1. Clyde is an elephant
Lucardie
P
rior 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 of
Knowledge Values
(Larry.Lucardie@knowledge-values.com)