Knowledge representation Concepts part 1

What is knowledge? In our colloquial thinking, it''s something that "we know" , mostly information.

For computers, this might not be as easy as it seems. Knowledge can be split in some categories:

- Common sense knowledge: We know that if we throw a rock on a window, it will break. Can a computer actually know this if we tell him about the window and the rock? Does the computer realize that the rock will eventually fall down to the earth ? In common sense, we have a lot of knowledge that we take for granted, like physics laws, which may not be as intuitive for computers as they are for us. To give a computer a "common sense knowledge" implies a lot of factors that we may or may not be aware of. In computer decision process, minor things can be of extreme importance.

- Logic knowledge: We know that if we add one apple to another apple we obtain two apples. This is quite logical and it''s appliance can be extended for various problems and situations. This is also fair to implement in a computer knowledge database.

Computers cannot have a full set of common sense knowledge. How can we implement a basic system that can be extended so that computer share a bit of our understanding? One idea is too use a rule-based system.

If thrown(rock, window) then break(window)

Actions, predicates, simple logic can be useful in such situations.

Objects can be implemented into classes, actions can move from one state to another.

However, knowledge representation is a very important part in having a basic start for creating an artificial intelligence and designing a system that will respond fast to stimuli and give an adequate response as well.

Data mining Concepts part 1

What is Data Mining?

First of all what is Data?

In a rudimentary conception could be organized information. In the back end, it''s just some bits. Lot of bits.

Information and Data have the same meaning ? Perhaps not.

Searching the web has the purpose of finding data or information? When searching the web with a regular search engine, for example Google, you only look for keywords, either as meta data or as words throughout other documents. Google does not interpret the meaning of your words. Relations and associations are not being done. Data mining would look something like "If somebody in a supermarket buys beer and diapers... what are the odds that he would also buy chips ?"

Data mining is the process of finding out new information from existing information, by using mathematical, statistical, data bases techniques that combined can give out new information that was not present.

"If somebody buys beer and wine, how risky is for that person to have a heart attack? Is it profitable to give to such a man life insurance?" Examples can continue.

Data mining is a complex process that can give us knowledge from what we already have. What do we have to do before we can apply data mining ? Usually databases and information sets are noisy, missing, redundant or perhaps too big. What can we do to make our data easy to "mine" and to give us the best results that we can obtain ? Databases cleansing techniques can be applied at this level : sampling, noise reduction, even machine learning algorithms can be applied to show out missing attributes from our information tables. For example one can use a decision tree like ID3

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