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Re: [ontolog-forum] Ontologies, knowledge model, knowledge base

To: "'[ontolog-forum] '" <ontolog-forum@xxxxxxxxxxxxxxxx>
From: "Rich Cooper" <rich@xxxxxxxxxxxxxxxxxxxxxx>
Date: Thu, 9 Aug 2012 14:07:10 -0700
Message-id: <A768CE395E334656B0830B0DC24078B9@Gateway>

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 Cooper


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:



    Knowledge Design Patterns


John Sowa



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