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Re: [ontolog-forum] Theoretical issues and practical applications

To: "[ontolog-forum]" <ontolog-forum@xxxxxxxxxxxxxxxx>
From: "John F. Sowa" <sowa@xxxxxxxxxxx>
Date: Mon, 04 Jan 2010 22:56:13 -0500
Message-id: <4B42B85D.8050107@xxxxxxxxxxx>
Jim,    (01)

Those are very good questions.  As a general comment, I would say
that there is no one-size-fits-all solution.  And *every* simple
slogan should be considered highly suspect.    (02)

JR> ... this discussion has yet to grapple with the practical
 > problems arising from the notion that the web will allow us
 > to integrate lots of knowledge bases to achieve a larger scope
 > of understanding than would result from any single project.
 > The intractability of formal reasoning poses a real threat
 > for this situation.    (03)

The first point I'd make is that one of the most pernicious
slogans is the following:    (04)

    Tractability  =  Polynomial time  =  Good.    (05)

When you're talking about billions of data items, any polynomial
with an exponent greater than 1 is a disaster.  A billion squared
is a quintillion.  And if you could process each data item in
one microsecond, a quintillion microseconds is greater than
the age of the universe.    (06)

For the web, you need logarithmic, linear, or at worst (N log N)
algorithms.  That's for the data.  But if you can extract some
subset of the data by those kinds of algorithms, then you can
take more time for the items of interest.    (07)

As an example, Experian -- one of the three major credit bureaus
that evaluates everybody's credit rating -- uses Prolog.  In fact,
they use Prolog so heavily that they bought Prologia, the company
that was founded by Alain Colmerauer, who implemented the first
Prolog interpreter.  They keep their algorithms secret, but I
would guess that they process the raw data in linear time and
extract smaller numbers of items of interest for more detailed
processing by more complex reasoning methods.    (08)

JR> Where is the research on central control of design and analysis
 > for logical systems? Where is the work on testing large collections
 > of knowledge and on determining the testing requirements for
 > merging two or more knowledge bases?    (09)

I'm sure that you remember the work on database schemas from the
'70s and '80s.  In 1980, there was a workshop that brought together
three groups:  programming language researchers, database researchers,
and AI researchers.  Among the participants were Ted Codd, Pat Hayes,
Jaime Carbonell, Ray Reiter, and quite a few others including me.
 From the dusty copy of the proceedings in my basement, I would
draw two sobering observations:    (010)

  1. The workshop had only a single track, but the talks clustered
     in three disjoint mini-workshops:  Prog. languages, DB, and AI.    (011)

  2. The topics covered and the level of the discussion was almost
     indistinguishable from the typical proceedings in ontology
     conferences today.  If anything, many of the recent conferences
     have degenerated to RDF & OWL hacking that is at about the same
     level of sophistication as typical SQL hacking.    (012)

To answer your question about when we can expect to merge independent
knowledge bases, I suspect that the answer is never.  When two banks
merge, they never merge their databases.  Either they keep both DBs
operating independently, or they close the accounts from one DB and
open new accounts in the other.  But banks have been interoperating
successfully since the Italian Renaissance by passing messages
(either paper or electronic).    (013)

People also interoperate by passing messages.  Except for the
rather rare Vulcan mind-melds, they never merge their knowledge
bases.  I doubt that computerized KBs will be merged any more
easily than banks or people.    (014)

Bottom line:  Focus on message processing, not on merging KBs.    (015)

John    (016)

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