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Re: [ontolog-forum] [ontology-summit] Proposed RDF FHIR syntax feedback

To: "[ontolog-forum]" <ontolog-forum@xxxxxxxxxxxxxxxx>
Cc: ontology-summit@xxxxxxxxxxxxxxxx, Hassan Aït-Kaci <hak@xxxxxxx>
From: John F Sowa <sowa@xxxxxxxxxxx>
Date: Wed, 11 Mar 2015 00:17:48 -0400
Message-id: <54FFC1EC.90405@xxxxxxxxxxx>
On 3/10/2015 5:14 PM, Pat Hayes wrote:
> AFAIK, nobody has claimed that [OWL] is The Ontology Language. It is
> "the" W3C-recommended WEB Ontology Language, with an emphasis on
> the "Web" part.    (01)

As soon as I hit SEND, I realized that I should have added the word
'web' because somebody was going to make that point.  But I'll cite
the following 4-page paper by Hassan Aït-Kaci:    (02)

    What formalism for the Semantic Web?    (03)

This short paper discusses the issues (with lots of references).
See below for excerpts from one of them.    (04)

I know that somebody will say "Yes, but the SW has huge volumes
of data.  We need high-speed performance."    (05)

Fortunately, HAK designed the CEDAR system, which runs circles
around the fastest OWL reasoners on very large ontologies
(ranging from 6,135 to 903,617 sorts or classes).  At the end of
this note is a copy of an earlier note I sent to Ontolog forum
with the references and details.    (06)

Fundamental point:  Anyone who finds OWL useful can continue to use
it indefinitely.  For those very large ontologies, CEDAR imported
the OWL specifications.  And it runs SPARQL queries that use them
-- but with a huge improvement in performance.    (07)

And CEDAR is just one example.  In the next few years, we may see
many other possibilities for better ontology languages -- for the SW
and for many other purposes.  We need standards, but we also need
to support an open-ended diversity.  That's the point of the web page
about interoperable systems:  http://www.jfsowa.com/ikl    (08)

_____________________________________________________________________    (09)

Data models as constraint systems:  a key to the semantic web    (010)

 From page 39:    (011)

4. Relation between OSF and DL formalisms    (012)

Description Logic (DL) and Order-Sorted Feature (OSF) logic are two
mathematical formalisms that possess proof-theories based on a
constraint formalism. Both are direct descendants of Ron Brachman’s
original ideas (Brachman, 1977). This inheritance goes through my own
early work formalizing Brachman’s ideas (A¨ıt-Kaci, 1984), which in
turn inspired the work of Gert Smolka, who pioneered the use of
constraints both for the DL (Schmidt-Schauß and Smolka, 1991) and
OSF (Smolka, 1988) formalisms. While the DL approach has become the
mainstream of research on the semantic web, the lesser known OSF
formalisms have evolved out of Unification Theory (Schmidt-Schauß
and Siekmann, 1988), and been used in constraint logic programming
and computational linguistics...    (013)

p. 43    (014)

5. Conclusion    (015)

We have shown how constraint logic programming offers an invaluable
abstraction mechanism for “integrating” correctly, seamlessly, and —
to boot! — operationally, rule-based programming (e.g., definite-
clause logic programming) with data description logics. Seen as
a formal constraint system, the data model is thus abstracted from
the rule model...    (016)

Easing rule interoperability is yet another substantial benefit of
the “data as constraint” approach.  Indeed, constraints are the
right level of abstraction for rule interchange because they allow
approximation. Approximation is important for exchange as one may
still wish to exchange rules at some level of abstraction...    (017)

Finally, the most obvious benefit of seeing data description as
constraints is that it simplifies things at both the theoretical
and practical level...    (018)

-------- Forwarded Message --------
Subject: Order-sorted reasoning for the Semantic Web
Date: Tue, 02 Dec 2014 10:46:30 -0500
From: John F Sowa    (019)

Last year, Hassan Aït-Kaci sent a note to Ontolog Forum (copy below),
and I replied "Your two slide shows are an excellent proposal for
the future development of the Semantic Web."    (020)

More recently, he sent me a new report about the updated version
of CEDAR, which is more robust and expressive than last year's.
It is still orders of magnitude faster than popular web reasoners
(Fact++, HermiT, Pellet, TrOWL, RacerPro, SnoRocket):    (021)

    Design and implementation of an efficient Semantic Web reasoner    (022)

To evaluate CEDAR, they used six ontologies, for which the number
of sorts (or classes) range from 6,135 for Amphibian to 903,617
for NCBI.  (See p.12)    (023)

For classification (p. 13), CEDAR and FACT++ are the fastest.
Each one beats the other on 3 of the 6 ontologies.    (024)

But after the classification stage, CEDAR is much, much faster than
the others on queries -- by two or three orders of magnitude (p. 14).    (025)

Some excerpts from the report:    (026)

 > For the same queries on the same ontologies, the results achieved
 > by CEDAR were compared to those obtained by all the other reasoners.
 > The results of experiments show that CEDAR consistently performs
 > on a par with the fastest systems for concept classification, and
 > several orders of magnitude more efficiently in terms of response
 > time for Boolean query answering over attributed concepts, as well
 > as for ABox triplestore querying. The latter result is irrespective
 > of the triplestore management used because the CEDAR reasoner uses
 > its knowledge to optimize SPARQL queries before submitting them
 > to the triplestore.    (027)

 > We use “sort” as a synonym of atomic “class” or “concept.”  In
 > particular, sorts are partially ordered type symbols, where the
 > ordering (“is-a”) denotes set inclusion.    (028)

 > this version ... can process both TBox and ABox information. It starts
 > with a classification phase performed on the TBox which includes cycle
 > detection, transitive closure of the “is-a” taxonomic ordering, and
 > feature domain/range constraint propagation down the taxonomy, and
 > normalization of all such information. Then, the encoded TBox is saved
 > on secondary storage independently of any ABox.    (029)

 > Finally, for retrieving instances, normalized queries are translated
 > to SPARQL form that can be optimized using TBox knowledge. In Section
 > 3.2, we discuss the classification and the query normalization steps
 > and give an example of the SPARQL query generation process.    (030)

The above results are for standard RDF triples.  But when sort
or type labels are added to the triples, the triple store can be
indexed for even better performance.  Depending on the kind of
query, the performance improves by a few more orders of magnitude.
(pp. 16-17).    (031)

In many cases, the CEDAR software can use the ontology to analyze
RDF and infer the implicit sorts (or types).  But it would be more
convenient for *both* humans and computers to use a notation that
supports typed triples (and n-tuples) explicitly.    (032)

John    (033)

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