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<div class="moz-cite-prefix">Hi,<br>
<br>
Sorry for the late reply, I was away from mail.<br>
<br>
I'm fine with assumptions such as unique name and closed world
because whatever method I use these handicaps will be there in
some form. In my case, why I can't go on with OWL/Logic is the
issue of large amounts of factual data which change frequently,
unlike, say, in Biomedical ontologies.<br>
<br>
For an instance, I want to model the semantics of a country's
appeal as a holiday destination for a particular crowd depending
on any events that makes it interesting for that crowd. These
things can change on day-to-day basis depending on weather,
currency rates, school holidays, ticket prices etc. OWL ontologies
(as you must know) handle these kind of volatile data quite
poorly. Unfortunately, its difficult to plug things to the
reasoner at the facts-end of the OWL. This last bit is different
for Drools.<br>
<br>
On the other hand, as I said, the number of possible inferences
are near infinite, and unnecessary unless they are explicitly
queried. For instance take a transitive relationship such as
situated_in(x, y). If there are few levels of locality (say,
country/district/city/town/suburb) there will be so many
unnecessary inferences for the relationship in drools rules. With
a backward chaining reasoner, these inferences won't fire unless
queried.<br>
<br>
According to my limited understanding of drools, this is the main
issue one could come up against using drools here. I was trying to
work around that fact by using the (apparently recent)
backward-chaining queries, and by delegating some of the worse
(transitive etc) clauses to POJO. I really enjoy being able to do
that, even if I'm doing it wrong and there's a better way ;-)<br>
<br>
So far, I'm using a mix of technologies to get what I think is the
best of both worlds: backward-chaining, POJO for logic itself at
lower level for recursive rules, semantic classes to "store"
inferences about stuff so they can be queried later.<br>
<br>
If there is a better way of doing this, I'd really love to know
it. I'm aware that this is not ideal problem for an expert system
or an ontology. However I need to model a little bit of semantics
than a database-only system, and do it in an easily changeable
way.<br>
<br>
Thanks,<br>
Upali<br>
<br>
<br>
<br>
On 12/04/2013 00:56, Davide Sottara wrote:<br>
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<blockquote cite="mid:51670E6C.2000006@gmail.com" type="cite">
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<div class="moz-cite-prefix">Ontology Reasoners and Rule Engines
are quite different, can be integrated under some assumptions, <br>
but are definitely not easily interchangeable.. (unique name
assumption, closed world assumption, etc..)<br>
Which one is better pretty much depends on the use case, which
also dictates the required level of <br>
expressiveness of the language - even Fact++, Hermit and Pellet
do not support full FOL...<br>
<br>
This said, your case seems very interesting.. would you mind
sharing a few more details or a concrete example?<br>
We would then be able to assess whether Drools is appropriate or
not, and which constructs could be<br>
used (e.g. traits, hybrid chaining queries, tms, ...) to tackle
the complexity.<br>
<br>
Or maybe we could take it as a use case to improve the engine
capbilities :)<br>
<br>
Best<br>
Davide<br>
<br>
<br>
<br>
<br>
On 04/11/2013 05:01 AM, Upali Kohomban wrote:<br>
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<div class="moz-cite-prefix">Thanks a lot again, you made my
life easy with the build instructions :)<br>
<br>
Yes I'm trying to do an integration. I have some experience
with ontologies and want to experiment with drools as an
alternative.<br>
<br>
OWL with reasoners like Fact ++ are pretty much good for the
job, except for the fact that they handle individual data bits
in quite a cumbersome way. It was because of this reason that
I wanted to experiment on other ways of modeling a knowledge
structure with better facilities for querying large amounts of
factual data. I'm giving drools a try, but I'm not entirely
sure if drools can do things as powerful as a reasoner.<br>
<br>
Basically, I'd like to be able to let the data lie (without
firing any "events" until necessary) and query the inferences
in a first-order logic like manner. This is very
space-efficient in data-heavy scenarios. I know that this
isn't the traditional way drools is designed to work. I'm
trying to solve the inference problem by dynamically adding
semantic classes to each object to store all the possible
inferences about it, so they can be queried later. Problem
with this approach is that it gets out of hand very quickly,
because the enumerated number of such inferences can be HUGE
even for a moderate KB. Backward chaining will eventually
solve this problem, but it seems that the constructs available
for that in drools queries are not too matured at the moment.
For instance they don't seem to support numerical inequality
operators (or I am wrong, which is also very likely).<br>
<br>
Thank you again for the quick help,<br>
Upali<br>
<br>
<br>
<br>
<br>
<br>
<br>
On 11/04/2013 14:14, Davide Sottara wrote:<br>
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<blockquote cite="mid:516677D1.9000009@gmail.com" type="cite">
<pre wrap="">You should be able to
git clone ...
the droolsjbpm repository, then
git checkout -t origin/5.5.x
this should switch to 5.5.1-SNAPSHOT (you can check the pom in the root
folder)
eventually, mvn clean install should do the trick.
See also the readme.md in github
Btw, it seems that you're working with rule/ontology integrations..
would you
be interested in sharing thoughts?
Davide
On </pre>
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