On 02/07/2021 13:29, Simon Gray wrote:
Hmm… I am not sure how my rules are modeled. I just use the built-in
OWL_MEM_MICRO_RULE_INF OntModelSpec.
Anyway, my question is still this: how do I get all of those inferences
computed *before* I start querying the Model. It’s great if I can just store
them later, but I still need to *compute* them before I can think about
persisting anything. Running a single query doesn’t seem to compute them all,
just relevant ones to that specific query… I think?
Short answer is there's no built in way to precompute everything that's
precomputable for the OWL reasoners other than that which the others
have pointed out - copy the inferred model.
The OWL rules use a mix of forward and backward reasoning. The forward
reasoning can all be invoked in one go via prepare() but the backward
reasoning is mostly done on demand. Some of the backward rules are
tabled/memoized so once they've been run once future runs are supposed
to be quicker. Others are always run on demand.
If you have a few particular query patterns then to warm up the relevant
memoization run those queries.
The most comprehensive way to ensure everything has been computed is to
copy the model to a plain model (in memory or persistent). That copy is
essentially running the query (?s ?p ?o) and will compute everything the
rules can reach. After that the inference model is as warm as it's going
to get. But since that that point you've already materialized everything
then might as well keep the materialized copy as the others have said.
There'd be nothing to doing the general query (e.g. via an unbounded
listStatements()) call and throwing the results away. That *could* be
beneficial if the materialized model is too big but the
tabling/memoization is proving useful and smaller - but no guarantees.
Dave
Den 2. jul. 2021 kl. 14.06 skrev Lorenz Buehmann
<buehm...@informatik.uni-leipzig.de>:
But can't you do this inference just once and then somewhere store those
inferences? Next time you can simply load the inferred model instead of the raw
dataset. It is not specific to TDB, you can load dataset A, compute the
inferred model in a slow process once, materialize it as dataset B, and later
on always work on dataset B - this is standard forward chaining with writing
the data back to disk or database. Can you try this procedure, maybe it works
for you?
Indeed this wont work if your rules are currently modeled as backward chaining
rules as those are computed at query time always.
On 02.07.21 13:37, Simon Gray wrote:
Thank you Lorenz, although this seems to be a reply to my side comment about
TDB rather than the question I had, right?
The main issue right now is that I would like to use inferencing to get e.g.
inverse relations, but doing this is very slow the first time a query is run,
likely due to some preprocessing step that needs to run first. I would like to
run the preprocessing step in advance rather than running it implicitly.
Den 2. jul. 2021 kl. 13.30 skrev Lorenz Buehmann
<buehm...@informatik.uni-leipzig.de>:
you can just add the inferred model to the dataset, i.e. add all triple to your
TDB. Then you can disable the reasoner afterwards or just omit the rules that
you do not need anymore
On 02.07.21 13:13, Simon Gray wrote:
Hi there,
I’m using Apache Jena from Clojure to create new home for the Danish WordNet. I
use the Arachne Aristotle library + some additional Java interop code of my own.
I would like to use OWL inferencing to query e.g transitive or inverse
relations. This does seem to work fine although I’ve only tried using the
supplied in-memory model for now (and it looks like I will have to create my
own instance of a ModelMaker to integrate with TDB 1 or 2).
However, the first query always seems to run really, really slow. Is there any
way to precompute inferred relations so that I don’t have to wait? I’ve tried
calling `rebind` and `prepare`, but they don’t seem to do anything.
Kind regards,
Simon Gray
Research Officer
Centre for Language Technology, University of Copenhagen
