Re: [ontolog-forum] [SPAM] Re: Ontology-based database integration

[Pavithra <pavithra_kenjige@xxxxxxxxx>]

Cecil,

 

In most cases,  the application that collects the data has the work flow logic ( needs lab results before something .. etc )  built in!   Application architects decides the  logic of the work flow in an application

vs datastores vs real time data feed data  verifications to support performance and the user and business requirements !

 

Incorporating Workflow logic in an application is a mature  technique!  Collecting & Verifying information from web and other data sources ( rss feed)  in real time for verification is what Kingsley is explaining ! 

 

Pavithra

--- On Fri, 10/9/09, Cecil O. Lynch, MD, MS <clynch@xxxxxxxxxxxxxx> wrote:

From: Cecil O. Lynch, MD, MS <clynch@xxxxxxxxxxxxxx>
Subject: Re: [ontolog-forum] [SPAM] Re: Ontology-based database integration
To: "[ontolog-forum] " <ontolog-forum@xxxxxxxxxxxxxxxx>
Date: Friday, October 9, 2009, 7:59 PM

HI Kingsley,

 

I don't think you are getting the message here. There is no "RDBMS" in play. These messages are not dumped to a database then processed. They are processed in real-time, in memory, off the data stream. If you had time to database them, then you are not in need of real-time analysis. That is not the use case I am talking about here, or in most decision support that we do in healthcare.

 

You need to process that data as soon as the submit button is hit from an application and the message is transmitted to the router. Processing that data means comparing the chief complaint, signs and symptoms, clinical observations, radiology tests and lab data of that patient against hundreds of other diseases that might mimic it, maintain in memory facts that may be of importance, orchestrate the timing relations of the data coming in so that you know a lab was drawn before a medication that might affect the lab result and not after, and modify your confidence as data aggregates on the fly, reporting your new facts to listeners on the service bus that may need those facts to correlate new data that it (the listener) has.

 

The issue is that DL inference cannot scale to this nor does its monotonicity allow this type of reasoning.

 

Also, you are talking about SPARQL endpoint that would be too limited to handle the OWL 2 constructs we use anyway, even if we could get the performance. In that case we would likely use SWRL.

 

Cecil

 

Cecil O. Lynch, MD, MS

 

 

-------- Original Message --------
Subject: Re: [ontolog-forum] [SPAM] Re: Ontology-based database
integration
From: Kingsley Idehen <kidehen@xxxxxxxxxxxxxx>
Date: Fri, October 09, 2009 3:16 pm
To: "[ontolog-forum]" <ontolog-forum@xxxxxxxxxxxxxxxx>

Cecil O. Lynch, MD, MS wrote:
> Hi Kingsley,
>
> I am talking about reasoning. The data sets are HL7 messages passed on
> disease surveillance to the US CDC. The system that we built processes
> an XML message, reasons against the syntax of the message to confirm
> the structure, reasons over the data contained in the message,
> to validate the terminology, then reasons against the content to
> determine the disease status (drug resistance pattern, treatment
> guideline adherence).
Okay, lets take a look at Web Scale Reasoning.

Scenario: Information about Michael Jackson
Data Sources: various data spaces on the Web e.g., Last.FM, Discogs.org,
MusicBrainz, DBpedia, blog posts from Live Journal etc..
Virtuoso instance information: 7.5+ Billion Triples, SPARQL endpoint
live on the Web at: http://lod.openlinksw.com

Actions:
All of this data gets into the Virtuoso Quad store via a variety of
means (remember, Virtuoso is an RDMS and Quad Store Hybrid)
Inference Rules in play: explicit co-reference via "owl:sameAs" and much
fuzzier indirect co-reference using a local axiom that designates
"foaf:name" as being an Inverse Functional Property (IFP).

Links:

1. http://bit.ly/38Jlw4 - Page showing the effect of explicit and
fuzzier reasoning (note the obvious stick out errors re. entity Michael
Jackson); here de-referencing each URI will expose the same expanded
union of data from across all the data spaces (named graphs)
2. http://bit.ly/LKtnt - Page showing same data but without the
inference rules context in play; here each URI will de-reference to its
data space specific data (no union expansion)
> All 50 states send messages to the system. When you start getting
> into the complex reasoning for these types of medical messages for an
> entire country, OWL does not scale.
Really? It doesn't scale if forward-chaining is in play and
unconstrained. I am demonstrating Web Scale reasoning using
backward-chaining. Naturally, since some of the data gets into the Quad
Store via our Sponger Middleware (an RDFizer middleware component),
there is some constrained forward-chaining as part of the sponger
processing pipeline.

Conclusion:

We shouldn't write-off anything, its about using the best combination of
tools for the problem at hand. In this case, the trick is to combine
technology and techniques from a range of realms: raw DBMS and Middleware.


Kingsley
>
> Cecil O. Lynch, MD, MS
>
>
>
> -------- Original Message --------
> Subject: [SPAM] Re: [ontolog-forum] Ontology-based database
> integration
> From: Kingsley Idehen <kidehen@xxxxxxxxxxxxxx>
> Date: Fri, October 09, 2009 10:41 am
> To: "[ontolog-forum]" <ontolog-forum@xxxxxxxxxxxxxxxx>
>
> Cecil Lynch wrote:
> > John,
> >
> > I would say that I completely agree with you that for large volume
> > transaction based systems, OWL or RDF are hopeless from a performance
> > perspective.
> Cecil,
>
> What have you tested? How have you arrived at your conclusions?
>
> You are assuming that RDBMS and RDF Graph hybridization (all the way
> down to the engine core) isn't achievable, right?
>
> What performance are we talking about? Instance data queries,
> reasoning
> over the ABox and TBox etc.?
>
> I really think that when we talk about data integration and the
> prowess
> of RDF, OWL, and what can be achieved re. reasoning etc.. Best we
> point
> to actual data available on the Web.
>
> Links:
>
> 1. http://www4.wiwiss.fu-berlin.de/bizer/BerlinSPARQLBenchmark/ --
> SPARQL Benchmark with RDBMS to RDF component
>
> Kingsley
> > That said, I think that OWL 2 is a great language for basing
> > your model to ensure formalism in the development of the domain
> of interest,
> > but then you need to hand it off to another reasoning approach.
> >
> > There is no perfect ONE language, including Prolog, to approach
> most real
> > world, real time reasoning. In our experience, the best
> performance and
> > logic come from assembling a suite of tools that can work
> together in an
> > orchestrated services platform. There are some problems I want to
> address
> > using DL or regression analysis, others with classification and
> still others
> > with backward chaining. Some tools are better for each of these
> and I think
> > the best systems use them in concert.
> >
> > Cecil
> >
> > -----Original Message-----
> > From: ontolog-forum-bounces@xxxxxxxxxxxxxxxx
> > [mailto:ontolog-forum-bounces@ontolog.cim3.net
> <http://email.secureserver.net/#Compose>] On Behalf Of John F. Sowa
> > Sent: Friday, October 09, 2009 12:27 AM
> > To: edbark@xxxxxxxx
> > Cc: [ontolog-forum]
> > Subject: Re: [ontolog-forum] Ontology-based database integration
> >
> > Ed,
> >
> > I agree with most of your response to my remark, with the exception
> > of one sentence.
> >
> > JFS>> DL is just one of a large number of logic-based technologies
> > >> that produce useful results for certain kinds of problems.
> > >> Unfortunately, people are being forced to use OWL for tasks
> > >> that it was never designed to do. They go through contortions
> > >> that make Perl look like the epitome of structured elegance.
> >
> >
> > EB> I fully agree. Part of that is the silver bullet mentality:
> > > OWL is the best technology available; so whatever contortion you
> > > have to perform to use it is the best you could have done. And
> > > we are both familiar with the software engineer's pride of
> > > accomplishment in building a Rube Goldberg device to solve a
> > > problem that would be a simple application of a technology he
> > > is unfamiliar with. But we have made progress -- it is not
> > > a primitive AI application coded in Fortran anymore.
> >
> > The point that I very strongly disagree with is that "OWL is
> > the best technology available." At VivoMind, we are delighted
> > when our competitors use OWL because we can translate their
> > sources to Prolog and get orders of magnitude improvement over
> > their "native" implementations.
> >
> > But for heavy-duty lifting (gigabytes and terabytes) we would
> > never dream of using RDF or OWL. Those languages are hopelessly
> > inadequate for truly massive volumes of data. Just note that
> > Google doesn't use those languages. They know better.
> >
> > John
> >
> >
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> --
>
>
> Regards,
>
> Kingsley Idehen Weblog: http://www.openlinksw.com/blog/~kidehen
> <http://www.openlinksw.com/blog/%7Ekidehen>
> President & CEO
> OpenLink Software Web: http://www.openlinksw.com
> <http://www.openlinksw.com/>
>
>
>
>
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--


Regards,

Kingsley Idehen Weblog: http://www.openlinksw.com/blog/~kidehen
President & CEO
OpenLink Software Web: http://www.openlinksw.com





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