Categories as ordering structures

Categories. Groups.  Hierarchies. Networks, of Categries.

These are the usual structures for information ordering.  See this article for the seed of the idea (a bit over half way down) http://idratherbewriting.com/2012/06/11/essay-my-journey-to-and-from-wikis-why-i-adopted-wikis-why-i-veered-away-from-them-and-a-new-model-for-collaboration/

The problem with all of the above is that no matter what the structure is, hierarchy, venn groups, directed or undirected graphs, networks and other half assed structure ideas; the problem is the links between the nodes.  They are represent a weight of "1".  Conceptual, semantic, relationship etc. 

It would be more interesting to make a connnection between each leaf node and all the structure nodes (markup, meta-data, semantic...) with a weighted relationship.  (Neural net anyone).

This gives the ability to generate word clouds, semantic networks, relevance calculations etc and more importantly, they can be pre-rendered and encoded into the structure on the fly.  They can also be updated locally or as fragments.

I read a couple of articles on big data punching at Facebook and Google yesterday and the ideas are still bubbling around.

* Seed with either a relationship between a leaf node and all stem nodes and prune or randomly seed relationships and then agressivly create new ones.  

* Build structural semantics via emergent naming systems.  This allows blind structure discovery wihout having to pre-name anything. 

* Describe the relationships between leaf and stem nodes with more complex relationships than "1".   Simple weight systems, direction, traversal stats, utility for purpose, repeat visits (if your google) time of traversal, local time of traversal, reversals ( back button on browser)   all sorts of interesting data.  You could even map traversal paths and draw some conclusions about eventual destinations.  This would turn a leaf node into some kind of high value interconnect even though its not an endpoint in itself. Kind of like traffic analysis looking for high value interesections and points of failure.  If your advertising, these seem like good places to build billboards.  Not only for simple eyeball count, but for eyeballs that are on their way to specific things... which perhaps even they don't yet know. 

* Continually evolve the relationships between the nodes (wandering dendrites?)  This could be establishing and testing paths, looking for new connections, dealing with dynamic content... various different strategies.

Hmm... ideas synthesizing...