In my last post, I did not elaborate too much on rules sets that contain the logic to map from one space to another. These rules sets are interesting in that usually when you map from a complex multi-dimensional taxonomy space to a simpler domain specific taxonomy space it is a one way mapping. A good way to think of it is to think about how photography works. A camera has a lens that focuses an image of a three dimensional space onto a two dimensional piece of film. Needless to say, there is a loss of information when the camera takes a picture because the resulting image is just a single view of a three dimensional image. Can we recreate the three dimensional space from our two dimensional photo? Not really, though I have seen some software that guess. Nevertheless, we still love photography. I was just looking at my wedding pictures last night, and in a way photography gives us a clearer vision of our shared reality from an authorial viewpoint.Great portraits or landscapes captures a moment and gives it clarity.
Let's get back to our idea of rules sets (our taxonomy camera), and how they map from from a complex multi-dimensional taxonomy space to a simpler domain specific taxonomy space. We develop the simpler taxonomy to give us a perspective of a domain which gives us vision of clarity and simplicity. We use it to give an authorial view of certain business sectors in a way that our more general purpose taxonomy can not do.
For those with a mathematical bent, I can say that our rules sets are prioritized rules and the fact that we have rules with greater priority than other rules makes these rules sets one way, and collapse the information to a simpler view. If we ran our rules sets on companies classified using the complex taxonomy to get the simpler classification, and then ran the rules sets in reverse on the simple taxonomy to get the categorizations in the complex taxonomy, the original complex classification will not be the same as the derived categorizations.
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