#codingexercise
Double GetAlternateOddNumberRangeSqRtSumOfSquares()(Double [] A)
{
if (A == null) return 0;
Return A.AlternateOddNumberRangeSqRtSumOfSquares();
}

We continue our discussion on Text Knowledge Mining. We were discussing reasoning. Complexity is one of the important aspects of the automatic inference in knowledge based systems. Systems may find a tradeoff between expressiveness of knowledge reasoning and complexity of reasoning. While this is true for both TDM and TKM, the TKM don't have as much difficulty. First, both TDM and TKM, are hard problems and there are exponential algorithms and many strategies for designing efficient mining algorithms. Second TKM is not intended to be exhaustive so it can limit the data sources.
Now let us look at how to assess the results. In data mining, there are two phases for evaluating the results. The first phase is one in which statistical significance of the results is assessed. The second phase is one in which there is subjective assessment for the novelty and usefulness of the patterns.
For TKM, we have the following:
The results are considered reliable if there is validity and reliability of the text. Second, inference procedures are applied.
The user decides whether the results are non-trivial.  fortunately the results are expected to be trivial.
The non-triviality of the results is evaluated by the expert user.
The novelty of the results are evaluated against the BK.
The usefulness is evaluated by the experts.
The authors conclude that TKM is a particular case of knowledge mining. Knowledge mining deals with knowledge while data mining deals with data in which the knowledge is implicit. The operations are therefore deductive and abductive inference.