Dear Kingsley,

Thanks again for your input.

I understand the need for scalability here in the LOD context. And you are
making a great job for DBpedia and the LOD Cloud.

But what about this requirement?

I use a lot Virtuoso and now have to deal with an ontology in OWL DL, with
35 K subject instances. I want to be able to use a DL reasoner, like
Pellet. My workflow does not need sync (no live inference) at all. So I can
launch the inference as a batch from time to time. Furthermore, updates on
the dataset + onto are done every 2 months. How can Virtuoso helps here?
One could have expected to have in virtuoso.ini a way to call Pellet 2.3.1
(jars, classes, etc.) and then specify the named graphs (input and the one
to store the inferred result).

Could this feasible? And how?

 Best,
 Ghislain


2015-08-17 11:09 GMT+02:00 Kingsley Idehen <kide...@openlinksw.com>:

> On 8/17/15 9:30 AM, Ghislain ATEMEZING wrote:
>
>> Hi Hugh,
>>
>> Thanks a lot for the pointer. I'll have a look and let you know.
>> I am still really curious to see how it's feasible to integrate any
>> external OWL reasoner with Virtuoso :)
>>
>
> It hasn't been done due to lack of APIs that make very low-level DBMS
> integration possible. We have to have an option for reasoning and inference
> that scales. For instance, all the reasoning and inference functionality
> supported natively by Virtuoso scales from single-server instances (DBpedia
> static and live) all the way up to the 59 Billion+ live LOD Cloud cache we
> maintain.
>
> Delegating inference and reasoning to a 3rd part module or service comes
> with serious performance costs, at all levels of integration.
>
> Share some reasoner APIs and we can comment further about the specific
> interface, if need be.
>
> --
> Regards,
>
> Kingsley Idehen
> Founder & CEO
> OpenLink Software
> Company Web: http://www.openlinksw.com
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>
>
>
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