Information Technology Reference
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associating activity, and do not envision taking
the mind out of the loop. We are looking for ways
to increase the power and the ease of that mental
work. People often say that there are “innumer-
able contexts.” We know that the exabytes of
data flow are quite numerable -- there are simply
too many of them. But the number of contexts is
some reasonably finite multiple of the number of
people that a system would support. In the whole
world this is perhaps thousands of billions. Surely
many of them are so similar that sharing and reuse
would be really worth aiming for.
In an agency or corporate context, there are
perhaps thousands of searchers who might form
a pool, and they may each have no more than
a few hundred contexts that would be of inter-
est to us. These contexts would have enormous
overlap. All the people tracking developments in
Iran's nuclear program have only a few contexts:
scientific; political; warning analysis; background
analysis, etc. The key idea of “Quest Reuse” (QR)
is to store and reuse pools of “quest profiles” that
are effectively labeled by context, retrievable
by others with closely related contexts, and that
contain parameter settings which help a support
system (a hypothesis testing tool, a search engine,
a report generator, etc.) to refine, disambiguate
and prioritize what it seeks and what it finds.
To give the familiar trivial example, the word
“bank” in the aviation context loads more heavily
on “change direction” than it does on “financial
institution”.
The chapter will address researches at finding
out how to represent and store these profiles, and
how to retrieve them by
similarity
rather than
simply by
name
. That is, QR is valuable if I can
know that Larry often works on the same prob-
lems as I do, and I tell the machine 'Please load
context “Larry 23”.' But it is priceless if, by the
very actions I take, the system can recognize that
I would benefit from the context “Abigail 19”,
when I have never heard of Abigail. In a sense
this is the familiar theme of “finding experts”.
But the experts are to be labeled automatically by
analysis of what they look for, what they study
hard, and what they mark as “worth keeping” [a
crucial part of the model], as well as “what they
say they are up to”. We believe this is a worth
developing area, so that the many minds work-
ing on crucial problems can be working more
effectively together, communicating through the
perfect memories of the not-very-smart systems
that they use.
This same problem, in the specific applica-
tion to counterterrorism intelligence, is known
as “shoebox sharing.” The name is a holdover
from the days when an analyst would maintain
a shoebox containing 3x5 cards or 5x8 cards
summarizing interesting bits of information that
he or she found during research. Now that such
information is stored on the computer, it could
in principle be available to other analysts, but
it would be simply a waste of their time unless
they can make targeted forays into the material to
retrieve that which is most relevant to their own
present quests.
bACkground
In any model of information retrieval, an infor-
mation retrieval process is divided into three
basic parts: collections of information objects,
users, and searching techniques. Developments
in information technology, especially the per-
sonal computer and the Internet, have brought
significant changes to the user and the information
collection components. However, the techniques
do not change much of the fundamental mecha-
nism that represents and matches user needs and
information objects much. With the successful
application of information retrieval techniques
in web searching since early 90's, the searching
techniques have improved greatly, prompted by
the huge business interest of searching market.
However, the advances were mainly focused on
developing ways of improving existing indexing
and ranking techniques. For example, PageRank,
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