We continue with our discussion on Text Knowledge Mining. We were discussing knowledge representation, Text mining requires to translate text into a computationally manageable intermediate form. This step is crucial and poses several challenges to TDM and TKM like obtaining intermediate forms, defining structures for information extraction, identifying semantic links between new concepts, relating different identifiers etc. A key problem with obtaining intermediate forms is that the current methods require human interaction. The objective of an intermediate form is not to represent every possible aspect of the semantic content of a text, but those related to the kind of inference we are interested in. That is why even if we are not able to fully analyze the text, we are only impacted with some missing pieces of knowledge. And TKM is not expected to be exhaustive in obtaining new knowledge. That said, if the analyzer obtains an inexact representation of text in an intermediate form, this can affect the knowledge discovered even so much as reporting false discoveries. The knowledge in a knowledge based system is assumed to be reliable and consistent but the same cannot be said to be true for a collection of text. This poses another challenge in TKM.
We now look at the role of background knowledge. The inclusion of background knowledge in text mining applications is widely recognized. Similarly in TKM, text does not contain common sense knowledge and specific domain knowledge that is necessary in order to perform TKM. As an example text containing A is father of B and B is father of A is not considered contradictory without background knowledge. This knowledge can contribute to TKM in the following ways: First, it allows us to create new knowledge that is not fully contained in the collection of text, but can be derived from a combination of text and background knowledge. This was a requirement of Intelligent text mining. Another interesting application of Background Knowledge is in the assessment of knowledge, in aspects like novelty and importance.
Reasoning and complexity are also important aspects of automatic inference in knowledge based systems.
#codingexercise

Double GetAlternateOddNumberRangeSumOfSquares()(Double [] A)


{


if (A == null) return 0;


Return A.AlternateOddNumberRangeVarianceSumOfSquares();


}