Hi Ed, (01)
Really excellent post and thank you tremendously for the early
references. I will be a sponge if you care to provide more
specifics. ;) (02)
Ed Barkmeyer wrote:
> Len Yabloko wrote:
>> As I said - OntoBase is designed to simplify data integration and database
>application development by taking advantage of ontology-based semantic
>modeling. Part of the method used in OntoBase is semi-automatic generation of
>ontology guided by the user, who *is* the main source of semantics. Database
>metadata is used to ensure consistency of the end result and , at the same
>time, to provide automatic translation of the application activity into
>database transactions. None of these goals was ever set or accomplished by the
>above mentioned Semantic Web initiatives.
> They were, however, the objective of several "semantic database
> integration" and "semantics-based federation of distributed databases"
> projects of the 1980s. As I remember, there was an entire issue of the
> ACM Journal devoted to such projects, long about 1988. Of course, the
> integrating ontology wasn't called an "ontology" in that time; it was
> called a "semantic data model", with such languages as SDM and OSAM*.
> (Sudha Ram of U. Arizona published a compendium of "semantic data
> modeling concepts" in 1990, with a bibliography of the tower of Babel of
> that time. I rather shamelessly plagiarized her concepts list for my
> presentation of "OWL as a Data Modeling Language".) NIST built similar
> tooling back then, and we later used a commercial tool called ORION that
> was a spinoff of some DARPA project (brainchild of Amit Sheth and some
> other famous names). (More lore from the ancient world before PDF, but
> not nearly so interesting as the Dead Sea Scrolls.)
> The major difference in using OWL as the language for the integrating
> model is that it is a standard language that is well-defined and comes
> with a variety of supporting tools for validating the ontology itself.
> In addition, it offers the possibility of extension to additional
> information bases by linking to additional ontologies, assuming that the
> "integrating ontology" is one, and is not just the OWL version of a
> purpose-built SDM model. (And it is easy to make tools with much nicer
> GUIs now.) Unfortunately, many/most databases are treated as having
> inherent "closed world" semantics, which may make it difficult to get a
> "semantic web language" to support some important semantics of the
> Ian's point is well-taken:
>>> I spent years mapping legacy data into "semantically richer" formats ...,
>and it always comes down
>>> to the data, not the data model. You can guarantee the data modeller got it
>>> wrong when they built the system, and the users have been working against
>>> rather than with the model for years. Unless you have a good sniff around in
>>> the data, you'll never get a real picture of the true semantics (i.e. what
>>> the data is actually referring to in the real world). ...
> The data modeler needn't have got it wrong, initially. The problem is
> that the databases are, for various reasons, rigid, and are not easily
> evolved when the business practices or business environments change.
> So, over time, the schema becomes gradually more and more out of sync
> with the reality of the business, and some bits no longer have any
> vestige of their original intent. (I am reminded of the executive who
> told me: "I know that is what the DB document says, but what my staff
> puts in that field is X, and we are the only ones who still use that
> field." Unfortunately, he was wrong about the last.)
> Len wrote:
>> The project I do for DoD calls this process Semantic Model Discovery.
>Actually, there are many models some times used in turns, and they all are
> Yes. That agrees with my experience. In a big enough organization,
> there is more than one (mis-)use of certain DB elements, and over time
> some will no longer be used and others will appear. But the schema will
> live on.
>> They main goal of data integration should be maintaining consistent mapping
>between different semantic models. The main challenge is to define what
> Uh, yeah. The statement of the task strikes me as an oxymoron. I would
> have thought that your OntoBase was about linking multiple data
> organizations and representations to a common semantics, rather than the
> other way around. Perhaps I misunderstand...
>> This where Chris Partridge's work first opened my eyes on true mature of
>semantics, as opposed to mathematical notion of 1st order logic some times
>confused with semantics (I know this statement will provoke discussion)
> I have seen a lot of things confused with "semantics", because the
> interpretation of "semantics" is usually "what this means for _my_ level
> of abstraction, purpose, and domain of interest", and it often makes the
> assumption that "my" is synonymous with "our common" or "everyone's".
> But anyone who confuses formal logic with semantics understands neither.
> Formal logic is a means of expression, in which the basic elements of
> the language have well-defined meanings and a set of well-defined
> manipulations retain those meanings, and nothing more. As to whether
> the snark actually is a boojum, classical formal logic offers only two
> possible interpretations: it is either true or it is false, and it
> cannot be both. And if you make a set of statements that allow the
> well-defined manipulations to produce a result of "true", then it does.
> That's all there is; there ain't no more. Formal logic is about "how to
> think"; it is not about what you think about.
> P.S. Ian:
>>> I never had a formal method for re-engineering data in those days. In recent
>>> years I've been using Chris Partridge's BORO Method. It seems to work pretty
>>> well as far as I can tell. Don't tell him I said that though.
> Since Chris is a frequent contributor to this exploder, your secret is
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