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In AIR, each new document first causes a corresponding document node to
be generated. An author node is then generated (if it doesn't already
exist) for each author of the document. Two links are then created
between the document and each of its keywords (one in each direction),
and two more between the document and each of its authors. Weights are
assigned to these links according to an inverse frequency weighting
scheme: the sum of the weights on all links going out of a node is
forced to be a constant; in our system that constant is one. Figure
(figure) shows the subnet corresponding to the book
Parallel Models of Associative Memory, by J. A. Anderson and G.
E. Hinton [REF35] .
The initial network
is constructed from the super-position of many such documents'
representations. Most of the experiments to be described in this report
used a network constructed from 1600 documents, forming a network of
approximately 5,000 nodes. This is a trivial corpus, and used relatively
crude lexical analysis and keyword weighting ideas. However, AIR
requires only that the initial automatic indexing assign some
weighted set of tentative keywords to each document.
There is one
property of the inverse weighting scheme on which AIR does depend,
however. A network built using this keyword weighting scheme, together
with similar constraints on the weights assigned author links, has the
satisfying property of {conserving activity}. That is, if a unit of
activity is put into a node and the total outgoing associativity from
that node is one, the amount of activity in the system will neither
increase nor diminish. This is helpful in controlling the spreading
activation dynamics of the network during querying.
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Basic representation