Gary,
I think you can partially look at existing database conceptual schemas (if they exist; often you just have physical schemas + data dictionaries) and conceptual
models (ala UML OO models) as vocabulary, which can then be mapped into ontologies. The formerly burgeoning “Community of Interest (COI)” movement at least in the federal government used that interpretation: not the XML folks, of course, for whom everything
is just XML and XSD structural models and there are no ontologies in general, but for ontologists in the COI effort.
With OO models typically you have less “distance” to go to map to ontologies, but you still have some – in particular since DB conceptual models have often
moved from ER/EER/etc. to UML models. Nearly always in DB and OO (conceptual) models you have data- and system-specific semantics (i.e., local semantics that combines real-world notions with local notions). Folks such as Giancarlo Guizzardi have tried to
explicitly bridge the conceptual model to ontology gap more formally.
Often in our own work, we try to intuit/analyze and then develop or “project” a “local ontology” from the given database (some folks would call this an “application
ontology”, e.g., Barry Smith and his folks; others would call this a “semantic wrapper” on the local data source), using semantic/ontological analysis over the existing resources: often just historical documents, data dictionaries, talking with DBAs, end users,
and developers (whose programming code provides much of the “semantics” of the database). Why this informal method? Because generally models are not kept except as drawings in desk drawers, an unfortunate fact of life for legacy databases and legacy systems.
It’s a craft, not an engineering discipline yet, but there are some engineering principles in play.
Then this “local ontology” is more easily mapped to the real ontology/ies, which typically spans multiples databases and thus multiple “local ontologies”.
Thanks,
Leo
From: ontology-summit-bounces@xxxxxxxxxxxxxxxx [mailto:ontology-summit-bounces@xxxxxxxxxxxxxxxx]
On Behalf Of Gary Berg-Cross
Sent: Saturday, February 08, 2014 3:38 PM
To: Ontology Summit 2014 discussion
Subject: Re: [ontology-summit] OWl and Knowledge reuse via import and modularization
>The given example is very simple, and for real application, would need (points)1-4 (in Leo's email) to
help assure us that >the equivalences are accurate.
I would further note that the illustrations of ontology integration you provided were responsive to my original focus frame of Owl Imports - so at the language
level. But as we consider what can be done with the language we quickly get into discussion of whether this is appropriate for the underly content's concepts and their relations being represented. While we can point to documentation in the ontologies themselves
we might be dealing with 2 or more ontologies representing concepts in data and thus look to the conceptual models for DBs holding that data.
The point here is that in practice we may be reusing the content of conceptual and logical models and their documentation as well as reusing ontologies and
documentation. This may be particularly true when Big Data meets Ontologies and we look at ways of them working together.
This is a point that john Sowa might support in part and he is scheduled to be a speaker at the 2nd Track A session on Content Reuse, so perhaps we will hear
relevant things in his talk.
SOCoP Executive Secretary
On Fri, Feb 7, 2014 at 4:03 PM, Obrst, Leo J. <lobrst@xxxxxxxxx> wrote:
Gary,
Yes, indeed. Really the ontologies require more documentation:
1) Definitions of what the classes/properties mean in standard natural language (English, etc.)
2) If available, more strict axiomizations: in OWL ontologies, if the axioms exceed OWL expressivity,
one can express these in textual annotations.
3) The use cases (pragmatics) for classes/properties/axioms, at least expressed in ontology-general
or more specific textual annotations.
Other:
4) Are instances available, and can we compare them against their specific (1-3) documentation?
The given example is very simple, and for real application, would need 1-4 to help assure us that
the equivalences are accurate.
Thanks,
Leo
Thank you for advancing the conversation. Concrete, illustrative examples can help focus the discussion. And you've shown how to do
it if the concepts are equivalent or thought so.
In your examples things are a bit simple and would be a bit more difficult, I think, with ontologies that were generated without considering
what had been done in a domain. So for example equating/equivalence between A and B ontologies seems directly possible, but if one of them was a bit more precise and detailed and said that PLACE hasA LOCATION then we might not equate PLACE in A with LOCATION
in B.
We'd know more about what is meant by the 2 concepts if we looked at the data instances they represent and found that in A we have place
locations like Grand Canyon and in B they are lat-lon.
SOCoP Executive Secretary
On Fri, Feb 7, 2014 at 3:05 PM, Obrst, Leo J. <lobrst@xxxxxxxxx> wrote:
Folks,
I quickly created 3 ontologies that illustrate an integration ontology pattern:
Integration-a-b.owl imports both ontology-a.owl and ontology-b.owl. Then in integration-a-b.owl various
equivalences are made.
Integration-a-b-ontology-imports.png shows the imports.
Ontology-a-classes.png shows the classes of ontology-a.owl.
Ontology-b-classes.png shows the classes of ontology-b.owl.
Classes-equivalences-integration-a-b-ontology.png shows the classes and equivalences of integration-a-b.owl.
The equivalences are shown as darker ovals in the last png file above.
I hope this is a helpful illustration of one very typical pattern.
Thanks,
Leo
Gary, I emailed on this topic (importing everything versus modularity) a few weeks ago on
another Summit thread (and Leo expanded on it). Also, I just posted about it on my blog. Here is the text:
[My previous post talked ...] about creating small, focused "modules" of cohesive semantic content.
And, since these modules have to be small, they can't (and shouldn't) completely define everything that might be referenced. Some concepts will be under-specified.
So, how we tie the modules together in an application?
In a recent project, I used the equivalentClass OWL semantic to do this. For example, in a Person ontology, I defined the Person concept with its relevant properties.
When it came to the Person's Location - that was just an under-specified (i.e., empty) Location class. I then found a Location ontology, developed by another group, and opted to use that. Lastly, I defined an "integrating" ontology that imported the Person
and Location ontologies, and specified an equivalence between the relevant concepts. So, PersonNamespace:Location was defined as an equivalentClass to LocationNamespace:Location. Obviously, the application covered up all this for the users, and my triple
store (with reasoner) handled the rest.
This approach left me with a lot of flexibility for reuse and ontology evolution, and didn't force imports except in my "integrating" ontology. And, a different application could bring in its own definition of Location and create its own "integrating" ontology.
But, what happens if you can't find a Location ontology that does everything that you need? You can still integrate/reuse other work, perhaps defined in your integrating ontology as subclasses of the (under-specified) PersonNamespace:Location concept.
This approach also works well when developing and reusing ontologies across groups. Different groups may use different names for the same semantic, may need to expand on some concept, or want
to incorporate different semantics. If you have a monolithic ontology, these differences will be impossible to overcome. But, if you can say things like "my concept X is equivalent to your concept Y" or "my concept X is a kind of your Y with some additional
restrictions" - that is very valuable. Now you get reuse instead of redefinition.
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