> Date: Sun, 31 Mar 2013 09:55:02 -0400
> From: sowa@xxxxxxxxxxx
> To: ontolog-forum@xxxxxxxxxxxxxxxx
> Subject: Re: [ontolog-forum] Why a data model does not an ontology make
>
> Michel, Leo, and Michael,
>
> JFS
> > But what was produced [by the SW] failed to address the requirements Tim
> > proposed and many others (including Robert and me) believe are essential.
>
> MD
> > can you list/summarize the requirements and why you think the steps that
> > the semantic web effort has made do *not* contribute to those requirements?
>
> As I've said repeatedly, three words that Tim B-L emphasized in the DAML
> proposal of 2000 were diversity, heterogeneity, and interoperability.
>
> In the final DAML report of 2005, two of them (diversity and
> interoperability) were mentioned just once and heterogeneity was
> never mentioned at all.
>
> I also believe that Robert Meersman's short summary is very good:
>
> http://starlab.vub.ac.be/website/files/MeersmanBuffaloAug2007.pdf
> > Why "the" Semantic Web has failed.
> > * Data models vs. ontologies
> > * Legacy systems
> > * Scalability
> > * Methodology
>
> For point #1, RDF + SPARQL is just YADM -- Yet Another Data Model.
> It has few advantages and many disadvantages over data models that have
> been in use for decades. I have no objection to YADM if people find it
> useful, but I have serious objections to edicting any single data model
> as a requirement for the Semantic Web.
>
> For point #2, Tim B-L noted the importance of interoperability with
> legacy systems, but the DAML report ignored them completely. I can't
> blame them for not doing everything in five years, but they have not
> done *anything* to support legacy systems in the past 13 years.
>
> And please do not repeat the claim that they provided a tool to convert
> RDBs to RDF. Interoperability means that the legacy systems work with
> the new tools *concurrently* -- not by means of forced conversion.
>
> For point #3, the SW people claim that OWL is decidable. That only
> means that decisions terminate in *finite* time -- even though that
> time might be greater than the age of the universe. For anything
> the size of the WWW, scalability means no worse than (N log N) time.
>
> For point #4, please reread Robert M's slides for an example of what
> a methodology can and should support.
>
> Leo
> > The closest that relational databases get to having a semantic model
> > is the conceptual schema, which is a type of conceptual model (modeled
> > in a graphic Entity-Relation-Attribute language, with cardinality restrictions).
>
> Unfortunately, there was never a standard for a conceptual schema, and
> the vendors merely pasted the term 'conceptual schema' on top of what
> they were doing anyway. They turned it into an advertising slogan.
>
> E-R-A + cardinality is a requirement that must be specified in any
> conceptual schema (or ontology), but it's far from sufficient.
> And most of the published OWL ontologies do little or nothing
> to go beyond that level.
>
> From 1978 to 2000, the published R & D on the conceptual schema and
> related issues went far beyond what the vendors provided, Tim B-L
> cited some of that work, but the DAML developers ignored it.
>
> Leo
> > Now the above view does have rare exceptions in the database world: e.g.,
> > Matthew West's work immediately springs to mind. Similarly, HighFleet
> > (formerly Ontology Works) tries to bridge the ontology-database connection.
> > Also, of course deductive databases try to combine logic programming +
> > relational constructs, though these just focus on the implementational
> > apparatus you would need for more expressive ontologies, but say nothing
> > in particular about ontologies.
>
> I agree that the systems you mention are good. But there were many
> years of very good systems that the SW ignored. Deductive DBs were
> proposed in the 1970s -- note Planner and Microplanner. RDBs combined
> with Prolog and other AI tools have been widely used since the '80s.
> Tim B-L cited them in his DAML proposal of 2000, but the SW gnored them.
>
> By the way, two commercial companies *based* on Prolog + RDBs are
> Mathematica and Experian. Mathematica started with Prolog as their
> underlying reasoning engine in the '80s, and they have developed the
> foundation into a very rich logic-programming system that uses RDBs
> for external storage.
>
> Experian uses Prolog + RDBs for Big Data -- much bigger than any
> application that uses RDF + OWL. They compute everybody's credit
> rating on a daily basis with every imaginable input they can find.
> They use Prolog so heavily that they bought the Prologia company.
>
> MB
> > But I have to add that the transition between data model and ontology
> > is fluent. In practice, you often have to make compromises - for example to
> > enable better querying or because knowledge and application data cannot be
> > untied easily.
>
> I agree. And those issues were addressed in the 3-schema strategy of
> of the original ANSI/SPARC TR in 1978. The conceptual schema -- which
> is very close, if not identical, to what we now call formal ontology --
> was at the heart of the proposal. The physical schema, which is very
> close, if not identical to what is called the data model, specifies
> the data formats, layout, and structure. The application schema
> specifies the APIs of the software.
>
> I also agree that the detailed ontologies will often use primitives
> and operations that have a simple mapping to the preferred data model.
> That is another reason why I have recommended an underspecified upper
> level ontology with families of "microtheories" for more specialized
> ontologies that are optimized for different kinds of applications.
>
> But those issues get into details that we have discussed many
> times before, and I won't repeat them now.
>
> John
>
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