Re: Evidence for backing statements

On May 18, 2007, at 12:55 PM, Dan Brickley wrote:

>
> Matt Williams wrote:
>> I've been lurking & reading the discussion with interest.
>> It might be worth pointing out that there is an ongoing attempt to  
>> classify/ represent evidential links/ weight/ etc. started in the  
>> legal domain by people such as Wigmore and continued by people  
>> such as David Schum & William Twining. There's currently a  
>> Leverhulme-sponsored research programme on "Evidence Science",  
>> centered at UCL, London.
>> Such efforts don't seem to easily map to rdf (they're often based  
>> on Bayesian models), but might provide some inspiration, although  
>> some of the legal niceties may be unnecessary.
>
> Interesting! There's also another related group here in W3Cland -  
> an incubator on Uncertainty Reasoning for the Web, see
> http://www.w3.org/2005/Incubator/urw3/ ...though things are only  
> just starting up. The only mailing list traffic so far is about  
> scheduling the first telecon.
> The charter has some notes on what they're doing:
> http://www.w3.org/2005/Incubator/urw3/charter ...and Bayes gets a  
> few mentions there.
>
> My inclination with RDF and evidence/probability, ... is that  
> without reinventing the RDF graph model, it is likely easier to  
> attach probability and other annotations to collections of  
> statements, rather than to individual triples. This can be done for  
> example by making assertions about an RDF/XML document, ... and is  
> somehow related to the ability in SPARQL to associate a graph with  
> a URI. For example see http://www.w3.org/TR/rdf-sparql-query/ 
> #queryDataset

the idea of attaching probabilities to triples or sets of triples  
bothers me. It seems like a problematic mix of monotonic logic and  
probabilities.

BFO points to a cleaner solution through the use of bfo:Disposition.  
We can make statements such as

	HumanWithBRCA1-allele123 has_disposition some (bfo:Disposition that  
towards DevelopingBreastCancer)

Which are to the best of our knowledge true in a boolean non-fuzzy- 
probabilistic sense. At the same time the bayesian folks have a class  
on which to hang their probabilities off of. Subclasses can be  
introduced for specific environments and contributing factors.  
Bayesian networks can be built on top of the (logically) reasoned  
graphs. No need to worry about the probabilistic stuff feeding back  
to the logical model in a non-monotonic way.

The evidence/provenance would be attached to the whole (class-level)  
statement above

Of course Matt's NM approach may be useful too.. interested in  
hearing more.


> cheers,
>
> Dan
>
>
>
>
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Received on Friday, 18 May 2007 20:59:37 UTC