An entailment network is a dynamic attempt at creating a set of mutually reinforcing ideas. The network is composed of nodes and links. The current webpage you are reading is a node holding an idea in CE Stoicism's entailment net. Meaning can be found in both the text of a node, as well as in the way a specific node links to and is linked from other specific nodes. An entailment net's dynamism comes from a bootstrapping process, where people attempt to further develop the coherent idea set by differentiating and merging nodes. Theoretically certain links between nodes may be stronger than others, but in practice it is difficult to define link strength. We do not try to measure the strength of a link, however a link should only be proposed/created if the connection it represents is more than trivial.
In associative network models, memory is construed as a metaphorical network of cognitive concepts (e.g., objects, events and ideas) interconnected by links (or pathways) reflecting the strength of association between pairs of concepts.
Directed graphs governed by the “bootstrapping axiom”, determining which concepts are to be distinguished or merged. Entailment nets are then generalized to associative networks with weighted links.
In the present “bootstrapping” perspective, there is no overall direction or sequence leading from inputs to outputs; there are only nodes linked to each other by associations, in such a way that they are coherent with each other and with the user's understanding of the knowledge domain.
The very weak requirement of “associativity” allows virtually any pair of concepts to be linked, if only with a very small link strength. According to the bootstrapping axiom, if everything is linked to everything, then all nodes become uniformly indistinguishable. However, there is a simple way to generalize the bootstrapping axiom to associative networks by defining a similarity measure that takes into account continuous link strengths, not just the binary property of having a link (strength = 1) or not (strength = 0). High values of the similarity measure between two nodes could then be seen as indications of ambiguity, that may be resolved through merging, differentiating or integrating (clustering) the nodes.
...However, it is in practice impossible to let users realistically estimate a strength value for each of the huge number of possible links.
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