#codingexercise
Double GetAlternateOddNumberRangeStdDev()(Double [] A)

{

if (A == null) return 0;

Return A.AlternateOddNumberRangeStdDev();

}

We continue our discussion on Text Knowledge Mining. We discussed the algorithm for finding contradictions. Looking for contradictions is very useful. It helps with the consistency of text collection, or to assess the validity of a new text  to be incorporated, in terms of the knowledge already contained in the collection. In addition, we can use it to group texts such as when we take a  collection of papers expressing opinions about topics where opinions are in different groups. Finally it also lowers the overhead of reasoning with ontologies because we can now instead check the consistency by way of non-contradictions. This check can now become a preliminary requirement for reasoning.
We also looked at the challenges of TKM. Similar to the case in data mining, there are many existing techniques that can be applied but they have to be adapted. This may not always be easy. In addition, some areas require new research. Existing techniques could benefit areas like knowledge representation, reasoning algorithms for performing deductive and abductive inference and knowledge based systems. These are also applicable to natural language processing.
There are several differences between knowledge based systems and TKM
First, knowledge based system is built to contain as much knowledge as possible for a specific purpose. TKM treats them as reports and does not care for any one particular except that they are dedicated information collection.
Second, a knowledge based system tries to answer all the questions whereas a TKM assumes there is no such knowledge pieces and finds new hypothesis.
Third a knowledge based system does reasoning as part of a query processing. TKM does it to find new knowledge without specifying a query, though it can also choose to.
We also look at knowledge representation. Text mining requires to translate text into a computationally manageable intermediate form. This step of text mining is crucial and poses several challenges common to TDM and TKM.  A key problem for obtaining intermediate forms for TKM is that the currently used techniques for translating texts to intermediate forms are mainly semi-automatic involving human interaction. On the other hand, many domains are trying to express knowledge representation models directly. In SemanticWeb for example, not only ontologies are used to represent knowledge but also efficient deductive inference techniques like graph based search are also available.

#codingexercise

Double GetAlternateOddNumberRangeVariance()(Double [] A)


{


if (A == null) return 0;


Return A.AlternateOddNumberRangeVariance();


}