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We ask that comments and discussion on this series be made in the Yahoo groups from, eventChemistry.

APC1 – On enumeration by the human of the cell values over an event class

APC2– Minimal Voting Procedure

APC3 – Using the MVP to rout information

APC4 – Using eventChemistry to improve the framework specification


Action Perception Cycles 3


Using the MVP to rout information



The instantiation of a Sowa-Ballard framework for each of a series of events lead to a categorical abstraction about the nature of the cell in the context of the slots of events in the domain space


{ E l | l = 1, . . . , 100 }


{ Fl | l = 1, . . . , 100  }  à {  { a(i) l } i |  i = 1, . . . , 18  }


The Sowa-Ballard Framework is derived from:


{ independent, relative, mediating }

{ physical, abstract }

{ occurrent, continuant, universal }


From each of the slots (e.g., framework cells) we create categories around those values which are the same, or closely similar.  The issue of similarity must be formally handled with a thesaurus so that if strings that are not equal are to be treated as equal, for the purpose of the categorization; then this is well documented.  We might reduce the size of the set of  { a(i) l } first in the very nature way in which exact equality will reduce the size of a set so that


{ a, a }  =  { a }


In doing this, one might record the frequency of the value as an occurrence in the slot.  Due to repetition, the set of values will often be less than the number of events.   One might reduce the set of categories further using a thesaurus.  The result of this process of reduction produces categorical abstraction atoms for each slot.  The notation for the categorical abstraction atoms is made by introducing a prime symbol, so that a’(i) is used for the derived slot atoms and a(i) is used to indicate the original cell value.  It is appropriate to talk about the reification of slot atoms.


Now, following the original notation for the Minimal Voting Procedure we have, for each in a series of events


Domain space = { E l | l = 1, . . . , 100 },


the set of categories C = { Cq } is predefined, initially, and associated with the names of the event types.


C = {  a(0) l   } = {  a(0) l   | l = 1, . . . , 100  }  = {  a’(0) g   | g = 1, . . . , q < or = 100  }


Where the prime mark “ ’ ” in a’(0)  indicates that the set { a(0) l   } has been reduced using similarity analysis (see for example the work by Prueitt on declassification similarity engine). 


For each of the events we produce a representational set for the event using a Framework, such as the Zachman or Sowa-Ballard.   For specificity let us assume that we are using the 18-element Sowa-Ballard Framework.  Over the domain space, assuming 100 events, we have:


Domain space à { < a(0), a(1), a(2), . .  . , a(18) > l   | l = 1, . . . , 100  }

In the Minimal Voting Procedure notation, objects

O = { O1 , O2 , . . . , Om }

can be documents, semantic passages that are discontinuously expressed in the text of documents, or other classes of objects, such as electromagnetic events, or the coefficients of spectral transforms.  Here we take the objects to be events and m to be 100.

Some representational procedure is used to compute an "observation" Dr about the events. The subscript r is used to remind us that various types of observations are possible and that each of these may result in a different representational set.

We use the following notion to indicate the observation using a Sowa-Ballard Framework:

Dr : Ei à { a(0), a(1), a(2), . .  . , a(18) }

This notion is read "the observation Dr of the event Ei produces the representational set { a(0), a(1), a(2), . .  . , a(18) }

We now combine these event representations to form category representations.

·         each "observation", Dr, of the event has a "set" of cell values

Dr : Ek à Tk = { a(0), a(1), a(2), . .  . , a(18) }

·         Let A be the union of the individual event representational sets Tk.

A = È Tk.

One can talk about slot entanglement in various ways.  If S(i) and S(j) are two slots and q is a slot atom in both slots, then a SLIP reading of the membership records for S(i) and S(j) will produce the categoricalAbstraction atoms s(i) and s(j) with the “relationship” between the two slots given as the slot atom q.  The SLIP parse of the data will produce the relationship, called by Pospelov a syntagmatic unit,

< s(i), q, s(j) >

The categoricalAbstraction (cA) and eventChemistry (eC) software products now (as of September 2002) allow humans to easily see all of the entanglement between slots, and to annotate meaning to this entanglement.

This set A is the representation set for all of the slots of the framework over the domain space.

Using an iterated process, the humans in a community develop the category representation set, T*q, is defined for each category number q. 




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