[comp.ai] KEE vs other knowledge rep languages

baker@garfield (Michelle Baker) (01/24/89)

We are currently deciding on which knowledge representation language
to use for a fairly large research project.   Currently KEE seems to
be the candidate of choice but we are interested in comparing this to
some of the others, e.g. ART, NIKL, HYPERCLASS, etc.  

I am interested in any comments people might have on any of these
languages.  In addition I am trying to collect examples of the kinds
of things which are difficult to represent in each of the languages.
For example, NIKL has trouble representing attributes with multiple
values.

Thanks in advance.
Michelle Baker  BAKER@CS.COLUMBIA.EDU
Columbia University
New York, NY  10027

weltyc@cs.rpi.edu (Christopher A. Welty) (01/25/89)

In article <6140@columbia.edu> baker@garfield.UUCP (Michelle Baker) writes:
>
>We are currently deciding on which knowledge representation language
>to use for a fairly large research project.   Currently KEE seems to
>be the candidate of choice but we are interested in comparing this to
>some of the others, e.g. ART, NIKL, HYPERCLASS, etc.  

We use CGIs Knowledge Craft at RPI, and find it much more expressive
an environment than KEE or ART.  It's not as `flashy' as some of the
other commercial knowledge tools, but it is quite powerful.  The
underlying representation language is CRL which was SRL, and it
subsumes the frame-based capabilities of ART and KEE, neither of which
I would classify as `representation languages'.

NIKL, which is based on KL-ONE, is also very expressive, but I've
never seen any applications (which by no means implies there aren't
any) that couldn't be done in KEE or ART (by this I mean couldn't be
done using the natural facilities of these frame systems).

The real key in determining which is the best for you is what
exactly you are doing.  KEE and ART are (in my humble view) far better
`production environments', good for making manager-pleasing software
with bells and whistles, but perhaps not as interesting to the
KR researcher as NIKL or Knowledgecraft.

I have lots of references on CRL, KL-ONE, and implementations using
them if anyone is interested.


Christopher Welty  ---  Asst. Director, RPI CS Labs
weltyc@cs.rpi.edu             ...!njin!nyser!weltyc