Dear John,
In your tutorial, on slide 9, you state:
We need better tools,
interfaces, and methodologies:
● Experts in any field
spend years to become experts.
● They don’t have time to
learn complex tools and notations.
● The ideal amount of
training time is ZERO.
● Subject-matter experts
should do productive work on day 1.
The gist of that bullet list is that people should all
learn one ontology language/toolset/methodology. It will come as no
surprise to you that I respectfully disagree. The knowledge that SMEs
develop is strictly in the application domain, and almost never in any
theoretical area other than the usual minor amount of math, physics, chemistry
or other more generalized knowledge.
It is a common misconception among knowledge engineers
that there is a universal way to scoop up domain knowledge and process it
automatically, but that misconception has made real progress in applying FOL
and knowledge engineering to the complex real world applications which actually
require huge amounts of experience, and the internalization of knowledge
through that experience, which makes an SME so productive compared to a
beginner.
IMHO, that is why Cyc has not been successful.
The basic assumptions are wrong. For a deeper explanation, read Malcolm
Gladwell’s “Outliers: The Story of Success”
-Rich
Sincerely,
Rich Cooper
EnglishLogicKernel.com
Rich AT EnglishLogicKernel DOT com
9 4 9 \ 5 2 5 - 5 7 1 2
-----Original Message-----
From: ontolog-forum-bounces@xxxxxxxxxxxxxxxx
[mailto:ontolog-forum-bounces@xxxxxxxxxxxxxxxx] On Behalf Of John F Sowa
Sent: Thursday, August 09, 2012 6:04 AM
To: ontolog-forum@xxxxxxxxxxxxxxxx
Subject: Re: [ontolog-forum] Ontologies,
knowledge model, knowledge base
Dear Juan,
> I have encountered some difficulties concerning
the meaning
> of these terms and how they are related each
other.
Those are closely related terms, and nobody has ever
stated
precise definitions that could distinguish them.
The oldest of the three is 'knowledge base'. It
became popular
in the late 1970s and early '80s to distinguish AI
systems,
especially expert systems, from the more familiar
databases.
Another term that was popular around the same time was
'deductive database'. It was used for systems
that added
rules or axioms plus an inference engine to a
database.
The idea is that the rules or axioms were the
knowledge base,
and the data stored in the DB specified the facts (or
ground-
level clauses, as they're called in logic).
In philosophy, the word 'ontology' means the study of
existence.
A specific ontology is a theory about what
exists. I used that
word my 1984 book, _Conceptual Structures_, but always
in the
philosophical sense.
In the late 1980s, Doug Lenat coined the term
'ontology engineering'
as a variation of 'knowledge engineering', and he
advertised for
*ontology engineers* for the Cyc project. At
that time, the term
was mildly humorous or mildly startling. But in
the 1990s, it
became more popular.
The basic idea is that an ontology goes beyond a
taxonomy
of everything that exists (or can exist) in some
domain.
The crucial addition is a *theory* about what exists.
That theory determines the critical axioms and
definitions
that distinguish a knowledge base from a database.
But that definition leaves the distinction between an
ontology and a knowledge base very unclear. Are
all the
axioms and definitions of a knowledge base part of the
ontology? Or only some of them? Where do
you draw the
line to distinguish them?
This question has been hotly debated, and nobody has a
clear
answer. Some people claim that only the most
important or
fundamental axioms and definitions belong in the
ontology,
and the less important ones should be in that part of
the knowledge base that is outside the ontology.
Other people argue that the ontology should use a very
simple
version of logic, such as Aristotle's syllogisms, to
define
the ontology. The Description Logics are a minor
extension
of Aristotle's logic that are widely used for
ontology.
A popular example is OWL.
But that raises another issue: where do you draw the
line between
the logic used to define the terms, and the logic used
for the more
detailed reasoning. That is a controversial
issue. Anybody who
answers it uses their favorite technology to make the
distinction.
Finally, 'knowledge model' is a term that is related
to the term
'data model', which developed in the database
field. In DBs,
the distinction was about the storage method: in
tables for the
relational model, networks for the network model, or
trees with
cross references for the hierarchical model.
Each of those three
models has exactly the same expressive power, since
anything stated
in one can be translated to the others.
For knowledge bases, it's not clear how to distinguish
a knowledge
model from an ontology. And since the
distinction between an
ontology and a knowledge base is unclear, it's even
harder to say
what a knowledge model could be.
For more about these issues, see the slides I
presented in June
for a tutorial at the Semantic Technology Conference:
http://www.jfsowa.com/talks/kdptut.pdf
Knowledge Design Patterns
John Sowa
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