Dear John and Juan, (01)
Actually, rules can be stored in relational DBs
also, not just facts. It's slightly messier to do
arbitrarily complex rules folded into flat nodes,
but it is feasible for many applications. (02)
-Rich (03)
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 (04)
Dear Juan, (05)
> I have encountered some difficulties concerning
the meaning
> of these terms and how they are related each
other. (06)
Those are closely related terms, and nobody has
ever stated
precise definitions that could distinguish them. (07)
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. (08)
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). (09)
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. (010)
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. (011)
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. (012)
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? (013)
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. (014)
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. (015)
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. (016)
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. (017)
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. (018)
For more about these issues, see the slides I
presented in June
for a tutorial at the Semantic Technology
Conference: (019)
http://www.jfsowa.com/talks/kdptut.pdf
Knowledge Design Patterns (020)
John Sowa (021)
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