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Re: [ontolog-forum] Ontology of Rough Sets

To: Rich Cooper <rich@xxxxxxxxxxxxxxxxxxxxxx>
Cc: "'[ontolog-forum] '" <ontolog-forum@xxxxxxxxxxxxxxxx>
From: Tara Athan <taraathan@xxxxxxxxx>
Date: Sun, 16 Jan 2011 02:46:09 -0800
Message-id: <4D32CC71.7080003@xxxxxxxxxx>
Well, if we are going to develop a formal ontology, there a number of methodologies out there to follow.
I'd be interested in trying out John Sowa's proposal from http://www.jfsowa.com/pubs/rolelog.pdf

But I think we should ask if it is appropriate to continue this discussion here. There have only been a few participants, and this may indicate that the topic is not of interest to the majority of list members. If that is the case, perhaps we should continue off-list.


Rich Cooper wrote:

Hi Tara,


YES, it is starting to look like an ontology.  Can you suggest some instances and a definition, the simpler the better?  


Or something to deepen the discussion.  I suggest inference over rough sets – not statistical inference, but how rough sets combine with crisp and rough alike.  


When two linear signals are added, with both independently noisy at the same noise energy,  adding them cancels 3db of energy from the noise in the sum by signal averaging.  So arrays of noisy sensors can be integrated at that level.  


But what happens to algebraic expressions mixing rough and crisp sets in calculations?  Does it result in biases that can be worked with? 

Let's talk about expectations. Is this the sort of question that you expect to be able to answer by querying an ONTOLOGY? If so, then I think you have overestimated the capabilities of the tools.

Is this the sort of question that you expect ME to answer?  If so, then we should talk about a consulting contract. ;)

On the other hand, if you would like to answer this question for yourself (or your clients), then I suggest you invest some time/money into setting up a Monte-Carlo simulation environment, because the answer is going to be highly dependent on the underlying probabilistic model, and how it interacts with the coarsening of the reference scale.


A frequentist would split uncertainty into systemic uncertainty and bias,

Correction: systemic uncertainty( = bias) and random uncertainty.  Well, I said I'm not a frequentist...
Tara Athan

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