Sean Barker wrote: (01)
> I'm still trying to sort out when to tell people I'm doing data
> modelling and when ontologies, and the following is an interpretation of
> some previous postings on the subject. (02)
First rule: "ontology" is IN; "data model" is OUT. Therefore, whatever
you are doing in the modeling arena, you call it "ontology" if you want
to get FUNDING, and in some cases, Management Approval. (03)
This rule guarantees that any intelligent distinction will be ignored. (04)
Second rule (mine): The distinction between "ontological models" and
"data models" is "formal/axiomatic definition". (05)
In a data model, ALL classes are primitive. Their definitions are
always in "remarks" or "comments". All that is ever stated (and all
that can be stated in a data modeling language) is necessary and
accidental properties. In an ontology, SOME classes are primitive --
they are undefined symbols. All other classes are *defined*, by axioms
that state necessary and sufficient conditions. In OWL/DL, you can only
sometimes formulate class definitions, but you do what you can. In more
powerful languages, like CLIF and RDF and Ontolingua, you can write
axiomatic definitions for MOST classes. (06)
Moreover, in FOLs and near FOLs, you can define many properties
axiomatically as well. In DL languages, you can't really do better at
defining properties than you can in data modeling languages like UML/OCL
and EXPRESS; you can define "derived properties" as simple functions of
other properties. You can't introduce bound variables into the
definitions, and most of the languages even lack a LISP MAPCAR for
dealing with non-functional properties. (07)
It may appear that one can use "rules" in languages like OCL and EXPRESS
to formulate axioms. The problem is that the languages assign them the
semantics of (data set) "validation rules". They are tests of the
conformance of a set of assertions to the intended model. That doesn't
prevent data modelers from using "rules" to express the axioms they
intended; but it allows them to mix and match, and they do, which makes
it unsafe for the reader to see them as axioms. (08)
As Natasha Noy observed, most published OWL models are essentially data
models -- they contain no, or nearly no, class definitions. And IMO
that is a direct consequence of a generation of children (of all ages)
who thought XML Schema was a modeling language and, as they came to
learn something about data modeling, discovered OWL (because data
modeling languages like IDEF1-X and SDM and NIAM and EXPRESS were
"obsolete technologies"). And OWL/DL is a good data modeling language.
Quite honestly, I am only too happy to have their education caught up
to 1988. But this factor, coupled with the ignorance of buzzword-based
funding, has caused the distinction between "data model" and "ontology"
to be "academic". (09)
But then, this forum is about "academic" concerns... ;-) (010)
-Ed (011)
--
Edward J. Barkmeyer Email: edbark@xxxxxxxx
National Institute of Standards & Technology
Manufacturing Systems Integration Division
100 Bureau Drive, Stop 8263 Tel: +1 301-975-3528
Gaithersburg, MD 20899-8263 FAX: +1 301-975-4694 (012)
"The opinions expressed above do not reflect consensus of NIST,
and have not been reviewed by any Government authority." (013)
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