Information Technology Reference
In-Depth Information
experiences/knowledge will have influence on the interpretation of the data. The same
piece of data may carry different meanings under different contexts (knowledge).
Knowledge is our prior experience. In other words, knowledge is the accumulated
relationships and patterns that a person perceives among raw data. For example, if a layman
sees the blood glucose test result of 230 mg/dL, he may have no clue as what this means.
But for a trained doctor's eye, it means the person had the test is diabetic. The only
difference here is the pattern. In the doctor's mind, from his prior training, a series of
patterns such as:
Glucose level of 230 mg/dL -> diabetic
Diabetic -> risk of blindness
Diabetic -> risk of kidney failure
exist. On the other hand, there are no such patterns in the layman's mind. In essence,
knowledge is the factoring of patterns (this includes summarization, abstraction, and
crystallization of patterns). In the world of knowledge management in computer science, the
knowledge is accumulated and crystallized patterns and relationships; and the information
is the product of the interaction between data and knowledge. In other words, when
connecting the dots, you are producing information.
Wisdom is the highest form of deep patterns. Usually, we only attribute wisdom to
intelligent beings. Bellinger (2004) has the following description about wisdom:
“Wisdom arises when one understands the foundational principles responsible for the
patterns representing knowledge being what they are. And wisdom, even more so than
knowledge, tends to create its own context. I have a preference for referring to these
foundational principles as eternal truths, yet I find people have a tendency to be somewhat
uncomfortable with this labeling. These foundational principles are universal and
completely context independent. Of course, this last statement is sort of a redundant word
game, for if the principle was context dependent, then it couldn't be universally true now
could it?”
In this documentation, we will focus on data, information, and knowledge. We will leave
the topic of wisdom to philosophers. Particularly, we will deal with computer reasoning and
knowledge management using knowledge databases. Before presenting our methods for the
knowledge representation, reasoning, and knowledge management, we need to answer the
philosophical question: is there any difference between human reasoning and computer
reasoning? Our answer is “Yes.”
Computer reasoning and human reasoning are different. One of the biggest differences has
something to do with creative ideas. Often, we see someone with so called “killer ideas.”
“Killer ideas” refer to those ideas that are revolutionary, creative, and not conform to the
norms of the contemporary generation. For example, Sir Isaac Newton's law of gravity,
Albert Einstein's theory of relativity, and the idea of ten dimensional UNIVERSE are all
examples of killer ideas. How exactly these “killer ideas” are produced is still open for
debate. However, we do know computers are incapable of producing these ideas (at least for
the time being); because, we haven't seen any computer that can produce any meaningful
killer ideas yet. Thus, we conclude that computers reasoning and human reasoning are
