Content models

The jump form CI (Computational Intelligence) to Genetic Programming (GP), for an Information Architect, is somewhat exciting. Starting from the first steps till some intricacies of sieve building. When I approached it the assertion that among Living things a wrong if clause is an opportunity and not an error made me even more intrigued. So, even if not a geneticist, I've continued to follow mostly the theoretical side as a great source of inspiration for new content representational structures.
In this case I'm presenting here, and interesting set of consideration about data structures: hyper graphs. A serious study of content representations must consider not only the straight connections but functors and modifiers as well. In such a view the traditional graph representation (coming form the old semantic networks) introduced by topics (or tags) is somewhat static in its node to node jumps. See here. The soundness of the connection between biology and content analysis maybe traced back to Hegel and before English natural historians and scientists. For a study on the relation nature/culture in Hegel see here and here.