Doug and Rich, (01)
In general, I agree that Cyc has a good metalevel terminology and
methodology for knowledge engineering. But I would cite the warning
by Alan Perlis: You can't translate informal language to formal
language by any formal algorithm. There are many "judgment calls"
that cannot be stated in hard-and-fast rules. (02)
DF
> At Cycorp... We considered an ontology to define terms, properties
> of terms, and theories about the terms, while a knowledge base was
> like a database, using terms defined and provided rules in the ontology
> to describe information about individuals in some domain of concern. (03)
I agree that's a good distinction. But the dividing line between what
should be in the definitions and what should be in the "background
knowledge" is often fuzzy. (04)
Many people let their tools make the distinction: if it can be
stated in OWL, it's ontology; anything else goes in the knowledge
base or database. But that distinction is unreliable. (05)
For example, Plato cited a definition of Human as 'animal with speech'
or 'featherless biped'. Either one could be represented in OWL,
but the first is preferred because it is *essential*, but the other
is *accidental*. However, many people -- Quine is a prime example --
maintain that there are no clear criteria for making that distinction. (06)
DF
> Vocabulary microtheories, Theory microtheories, and Data microtheories. (07)
That's also a good distinction. But there are many vocabulary terms
that are also technical terms in some theory. For example, the words
'force', 'energy', and 'mass' are common vocabulary terms that became
technical terms in physics. (08)
When you have multiple microtheories that use the same technical terms,
you also run into issues about using values defined in different ways
in different microtheories. That can become critical for a large
engineering project that uses different microtheories to specify
different kinds of components. (09)
DF
> This term [knowledge model] was not used at Cycorp while i was there. (010)
I agree that it's rare, and I would avoid it. (011)
RC
> Actually, rules can be stored in relational DBs also, not just facts. (012)
You can store anything in any DB, if you treat it as an uninterpreted
"blob" (Binary Large Object). The critical distinction is how and
whether those rules are used in reasoning. (013)
In SQL, there are three kinds of "knowledge" that can be used to
supplement the database: *views*, which are backward-chaining rules;
*constraints*, which block illegal updates; and *triggers*, which
are forward-chaining rules that invoke operations during updates. (014)
If you use those features extensively, they would make SQL into
a kind of deductive database that could be called a knowledge base.
This is an issue that many DB and AI people have been discussing
since the 1970s -- that includes Ted Codd, who was not happy with
the quirks and limitations of the SQL design and implementation. (015)
RC
> 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. (016)
No, definitely not! What I was trying to say is that future systems
should support *everybody's* favorite ontology and notation. When
I said "zero training time", I meant that nobody should be required
to learn a notation or a vocabulary that is different from whatever
terms, diagrams, and notations they prefer for their daily work. (017)
> 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. (018)
I agree with that principle. My only disagreement is in the claim
that applications don't involve theory. I use the word 'theoretical'
for *every* kind of "book learning'. That includes all knowledge
that is represented in words or symbols of any kind. (019)
John (020)
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