Jack Park wrote:
> From: Eric Armstrong <firstname.lastname@example.org>
> > I thought our primary focus was OHS-related, avoiding deeper
> > knowledge issues, at least at the outset.
> Interesting point. But then, if you focus the initial design on
> solving one problem, you risk painting yourself into a corner that
> forces redesign later on. I'm a "big picture" player, myself.
I agree with that, in principle. I like to design in the large, then
narrow the focus down to the smallest thing I can construct and start
building outward from that kernel. The question is, is there a smooth
transition between the two domains (OHS & DKR)?
There is an interesting possibility that there is. Looking over the
Traction offering and comparing that with IBIS concepts led to the
minor epiphony that the simple act of categorizing information nodes
according to some (agreed upon) schema is in essence a knowledge-
abstraction process. I'm convinced that Traction is absolutely on the
right track with respect to categorization -- IBIS is an
easily-definable subset of their system. Where they fall down is with
respect to document hierarchy, but they've made a contribution (to my
thinking, at least)
with respect to categories.
What is interesting, here, is the concept that the whole "knowledge
management" domain exists in the realm of the categories, where
"documents" are found among the information nodes. If it makes sense
to think of knowledge management in those terms, then we can conceivably
apply some interesting abstract manipulations to "knowledge", where
knowledge means a common (or possibly standard) set of categories, and
where the underlying information is unique to each domain.
For example, the category "argument for" can be applied to information
nodes in a biological sciences domain, or to one in an art analysis
domain. The category is a form of meta-data that is independent of the
Now, given a standard set of categories, it might be possible to begin
describing category-relationships. That would produce the property of
abstract reasoning, that was independent of the problem domain.
I keep thinking in terms of "implies". If there is some way to add the
meta-data "implies" in the category space, then automated reasoning
Example: at the initial writing, node A is written, as well as node B,
with the "implies" attribute linking the two. Later, someone adds C as
an implication of B. The system can now deduce that A implies C --
regardless of the information content contained in the nodes.
Perhaps the "category" for such a system is "implication". Categorizing
B as an implication then requires pointing to A, to identify the node
from which B was derived. The symettric relationship can then also be
added -- call it "motivator", or some such. (If there is a logic term
for it, I've forgotten it.)
There might also be categories for preconditions, requirements, and
what have you, all of which would allow for fairly sophisticated
reasoning engines to be built on top of the fundamental structures.
[There are also evaluations -- of node content as well as the logic
The interesting point to all this is that the "DKR" becomes a layer
of abstraction built on the OHS, where the categorization-capability
is already built into the OHS.
> > ...4 suggestions for continuation:
> > 1) Pursue the WBI vector
> > 2) Follow an ISO 9000 path
> > 3) " a path based on Nancy Glock's Knowledge Representation
> > 4) Focus on building the narrative
> Which do I prefer? Well, I'm playing with WBI, so ruling that one out,
> I'd much prefer to build the narrative, since that's the first step in
> the other two.
Our meta agenda, then, is:
1. Tomorrow: The WBI vector
2. Building the Narrative
3. (Ongoing) Evaluating Existing Collaboration Tools
4. Back to Use Cases, etc, as in the previous Agenda posting.
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