I'll have a go at articulating a position here.
A DL tableau reasoner is designed so that with moderate efficiency certain inference problems can be solved for ontologies that meet certain constraints. Some of these constraints are expressed by the syntactic constraints of OWL DL. Others are unarticulated, and in general, unarticulatable. While the tableau reasoner is guaranteed to terminate, this is a practically useless piece of information, since the complexity is so bad that even moderately small problems - that are not of the type that were tested during the development of the methodology - can easily explode so that the guaranteed termination comes good after the heat death of the universe. It is easy to find such problems in geometry, which is a hobby of mine. The practical question is: what is the general shape of your ontology and what sort of things do you want to find out, and is there a way of computing them? To the extent that an off-the-shelf reasoner solves your problems, great. If not, having the ability to add custom rules is a good thing. Also in practice - any ontology has some primitives that have additional constraints over and above those that can be articulated in OWL DL. Having the ability to add custom rules to express such constraints, is basically a good thing. Jeremy > -----Original Message----- > From: [email protected] [mailto:topbraid- > [email protected]] On Behalf Of Scott Henninger > Sent: Monday, March 23, 2009 9:02 AM > To: TopBraid Composer Users > Subject: [tbc-users] Re: Looking for an overview of inference engines > > > Ninus; In terms of OWL reasoners, Nick's general synopsis is > accurate. In addition, if you need explanations for things like > inconsistencies, then Pellet is what is needed. For more complete > information, you should consult the reasoner vendors directly. > > SPIN and SPARQLMotion have been designed to use SPARQL as a flexible > tool for inferencing. There is an overview of SPIN at > http://composing-the-semantic-web.blogspot.com/2009/01/object- > oriented-semantic-web-with-spin.html > > ...and the beta 2 of Composer 3.0 has full SPIN and SPARQLMotion > support. > > -- Scott > > On Mar 23, 10:42 am, Nick Khamis <[email protected]> wrote: > > At a very general level: > > > > Pellet: An OWL-DL Reasoner - Uses Tableau Algorithms to Deduce a KB > (Very > > Restrictive) > > SwiftOWLIM: Not an OWL-DL Reasoner - Cannot deduce things such as > > disjointness. > > > > I cannot think of any paper off the top of my head, however; there > are many > > out there. > > > > Regards, > > Ninus. > > > > On Mon, Mar 23, 2009 at 8:13 AM, Aidan <[email protected]> wrote: > > > > > Hello, > > > > > I am new to knowledge representation and the use of TBC, so please > > > excuse me if I am not that familiar with this. > > > > > Starting to get familiar with the use of TBC and the inference > > > machines TBC is supporting, I still don´t seem to understand when > to > > > use Pellet, SwiftOWLIM or Jena built-in Reasoner(or even > > > combinations). So I have been looking for some kind of overview to > > > this but I didn´t find what I was looking for. Is there some > reference > > > you could give me that could help? Some sort of overview what the > > > different inference engines are capable of (and what not would be > even > > > better) would be very welcome. > > > > > Regards, > > > > > Christopher > --~--~---------~--~----~------------~-------~--~----~ You received this message because you are subscribed to the Google Groups "TopBraid Composer Users" group. To post to this group, send email to [email protected] To unsubscribe from this group, send email to [email protected] For more options, visit this group at http://groups.google.com/group/topbraid-composer-users?hl=en -~----------~----~----~----~------~----~------~--~---
