Dear David and Kingsley,
David wrote:
Understanding language
& meaning across Silos would be an extremely useful application for
Ontologies if they can be commercialized.
But as you noted, the data is the only available view, with or without
ontologies. Adding ontologies to still the Babel of the silos could be the challenge
task for some prize or other, comparable to Turing’s challenge, but
likely at least somewhat less difficult since the data is already in
tables.
-Rich
Sincerely,
Rich Cooper
EnglishLogicKernel.com
Rich AT EnglishLogicKernel DOT com
9 4 9 \ 5 2 5 - 5 7 1 2
From:
ontolog-forum-bounces@xxxxxxxxxxxxxxxx [mailto:ontolog-forum-bounces@xxxxxxxxxxxxxxxx]
On Behalf Of David Eddy
Sent: Friday, February 14, 2014
5:20 PM
To: [ontolog-forum]
Subject: Re: [ontolog-forum] What the difference re., Data
Dictionary,Ontology, and Vocabulary?
Kingsley -
On Feb 14, 2014, at 7:47 PM, Kingsley Idehen wrote:
I have spent more than 20+ years of total dedication to making new and
emerging technologies work with existing (so called legacy) systems. I founded
OpenLink Software to enable integration of data across artificial data silos,
created by *myopic* applications.
But that's the DATA the systems produce.
I trying to talk about the SYSTEMS, that produce the data.
The SYSTEMS & the DATA are not the same thing.
The SYSTEMS are the machine tools that produce the end product, DATA.
I am reminded of wisdom from the 1840s when industrial America was
learning how to make things. It was noticed that building quality into
the manufacturing process is far more efficient than inspecting defects out.
Just handling the data—the manufactured product—is an
exercise in futility until a firm understanding of the upstream manufacturing
process is fully understood.
If one doesn't know which systems, programs, logic, data structures
& rules are producing the data, how will one know when the data suddenly
changes?
The sort of data dictionary product I'm talking of—decidedly not
a list of data elements—enables organizations to do impact
analysis—what's connected to what—so that we're not constantly
repairing down-stream errors.
integration of data across
artificial data silos,
Integration is not possible with silos. You mean interoperable.
There's a huge difference.
Integration is possible when one is in command, owns the budget & has
a blank sheet of paper, otherwise the only option is interoperability.
The silos are not necessarily artificial. They were built that
way for valid reasons... available skills, limits of technology, budget,
vision, deadlines, organizational boundaries, etc.
Silos are a complex reality we're going to have to learn to deal
with... as John Sowa points out,
these systems are going to be with us for decades.
Understanding language & meaning across Silos would be an extremely
useful application for Ontologies if they can be commercialized.