaces for various rule systems: the UIMA-Sicstus Prolog
>> interface (using the PrologBeans library) , the UIMA-SWI Prolog interface (using
>> the JPL library)  and the UIMA-InterProlog translator for used by XSB  and
>> Yap Prolog  systems (using the Interprolog library ). Our applications of this
>> annotator include: complex rules for question analysis, shallow semantic parsing, and
>> tools for development and testing UIMA analytics.
> The paper is only five pages long, but it gives a bit more detail about
> the kinds of things that Watson is doing. And I am very happy to see
> that they use Prolog, which is an outstanding language to use for this
> kind of application.
> In fact, Prolog is the primary language that we use at VivoMind, because
> it is highly flexible and can be quickly adapted to either informal
> processing (along the lines used by Watson) or precision analysis
> (as needed for formal logic). We also use C, but only for heavily
> used, well tested algorithms that can be frozen in low-level code.
>> Watson is of course a major achievement, as it demonstrated by
>> comfortably winning Jeopardy. It's now official that IBM sees
>> Watson has having potential in Medicine -- it could read the
>> biological-medical literature and outperform the Doctors.
>> However, for these purposes, there's a key difference between
>> Jeopardy and Medicine. In Jeopardy, humans know the right answers
>> -- the city of Toronto is not in the USA.
>> In Medicine, humans don't have consensus answers to new questions
>> (e.g. What is the best treatment for multiple sclerosis). So,
>> whatever algorithms Watson uses will lead to new medical knowledge
>> that humans cannot easily check by thought experiments.
> That may be true. But there is a huge amount of knowledge in the
> medical literature that a practicing physician can't possibly know.
> Even a research physician can only keep up with the literature in
> his or her own specialty. There is no single MD in the world who
> can know all or even most of the consensus answers.
> I would not expect Watson or any other computer system to produce
> a definitive answer to any medical question. But what it could do
> very well is produce several alternatives with its own confidence
> ratings for each *and* with pointers to the literature for the
> physician to verify. That would be immensely valuable.
>> Surely, if the knowledge base and learning algorithm of Watson is
>> based on a dynamic, but single algorithm as opposed to a data base
>> ... Call the undertaker
> First of all, Watson has a very wide range of different algorithms.
> But in any case, neither Watson nor any other computer system being
> designed today would ever replace a physician. Its primary role is
> to serve as a super search engine to find relevant knowledge that a
> physician might not be aware of. The final decision about treatment
> is always the responsibility of the physician. If anything goes
> wrong, the human MD is sued, not Watson or IBM. (I worked at IBM,
> and I know that IBM management is highly allergic to law suits.)
>> It appears that Watson can give you a lot of insight into how it
>> arrived at an answer including the various parallel processes that
>> were done. It probably can do a much better job of this than most
>> humans since we quickly forget the bad ideas and fruitless paths
>> whereas Watson remembers them all.
> Yes. Think of Watson as a super Google that keeps track of everything
> and evaluates the alternatives. But the human MD makes the decision.
>> ... just the task of thinking through *how* to organize resources
>> for Watson to deal with them is, itself, an important learning
> I agree.
>> It will be interesting to see how "ontologists" make the shift from
>> being "owltogists" to "Watson feeders".
> Watson is much more flexible than OWL. A knowledge engineer working
> with OWL is forced to state every point very precisely in an exactly
> *decidable* way. But most of the knowledge in every field is vague,
> flexible, and rarely, if ever, *decidable*.
> There are very specialized domains (microtheories) for which OWL and
> other formal logics are valuable. But the overwhelming amount of
> knowledge in the world is *unstructured* -- the first letter of UIMA.
> I believe that the combination of Prolog with UIMA (or something
> like it) is much better suited to processing the vast resources
> of the Web than OWL.
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