This is a debugging problem, but not a deployment problem. If one's
data is inconsistent one needs to fix it. Usually such
inconsistencies are either errors in the data that need to be fixed,
or indications that one needs to get clearer about what one wants to
say. In this case you need to make a choice about whether you want to
say something that we called in [1] the 'statement level' or the
'domain level'. If at the domain level you need to put your neck on
the line and say which experiment is right. If at the statement level
you need to remodel so that you are clearly communicating that you
are representing author statements.
-Alan
[1] section 2,3 of http://owl-workshop.man.ac.uk/acceptedLong/
submission_26.pdf
On Apr 17, 2007, at 9:53 PM, [EMAIL PROTECTED] wrote:
I think *if the ontology classifies reasonably at all*, then this
sort of query approach can achieve reasonable performance for this
rough application profile with a reasonable amount of engineering
effort in many cases.
Oh, but this is quite an important
We can expect that most of the ontologies that are based on 'real
data' are inconsistent, if not even highly inconsistent -- not
because of errors on the side of the ontology designers, but
because the represented information is contradictory. For example,
we have found some inconsistency in one of our SenseLab OWL
versions that was caused by the fact that the results of two
experiments that were entered into the knowledge base were
contradictory. Of course, this is a good example for the utility of
an OWL reasoner, because it pointed us to a (potentially
interesting or important) contradiction in the literature.
However, such contradictions could lead a reasoning-based approach
to querying fail, or at least they can make them less performant,
as you said.
cheers,
Matthias Samwald
.
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