Hi,
jvff wrote:While thinking about your method I understood the complexity. Mainly in the form of ambiguity. Also I think it could be better to define a three entry system, where we can create relationships. That way we "optimize" the two way system by providing sling-shots. Therefore A leads to B but AB also leads to C. Example: "cat is animal", "animal isn't plant", "elephant bigger than mouse".
That depends on what a "reference type" is.
For my original thinking, there were concepts (equals, less, split, create, etc) and a reference type was one of 6 different forms of a concept - 3 different temporal values (past tense, present tense and future tense) and opposites. For example, the "equals" concept becomes the reference types "was equals", "is equal", "will be equal", "wasn't equal", "isn't equal" and "won't be equal".
If a node is either an object (cat, elephant, fox, dog) or an attribute (animal, plant, red, lazy) then this seems to work well. For example, "cat" linked to "animal" with the "equals" reference type establishes that the cat is an animal.
It also seems to work well for queries - for example, a question like "did elephant create cat?" can be solved by checking if elephant and cat are linked by the "paste tense create" reference type or it's opposite ("elephant did create cat", "elephant didn't create cat"). If this fails then further information can be obtained by checking for links of the other "create" reference types ("elephant will create cat", "elephant won't create cat", "elephant is creating cat", "elephant isn't creating cat"). If this also fails you can check cat or elephant for links to other nodes with the create reference types ("I don't know, but elephant created cheese", "I don't know, but dog didn't create cat").
m wrote:It seems to have something to do with cache algorithms,huh?

Cache algorithms are a little different in that information is either in the cache or it's not - there's no "half remembered" state.
The human mind is a strange thing, in that it seems to be able to store an infinite amount of information. My theory is that the more stuff you try to learn the more you forget about what you already knew, but you only forget small details that aren't so noticeable.
For a database of linked nodes, you can keep track of which nodes are used least often and "forget" nodes and/or links to make space for more information. If the database allows nodes to refer to pictures, videos, sounds, etc, then you could also reduce the size of node using lossless and lossy compression. For a simple example, imagine a node for "dog" which includes a high quality picture of a dog and the sound a dog makes, where the picture and the sound could be compressed and reduced to a small low quality picture or sound (and the original picture and sound disposed of).
This is more like the human mind - I remember doing my laundry yesterday and even remember how many loads it was and details of which items I washed (not compressed yet), I remember doing my laundry a few weeks ago but not many of the details (partially compressed), but I can't actually remember doing my laundry before that (compressed into oblivion), except for once when I forgot I had a tap running and flooded the laundry (compressed a lot, but not compressed into oblivion because the information is referenced occasionally - I think that node is linked to the "don't forget the tap is on" node

).
Cheers,
Brendan