I didn’t understand everything completely, however when you mentioned “relevance realization” it reminded me of a recent post which gave labels to data which was of different levels of usefulness. The exact labels he used isn’t particularly important, but the categories that they represent are extremely useful. He outlines, more or less, that there are three types of information:
1. Unsorted, unfiltered data. Without a way to discriminate the signal from the noise, this data is basically useless. One example he gives are unsorted error logs for a given computer.
2. Highly relevant, well processed data. When the raw data is filtered, modified, and processed into a hyper usable form, that’s when it is it’s most useful. The ways to slice and dice the data are subjective in the sense that it depends on the goals of the user.
3. Misleading or incorrect data. Some data may be correct, but misleading. The example given is a ticket created because a “website loaded slowly”. Because this ticket was submitted, a lot of time may be taken to determine what may be wrong with a server. However, it turns out that the page loaded slowly because it was accessed via an old machine!
I wonder if the two of you might be interested in exploring these concepts together. Or apologies if I misunderstood!
I think I don’t have the correct background to understand fully. However, I think it makes a little more sense than when I originally read it.
An analogue to what you’re talking about (referential containment) with the medical knowledge would be something like PCA (principle component analysis) in genomics, right? Just at a much higher, autonomous level.