“All men by nature desire to know. An indication of this is the delight we take in our senses; for even apart from their usefulness they are loved for themselves; and above all others the sense of sight. For not only with a view to action, but even when we are not going to do anything, we prefer sight to almost everything else. The reason is that this, most of all the senses, makes us know and brings to light many differences between things.”
– Aristotle (Metaphysics, 980a21)
We live in the Information Era, where knowledge is everywhere and everything. The proper management of knowledge, however, presupposes the existence of mechanisms for organising information, in order to match the appropriate pieces together.
When the Internet came along, it offered users the ability to access many different types of electronic data. Unfortunately, these data are still mostly uncategorised and the human mind simply cannot absorb and process the increasingly huge amount of information available. This is the reason why computers are utilised to gather and present these in a more streamlined manner, so that people are able to browse through the findings in a faster and more convenient way.
Researchers have supported that the basis of computing technology is data (Ackoff, 1989). Data are represented by symbols that need to be put into context to make sense. For example, the letter ‘A’ is a symbol that could either represent “the first letter of the English alphabet” or “the sixth note in the musical scale of C major” (Longman, 2009).
To move from data to information, we require a meaning that can help us translate the symbols into something useful and comprehensible. This meaning presents the relational connection between the data and the audience they are meant for, providing, in this way, answers to simple questions such as “Who?”, “Where?” or “What?”.
With the help of meaning providing a context, we receive information which is something useful. We can now relate the given information to other pieces of information and learn it, thus acquiring knowledge. With knowledge we manage to answer the “How?” question, which puts the information into action by applying it to a specific domain like, for example, “’A’ comes before ‘B’ in the alphabet”.
It has been suggested that an extra layer should be added to this scheme called understanding (Bellinger, Castro & Mills, 2004). This is based on the argument that knowledge is information we memorise but, after we understand the true nature of “How?” it works, we can then answer “Why?” it works. According to their theory, memorising “2 + 2 = 4” is knowledge but answering the question “1267 x 300” requires understanding of the underlying mechanisms.
Together, knowledge and understanding can eventually lead to wisdom. Wisdom is produced after using and applying knowledge and understanding to many different cases and then reflecting on the results. Now, in addition to the “Why?” question, we can also answer the “When?” question. Expecting or estimating a result or an outcome (i.e. the future) requires wisdom, which derives from knowledge. This is something a human mind learns to do as a person grows up and matures, but with computers it is different. […]
(Adapted excerpt from Gkoutzis, 2013)
Konstantinos Gkoutzis
Bibliography
Ackoff, R. L. (1989) ‘From Data to Wisdom’. Journal of Applied Systems Analysis, 16 (1). pp 3-9.
Bellinger, G., Castro, D. & Mills, A. (2004) ‘Data, Information, Knowledge, and Wisdom’. Available at: http://www.systems-thinking.org/dikw/dikw.htm.
Gkoutzis, K. (2013) ’A Semantic Web Based Search Engine with X3D Visualisation of Queries and Results’.
Longman (2009) ‘Dictionary of Contemporary English – A’. [Online]. Available at: http://www.ldoceonline.com/dictionary/A_1.
