Proceedings of the 3rd European Conference on Intellectual Capital

Eliciting Tacit Knowledge From a Domain <strong>of</strong> Physical Skill
Peter Marshall and Damian Gordon
Dublin Institute <strong>of</strong> Technology, Ireland
pjmarshall@hotmail.com
damian.gordon@dit.ie
Abstract: Knowledge Acquisition (KA) can be seen as a form <strong>of</strong> requirements ga<strong>the</strong>ring where a process is
undertaken to ensure that <strong>the</strong> knowledge engineer fully captures <strong>the</strong> s<strong>of</strong>tware requirements <strong>of</strong> customers before
<strong>the</strong> design process commences. It involves a range <strong>of</strong> approaches that enable knowledge engineers to capture
<strong>the</strong> key knowledge that exists within <strong>the</strong> customers and <strong>the</strong>ir organisation (including <strong>the</strong> processes, procedures,
and documentation). Whilst <strong>the</strong> use <strong>of</strong> automated methods can produce great insights, <strong>the</strong>se techniques are
limited to organisations where a high percentage <strong>of</strong> its organisational knowledge is codified electronically.
Despite <strong>the</strong> shift towards a digital infrastructure, most <strong>of</strong> an organisation‟s knowledge still resides tacitly in <strong>the</strong>
minds <strong>of</strong> its members. To fully exploit this knowledge <strong>the</strong> field <strong>of</strong> Knowledge Elicitation (KE) has been established
to facilitate <strong>the</strong> acquisition <strong>of</strong> knowledge from human sources. A limitation <strong>of</strong> <strong>the</strong> current literature is that <strong>the</strong>
techniques focus primarily on capturing knowledge associated with skills at a cognitive level whereas relatively
little research has been performed in capturing knowledge found in physical activities. In organisations, where
physical tasks are part <strong>of</strong> <strong>the</strong> daily routine, research into acquiring and being able to manage <strong>the</strong>se manual skills
is <strong>of</strong> great importance. This research aims to help bridge <strong>the</strong> gap by applying and modifying existing KE
techniques, traditionally used to acquire knowledge in cognitive tasks, to <strong>the</strong> acquisition <strong>of</strong> knowledge and skills
found in physical tasks. For <strong>the</strong> purpose <strong>of</strong> <strong>the</strong> research, KE techniques were applied to <strong>the</strong> acquisition <strong>of</strong>
knowledge found in <strong>the</strong> performance <strong>of</strong> Mixed Martial Arts (MMA) skills. The experiments were conducted using
an expert trainer in <strong>the</strong> field. A wide cross-section <strong>of</strong> KE techniques were selected in <strong>the</strong> elicitation process and
retrospectively analysed. From <strong>the</strong> analysis, <strong>the</strong> techniques were <strong>the</strong>n critically compared, from which,
techniques that supported <strong>the</strong> verbal articulation <strong>of</strong> procedural and strategic knowledge required to perform <strong>the</strong>se
martial art techniques, were identified. The results from <strong>the</strong> study not only have implications in <strong>the</strong> elicitation <strong>of</strong>
knowledge from <strong>the</strong> field <strong>of</strong> MMA but also in <strong>the</strong> acquisition <strong>of</strong> knowledge in o<strong>the</strong>r fields <strong>of</strong> physical endeavour.
Keywords: knowledge management, knowledge capture, knowledge acquisition, knowledge elicitation, mixed
martial arts
1. Background
One <strong>of</strong> <strong>the</strong> fundamental processes at <strong>the</strong> heart <strong>of</strong> Knowledge Management is Knowledge Acquisition
(KA). KA is defined as <strong>the</strong> process <strong>of</strong> acquiring knowledge from a problem domain. The techniques
used in KA allow knowledge to be collected from different knowledge sources which can be validated
and maintained (Cooke, 2003). These processes are focused on <strong>the</strong> collection, analysis, modelling
and validation <strong>of</strong> knowledge for knowledge engineering and knowledge management projects
(Gronau et al., 2005).
The task <strong>of</strong> performing <strong>the</strong> KA problem is certainly non trivial. The impediments associated with this
undertaking are commonly referred to as <strong>the</strong> Knowledge Acquisition Bottleneck. Wagner (2003)
classifies <strong>the</strong>se issues into three broad categories, narrow bandwidth (i.e. limited number <strong>of</strong> channels
in which knowledge can be acquired), acquisition latency (i.e. length <strong>of</strong> time taken from <strong>the</strong> creation <strong>of</strong>
knowledge to when knowledge is available and ready to be shared) and knowledge inaccuracies (i.e.
mistakes made in extraction <strong>of</strong> knowledge from knowledge sources). Given <strong>the</strong>se challenges, KA
consists <strong>of</strong> a set <strong>of</strong> processes that aim to address <strong>the</strong>se limitations by providing <strong>the</strong> knowledge
engineer with a set <strong>of</strong> light-weight, knowledge-level, easy-to-use techniques that allow <strong>the</strong> engineer to
quickly obtain knowledge from a range <strong>of</strong> sources, and cross-validate <strong>the</strong>m to ensure accuracy.
Turban and Aronson (1998) categorise KA techniques into three distinct groups. The first group are
known as <strong>the</strong> automated techniques. These methods use tools, such as data mining, neural networks,
fuzzy logic and genetic algorithms, to address problems associated with KA latency and KA accuracy
by reducing <strong>the</strong> time taken and expense incurred in <strong>the</strong> KA phase. Whilst <strong>the</strong> popularity <strong>of</strong> such
methods has increased significantly over <strong>the</strong> past 20 years, <strong>the</strong>se techniques are reliant on <strong>the</strong> quality
and availability <strong>of</strong> digital sources (e.g. organisational corpora, databases, etc). Ano<strong>the</strong>r fundamental
problem associated with <strong>the</strong>se techniques is <strong>the</strong> omission <strong>of</strong> human expertise from <strong>the</strong> modelling
process. A survey conducted in 2000 estimated that over 90 percent <strong>of</strong> organisational knowledge is
not codified but resides tacitly an organisations entities (Bonner, 2000). By eliminating <strong>the</strong>se sources
from <strong>the</strong> KA process, <strong>the</strong>y are decreasing <strong>the</strong> bandwidth significantly, impacting on <strong>the</strong> accuracy <strong>of</strong>
KA initiatives. To address this problem, Turban and Aronson (1998), identified a fur<strong>the</strong>r two types <strong>of</strong>
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