Proceedings of the 12th European Conference on Knowledge ...

Peter Marshall and Damian Gordon
elimination <strong>of</strong> human resources is problematic. In order to address this, manual methods that employ
<strong>the</strong> use <strong>of</strong> human sources are required. Using manual methods, techniques are employed that
provide a richness <strong>of</strong> detail which cannot be achieved using automated or semi-automated methods.
However manual methods are slow, expensive and are <strong>of</strong>ten subject to bias especially when dealing
with a single expert. Multiple sources can be employed to increase <strong>the</strong> accuracy <strong>of</strong> <strong>the</strong> knowledge
acquired by eliminating bias.
Figure.2 Semi Automated Knowledge Acquisition
Figure 3: Manual Knowledge Acquisition
2. Knowledge elicitation
From <strong>the</strong> roadmap in section 3, KE can be defined as a form <strong>of</strong> KA that is concerned with <strong>the</strong> manual
acquisition <strong>of</strong> knowledge from human sources. Whilst KE is a relatively new field <strong>of</strong> endeavour, work
in <strong>the</strong> area can be traced back in history to eras such as <strong>the</strong> enlightenment period in which <strong>the</strong>
eighteenth century. This period is characterised by its great works in which reason was advocated as
<strong>the</strong> primary source and legitimacy for authority. Early works include <strong>the</strong> Encylopedie by Diderot (1751-
1772), in which he documented <strong>the</strong> skills involved in trade’s craft. The field <strong>of</strong> knowledge elicitation,
as we know it today, finds its roots in <strong>the</strong> era <strong>of</strong> Expert System development and <strong>the</strong> work <strong>of</strong> Edward
Feigenbaum (1971). Since <strong>the</strong>n <strong>the</strong> field has benefitted from fur<strong>the</strong>r research into fields such as
Cognitive Psychology (Lesgold, 1984; Means & Gott, 1988), Instructional Design, (Gagne & Smith,
1994) and Psychology <strong>of</strong> Expertise (Glaser, 1987). Where once <strong>the</strong> field was purely an academic
endeavour, <strong>the</strong> delineation between academia and industry has almost completely been dissolved. In
organisations, such as Skandia, <strong>the</strong> systematic use <strong>of</strong> KE techniques is engrained in its KM model
(Edvinsson,1997).
A criticism <strong>of</strong> <strong>the</strong> field lies in <strong>the</strong> hangover from <strong>the</strong> behaviourist movement where <strong>the</strong> idea that
routines and task, located tacitly in <strong>the</strong> minds <strong>of</strong> an expert, are inexpressible via introspection. This
notion has never been formally demonstrated (H<strong>of</strong>fman & Lintern, 2006). In principle, all tacit
knowledge can be elicited. It is <strong>the</strong> collaborative process between a knowledge engineer and an
expert as well as <strong>the</strong> quality <strong>of</strong> <strong>the</strong> techniques being used which provides a mechanism for its
discovery. Once conducted, <strong>the</strong> knowledge gained can be used in <strong>the</strong> creation <strong>of</strong> models pertaining to
a practitioner’s knowledge and reasoning within a domain <strong>of</strong> knowledge. The integration <strong>of</strong> individual
models, in <strong>the</strong> same domain, can help synergise ideas and present a collective model <strong>of</strong> <strong>the</strong> work
domain as a whole. From this, we can identify various components <strong>of</strong> knowledge (e.g. domain
concepts, best practices, etc.), and knowledge used in <strong>the</strong> creation <strong>of</strong> knowledge based products. In
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