Previous discussions described the variability
of contexts. The reason it is important to store rules as well as facts in the
database is that such architecture makes it very easy to store a table of
context IDs and relate them to which rules and facts apply to each context
ID. Searching for contexts becomes very manageable with that
To process a given context ID, retrieve
all the rules and facts into a single graph (locked in fast memory) and then
perform Q&A searches (also locked in memory) as needed to process part or
all of the specified context. The memory can be released when that
context is no longer applicable. Also, numerous contexts which are
related can also be modeled in a graph, and searched, leading to recursive
processes that are much easier to manage than without such capabilities.
Rich AT EnglishLogicKernel DOT com
9 4 9 \ 5 2 5 - 5 7 1 2
[mailto:ontolog-forum-bounces@xxxxxxxxxxxxxxxx] On Behalf Of Rich Cooper
Sent: Friday, August 10, 2012
Subject: Re: [ontolog-forum] Ontologies, knowledge model,
> Actually, rules can
be stored in relational DBs also, not just facts.
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
While BLOBs are widely used to store unstructured
quantitative data such as EKG signals, documents, and such other byte arrays in
a DB cell, that isn’t what I was referring to. BLOBs are useful when the
data can be inserted, queried or deleted in one hunk without further semantic
interpretation by the DBMS. Applications interpret each BLOB based on
what the programmer “knows” the BLOB implements.
Consider a parsed rule which parse produces a graph of
the syntax of the rule. Think of the way algebraic parsers operate.
The parse output consists of triples: one operator and two objects that
participate in the operation of that triple. Allocate one node in the
graph for each of the three elements, with the operator at the top node and two
arcs leaving said top node toward the two objects. Store the graph in two
tables called Nodes and Arcs.
For parameterized function calls such as f(x,y), the
parse produces arcs which are labeled with the parameterization term. For
example, consider the simple equation:
f(x,y) = x + y
which can be drawn in graph form as:
Whereas a BLOB could also be used, the above
representation lets the programmer store the triples in Nodes and Arcs rows
where they can be more easily programmed to traverse the graph.
I use a different <Node, Arc> design for the
triple graph from that I use for the search forest. Searching the graph
constructs the forest, and leads to the AND/OR structuring I’ve mentioned many
times in past posts. The solution subtree is the embedded tree with max
value, min cost, or whatever criterion is used for choosing the preferred
Rich AT EnglishLogicKernel DOT com
9 4 9 \ 5 2 5 - 5 7 1 2
From: ontolog-forum-bounces@xxxxxxxxxxxxxxxx [mailto:ontolog-forum-bounces@xxxxxxxxxxxxxxxx]
On Behalf Of John F Sowa
Sent: Friday, August 10, 2012 9:11 AM
To: '[ontolog-forum] '
Subject: Re: [ontolog-forum] Ontologies,
knowledge model, knowledge base
Doug and Rich,
In general, I agree that Cyc has a good metalevel
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
that cannot be stated in hard-and-fast rules.
> At Cycorp... We considered an ontology to define
> 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.
I agree that's a good distinction. But the
dividing line between what
should be in the definitions and what should be in the
knowledge" is often fuzzy.
Many people let their tools make the
distinction: if it can be
stated in OWL, it's ontology; anything else goes in
base or database. But that distinction is
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
> Vocabulary microtheories, Theory microtheories,
and Data microtheories.
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.
When you have multiple microtheories that use the same
you also run into issues about using values defined in
in different microtheories. That can become
critical for a large
engineering project that uses different microtheories
different kinds of components.
> This term [knowledge model] was not used at
Cycorp while i was there.
I agree that it's rare, and I would avoid it.
> Actually, rules can be stored in relational DBs
also, not just facts.
You can store anything in any DB, if you treat it as
"blob" (Binary Large Object). The
critical distinction is how and
whether those rules are used in reasoning.
In SQL, there are three kinds of "knowledge"
that can be used to
supplement the database: *views*, which are
*constraints*, which block illegal updates; and
are forward-chaining rules that invoke operations
If you use those features extensively, they would make
a kind of deductive database that could be called a
This is an issue that many DB and AI people have been
since the 1970s -- that includes Ted Codd, who was not
the quirks and limitations of the SQL design and
> In your tutorial, on slide 9, you state:
> We need better tools, interfaces, and
> ● 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.
No, definitely not! What I was trying to say is
that future systems
should support *everybody's* favorite ontology and
I said "zero training time", I meant that
nobody should be required
to learn a notation or a vocabulary that is different
terms, diagrams, and notations they prefer for their
> 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
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.
Shared Files: http://ontolog.cim3.net/file/
Community Wiki: http://ontolog.cim3.net/wiki/
To join: http://ontolog.cim3.net/cgi-bin/wiki.pl?WikiHomePage#nid1J