Clyde is an elephant

Lucardie

Prior to the establishment of Knowledge Values in 2003, I had conversations with people from various companies on the topic of the future X-internet: a

Web containing eXecutable intelligence. No more masses of consumers trailing the Web, but yet an Internet which informs people proactively and provides exactly what is required; based on smart connections. The lowest priced mortgage, a perfectly tailored mobile phone contract, or perhaps a partner to help you with the household chores.

The reality is that the Internet and Semantic Web such as de-scribed above are not yet at this stage. It is certain that intelli-gence will play a dominant part in defining the future Internet. However, drowning in information, but thirsty for knowledge is how the current situation can be defined. In order to create a smarter Internet we are going to have to organise knowledge. With the use of a scientific term this is called: ontological engineering.

One of the major obstacles on the path to an intelligent Web is the method we use to look at information. Too often we make a distinction between knowledge and data, but with an unjust emphasis on data. We are inclined to view data as ‘Clyde is an elephant’. However, if you subsequently learn that Clyde has just flown past the window and sleeps in a teacup you realise that a great deal of meaning is stored within data. Therefore it is not that simple to classify Clyde as an elephant; it re-quires deep insight in the meaning of the concept ‘elephant’. And without definitions, you cannot perceive in a cognitive manner. You can look at a mobile phone in its physical form, but cannot see it cognitively if you do not know what it is. Knowledge enables us to see. This is why a novice chess player sees the capture of a pawn, whereas an advances player sees a threatening checkmate. The data presented in the configura-tion of the chess board may be identical visually, but will be different for both players from a cognitive perspective. Data can only exist as a function of a definition and is connected inextricably with this definition. Furthermore there are no functionally separated parts in our brain for knowledge and data. Nevertheless, we prefer a division, and we focus on data

(objects) instead of the meaning of concepts (object types). This results in several problems created within, for example, business intelligence. Although we possess a massive level of data in this area, we are not able to produce accurate manage-ment report without adequate definitions of concepts.

When we store knowledge into a computer a reduction takes place: we reduce the infinite, richly discerned reality to models, manageable for the computer. What does not fit into these models ideally requires human intelligence. The other part can be automated by artificial intelli¬gence. Organisations often struggle with this part; putting concept-dependable data into a database without knowing the meaning of the concept. Defin-ing a concept is often unexpectedly complex. For example, the unfortunate Clyde, of whom we have just created an image, has a yellow trunk - a result of his imprisonment - and only three legs because of an accident. Concepts may have different meanings too. The concept of ‘income’ can mean something entirely different for the Public Employment Service than for the Tax Office; the concept of ‘student’ has a different meaning in the context of the Ministry of Education (logically reasoned, the meaning of a concept is a collection of conditions) com-pared to the context of a University.

In many cases bilateral relations between various meanings are absent - or subtle yet important differences occur. We are sur-rounded by databases which assume a certain level of homoge-neity within the objects, which, depending on the conceptual framework, is not always there. For this reason we must clearly record the definitions on a knowledge level, prior to making decisions on how we will present the knowledge including the data. In order to achieve an Internet which can apply concepts in a contextually smart way; making it eXecutable, the imple-mentation of these logical connections - ontological engineer-ing - will form our greatest challenge for the coming years.

IK, ninth volume, number 2, 2010 19

Prof. dr. Larry Lucardie is CEO ofKnowledge Values(Larry.Lucardie@knowledge-values.com)