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
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 AT EnglishLogicKernel DOT com
9 4 9 \ 5 2 5 - 5 7 1 2
[mailto:ontolog-forum-bounces@xxxxxxxxxxxxxxxx] On Behalf Of John F Sowa
Sent: Thursday, August 09, 2012 6:04 AM
Subject: Re: [ontolog-forum] Ontologies,
knowledge model, knowledge base
> I have encountered some difficulties concerning
> of these terms and how they are related each
Those are closely related terms, and nobody has ever
precise definitions that could distinguish them.
The oldest of the three is 'knowledge base'. It
in the late 1970s and early '80s to distinguish AI
especially expert systems, from the more familiar
Another term that was popular around the same time was
'deductive database'. It was used for systems
rules or axioms plus an inference engine to a
The idea is that the rules or axioms were the
and the data stored in the DB specified the facts (or
level clauses, as they're called in logic).
In philosophy, the word 'ontology' means the study of
A specific ontology is a theory about what
exists. I used that
word my 1984 book, _Conceptual Structures_, but always
In the late 1980s, Doug Lenat coined the term
as a variation of 'knowledge engineering', and he
*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
of everything that exists (or can exist) in some
The crucial addition is a *theory* about what exists.
That theory determines the critical axioms and
that distinguish a knowledge base from a database.
But that definition leaves the distinction between an
ontology and a knowledge base very unclear. Are
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
answer. Some people claim that only the most
fundamental axioms and definitions belong in the
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
version of logic, such as Aristotle's syllogisms, to
the ontology. The Description Logics are a minor
of Aristotle's logic that are widely used for
A popular example is OWL.
But that raises another issue: where do you draw the
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
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
cross references for the hierarchical model.
Each of those three
models has exactly the same expressive power, since
in one can be translated to the others.
For knowledge bases, it's not clear how to distinguish
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:
Knowledge Design Patterns
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