Markov Chains bookmark sorter REDUX

I have looked about the web as much as I can tolerate when there is no joy of discovering what I want. Keywords can be so dumb when the language uses word overloading for the same concept. What I will attempt to do in C C++ and python, is a bookmark tree that shows associations in a three of the dimensions of association at a time. It then has a list of the major categories ( sets ) that can be used as dimensions with an operator to allow union, complement, intersect and combine them so that it can do multiple operations in series on the sets and have an interface to automate the process of localizing on an interest. It must also have a way to take the words and determine by context which usage is in effect for words with multiple meanings. I suppose word(2) would be as well as any way to represent that it is the second most common contextual use.

I can use "dict" and the local dictionaries to automate the context determination. By looking up the word and parsing for the different meanings it can be associated to determine if the definition and context are closely related.

It should allow me to do something like search localization similar to how I recall distant memories. It was something to do with programming and had a name like a living creature where it used indents to represent separations and I think it had something to do with a comedy troupe whose name was like Monte Carlo or something like that. Did you mean "Python the computer scripting language", "Monty Python", "Python reptile" --- [Python(1)] Snake reptile. [Python(2)] Language ( #scripting ). [Python(3)] -Monty comedy troupe.

It seems that I could use it as a backend to search engines so that I can apply my intent to the data and not theirs. Python has good libraries for the web side of that. It seems reasonable that I could apply like / dislike weights to the results locally so that I don't keep retracing the last failure at some later date. It would be nice to have something that randomized to avoid pigeonholing my responses and acting in a somewhat superstitious way to the problem. If I just take the best of things so far it would continue to weight that higher without any real value. A continuous win for a specific context has no meaning unless I check the negative search space for a failure and view the results in relationship to the alternatives.


Automated Intelligence

Automated Intelligence
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