On 3/10/2011 2:34 PM, Ali Hashemi wrote:
> I take umbrage (ok slight exaggeration) with the claim that it
> doesn't make sense to separate the role of the knowledge engineer
> and the subject matter/domain expert, or that the latter is always
> better qualified to create a computational formal ontology. (01)
As I said to Rich, I would qualify the statement about the definition
of 'expert'. A SME who can't read the kn. rep. and a KE who has no
knowledge of the subject won't make a good pair. (02)
But a KE who has an apprentice level of expertise can collaborate
effectively with a SME who has a reading-level knowledge of the
notation. (03)
> I think clarifying and designating someone as the knowledge champion
> (with deep knowledge of the tools and technologies available) is not
> only beneficial but required. (04)
I agree. See Slide 62 from my tutorial on CNLs (copy below). (05)
> I would expect that a well trained, competent Knowledge Engineer
> would be able to develop and implement the framework where each
> fragment of the domain comes together to form a coherent whole
> (or views / perspectives that are appropriate for varying contexts,
> or variations thereof). (06)
I agree. But that KE would have to achieve an apprentice level
of expertise about every aspect of the subject. (07)
> It makes a lot of sense to me to have a Knowledge Engineer
> (or whatever you want to call it) role separate from the SME. (08)
Yes. That's the point of Slide 62 below. (09)
> I occasionally like to think that ontologists specialize
> in generalizing... (010)
Yes. An ontologist cannot design a mid-level ontology about
a subject without achieving at least an apprentice level of
expertise about it. But to verify that design, the SME
would need a reading-level knowledge of notation. (011)
> Similarly, my experiences with controlled languages is that for
> complicated formulae or axioms, they quickly become verbose and cumbersome. (012)
The kn. rep. for any subject should use all the abbreviations and
symbols that the SMEs are familiar with. Please note Naproche
(NAtural-language PROof CHEcker), which can use any symbol or
expression that can be written in LaTeX notation: (013)
http://naproche.net/ (014)
Naproche can read mathematical English at the level of a textbook.
Mathematicians normally embed mathematical variables, symbols,
and expressions into English syntax -- and Naproche allows that. (015)
> There is a large untapped, but potentially more useful (and
> at worst complementary) avenue of capturing SME knowledge via
> examples (i.e. Tarski models rendered in SME readable form). (016)
The most critical part of the ontology is deciding on an
appropriate choice of objects, functions, and relations. (017)
The language required to describe a Tarski-style model would
use exactly the same vocabulary for those items as the language
for describing the logical combinations. (018)
The only difference is in the use of Boolean operators and
quantifiers. The issue we're debating is whether we should
replace 'and', 'or', 'not', 'if', 'every' and 'some' with
special symbols. To answer that, please look at Naproche. (019)
John
______________________________________________________________ (020)
Source: Slide 62 of http://www.jfsowa.com/talks/cnl4ss.pdf (021)
CNLs as a Bridge (022)
Natural languages evolved to express and support human ways
of thinking. (023)
Computer languages enable IT professionals to think about the
data and operations inside the computer system. (024)
Forcing subject matter experts (SMEs) to think about their own
subject in computer terms is counterproductive: (025)
● Some of them become bad IT professionals. (026)
● Some become good IT professionals, but compromise and distort
their intuitions about their own subject. (027)
● Very few become equally good at both. (028)
CNLs can form a bridge between the NL of the subject and
the computational requirements for precision. (029)
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