RE: An argument for bridging information models and ontologies at the syntactic level

 

> Okay, for me, the difference is whether or not the underlying language
> is backed by a some knowledge reprsentation with a formal 
> semantics.  By
> knowledge representation I primarily mean a language that can 
> facilitate
> inference [1]. 

I agree with the above, though I would probably draw a distinction between:
(A) A language with a formal semantics
(B) A language that can facilitate inference.

(A) is the crucial issue. (B) is an added bonus. The reason for this is that
(A) helps even when you don't want to draw inferences, e.g., establishing a
knowledge
standard and consistent definitions across the enterprise.

Note in that reference (which serves as my preferred
> definition of what a KR is) it states: "A KR is not a data structure."

I agree.

> So, although data modeling systems such as RELAX NG and HL7 
> RIM might be
> used to refer to real entities, they do not facilitate any level of
> inference.

Yes. and they also model the data structure type of things. Further more
the semantics of various RIM constructs are not clear and are not well
specified.

> So, I think of what you call 'implementations or data models' as being
> synonymous with a concrete syntax.  

I would agree with the above as the syntax seeks to describe the structure and
organization
of the data as opposed to information.

> [[
> 	[VK] The definition of knowledge representation above is closer
> in my mind to the notion of an information model, where the 
> focus should
> be on describing information as opposed to describing structures
> 	for messaging, data manipulation, etc. 
> ]]
> 
> Does the distinction of inference change this for you?

No. In fact, inference is one of the "side-effects" of semantics, which is why I
accord
a higher level of priority to the specification of well defined semantics of a
language.

Cheers,

---Vipul

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Received on Thursday, 27 March 2008 10:36:24 UTC