Dear Matthew, Andrea, and Jack, (01)
MW
> Reuse also improves quality. The more an ontology is used,
> the more bugs are identified and eliminated, and this is even
> more the case when the different uses are diverse. (02)
I agree with that point. But as a logician would say, any change
to a theory makes it a different theory. That means you need
some method for specifying how the improvements are related to
the original and to one another. (03)
AW quoting JR
> Jack went further to say ...
>
>> Seems to me that any chunk of an ontology may be or may not be
>> Reuseable depending on whether the specification of its attributes
>> (delimiters) is sufficient. Further, that John Sowa's multifacted
>> diamond structure specifies those attributes. (04)
It's always nice to hear favorable comments. But I'd like to
translate the comments about diamonds and attributes to the
terminology I usually use to discuss them: lattices. (05)
There are two ways I've used lattices in discussing ontologies: (06)
1. Organizing the terms, classes, types, or attributes in
a hierarchy: A lattice is a hierarchy in which any two
items have a supremum (common supertype) and an infimum
(common subtype). (07)
2. Organizing theories in a lattice -- in which the supremum is
their common generalization (less detail) and their infimum
is their common specialization (merger). If the two theories
are inconsistent -- the infimum is the *absurd theory* that
contains every proposition p and its negation ~p. (08)
To relate the lattices to Matthew's remarks, I would say that
the way "reuse improves quality" is by making theory revisions
in a systematic way. For a summary, see slides 77 to 81 of (09)
http://www.jfsowa.com/talks/iss.pdf (010)
Those slides are from 2010, and I'd make some revisions today.
But I'm happy with that section from 77 to 81, and I'd still
recommend it as a direction for the future. (011)
And while we're talking about lattices, I'd like to mention
a highly efficient implementation of an order-sorted logic
(lattice based) by Hassan A-K and his colleagues (copy below). (012)
Note that their CEDAR system imports huge ontologies in OWL and
runs circles around the fastest OWL tools. During the importation,
it also detects inconsistencies (cycles). (013)
John
___________________________________________________________________ (014)
The following excerpt is from http://www.jfsowa.com/logic/sorts.pdf (015)
More recently, Amir and Aït-Kaci (2013) compared the CEDAR system, which
uses an OSL for classifying and querying very large taxonomies, to six
OWL-based reasoners: Fact++, HermiT, Pellet, TrOWL, RacerPro, and
SnoRocket. They compared them on four taxonomies that ranged in size
from 111,559 sorts or classes (Wikipedia) to 903,617 sorts (NCBI). (016)
For classification, CEDAR was among the three fastest for all the
taxonomies. On the Wikipedia taxonomy, it was five times faster than the
second best (Fact++). For querying, CEDAR beat all the others by several
orders of magnitude. The query time is the most important, since a
classified CEDAR taxonomy can be saved and reused. CEDAR also detects
cycles in the taxonomy, which are a serious source of inconsistencies. (017)
For related issues, see the three slide presentations by Aït-Kaci (2013). (018)
Aït-Kaci, Hassan (2013) Three slide presentations:
Is it possible to make the Semantic Web a reality?
http://cedar.liris.cnrs.fr/papers/intis.pdf ;
Reasoning and the Semantic web,
http://cedar.liris.cnrs.fr/papers/ontoforum.pdf ;
Empirical study of high-performance triple stores,
http://cedar.liris.cnrs.fr/papers/Presentation_Cedar-PetaSky-LIP_ENS-Web-Site.pdf (019)
. (020)
Amir, Samir, & Hassan Aït-Kaci (2013) Fast taxonomic reasoning based
on lattice operations, CEDAR Technical Report No. 3, LIRIS-UFR
d’Informatique. http://cedar.liris.cnrs.fr/papers/ctr3.pdf (021)
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