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

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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 592
Peter Marshall and Damian Gordon tackling KE, <strong>the</strong> underlying question is succinct and simple; “how can we effectively elicit knowledge from an expert?” (Cooke, 1994). 2.1 Issues in Knowledge Elicitation In adopting KE as <strong>the</strong> basis for <strong>the</strong> KA process, not only are <strong>the</strong> difficulties associated with KA (knowledge acquisition bottleneck) inherited but also additional issues specific to KE need to be addressed. The development <strong>of</strong> a knowledge base is problematic and requires considerable more time and effort than required in o<strong>the</strong>r forms <strong>of</strong> KA. In this sense, <strong>the</strong> KE process is an important costdetermining factor in <strong>the</strong> whole Knowledge Creation process (Martinez-Bejar et al. 1996). The selection and availability <strong>of</strong> expertise is one such issue. Whilst <strong>the</strong> use <strong>of</strong> a single expert might be sufficient in some domains, in o<strong>the</strong>rs it is not. Martinez-Bejar et al. (1996) use an example from <strong>the</strong> field <strong>of</strong> environmental planning task to illustrate this, whereby expertise, in areas such as botanical, zoology, ecology, economy, and forestry engineering, are essential in fully understanding <strong>the</strong> problem. Using a single domain expert with expertise in all areas seems highly unlikely. In dealing with human sources, ano<strong>the</strong>r concern is <strong>the</strong> difficulties in encouraging experts to participate in <strong>the</strong> KE process (Okafor and Osuagwu, 2006). Intrinsic fears associated with <strong>the</strong> loss <strong>of</strong> unique knowledge previously used to guarantee relevance, status or job security. The cost / high demand for expertise <strong>of</strong>ten prohibits <strong>the</strong>ir inclusion in KM related activities. Complexity is ano<strong>the</strong>r issue; <strong>the</strong> effort to elicit knowledge is high in terms <strong>of</strong> time and expertise (Martinez-Bejar et al. 1996). Once a Knowledge Elicitation activity is up and running, <strong>the</strong> task can be slow, inefficient, and frustrating for all participants. Stefik and Conway (1982) described <strong>the</strong> knowledge elicitation as a “necessary burden, carried out under protest so that one can get on with <strong>the</strong> study <strong>of</strong> cognitive processes in problem solving.” Okafor and Osuagwu (2006) identify problems in conducting KE sessions. Experts are inclined to present an unbalanced picture <strong>of</strong> <strong>the</strong>ir knowledge, particularly in explaining <strong>the</strong>ir own knowledge. They tend only to present <strong>the</strong> procedural steps taken to achieve a task as opposed to <strong>the</strong> actual problem solving strategies employed in decision making. In respect to working with a knowledge engineer, Wilson and Holloway (2000) raised several important issues. Experts can feel <strong>of</strong>ten nervous or intolerant <strong>of</strong> <strong>the</strong> process. This usually happens when <strong>the</strong>y feel handicapped or insecure in giving <strong>the</strong> knowledge. Problems in <strong>the</strong> use <strong>of</strong> terminology can occur between an expert and a knowledge engineer occurs; that leads to confusion. A problem with most elicitation techniques is that in addition to capturing valuable knowledge <strong>the</strong>re is also a certain amount which is irrelevant with respect to <strong>the</strong> goals <strong>of</strong> <strong>the</strong> KE session. The development <strong>of</strong> multiple techniques and methodologies in <strong>the</strong> KE field has provided a basis for confusion. In selecting appropriate techniques for KE, Cooke (1994) stated “no technique is guaranteed to result in a complete and accurate representation <strong>of</strong> an expert's knowledge”. Selecting <strong>the</strong> right techniques is an empirical and <strong>of</strong>ten iterative process. Techniques differ in terms <strong>of</strong> <strong>the</strong>ir procedures, as well as <strong>the</strong>ir emphasis on one type <strong>of</strong> knowledge over ano<strong>the</strong>r. In selecting appropriate techniques, a knowledge engineer must be aware <strong>of</strong> techniques used in similar or related fields. In new domains, whereby previous case studies do not exist, <strong>the</strong> selection process is complicated. From <strong>the</strong> literature, we see that techniques are almost always used in collaboration with o<strong>the</strong>rs, thus adding to complexity <strong>of</strong> technique selection. Ano<strong>the</strong>r consideration when choosing a KE technique is <strong>the</strong> use <strong>of</strong> a single expert or multiple experts. Using techniques such as interviewing in <strong>the</strong> knowledge elicitation <strong>of</strong> multiple experts can exponentially increase <strong>the</strong> cost to perform <strong>the</strong> KE activity. Ano<strong>the</strong>r factor that will impact <strong>the</strong> process is <strong>the</strong> ability <strong>of</strong> an expert to articulate <strong>the</strong>ir knowledge as a clear set <strong>of</strong> modifiable facts, objects and rules, and give reasons for each decision. 2.2 Requirements for Knowledge Elicitation From <strong>the</strong> outset <strong>of</strong> a KE program, identifying relevant knowledge sources is key its success. Hanes and Gross (2002) noted that experts used in <strong>the</strong> process should ei<strong>the</strong>r be recognised as an expert in <strong>the</strong> field and possess expertise in handling rare or infrequent events. As a means to encourage expert participation, Slagle and Wick (1988) identified several key factors. Management participation is essential as <strong>the</strong>y are responsible for creating a cultural change management program required to aid all KM initiatives. Management must make <strong>the</strong> experts aware <strong>of</strong> <strong>the</strong> importance <strong>the</strong>ir contribution to <strong>the</strong> KE process. Organisations can also encourage experts to participate through financial and statutory pay<strong>of</strong>fs. In terms <strong>of</strong> <strong>the</strong>ir usage in <strong>the</strong> process, <strong>the</strong>y must provide assurances that <strong>the</strong> time allocated to each elicitation session is kept to a minimum. In order to reduce <strong>the</strong> complexity <strong>of</strong> <strong>the</strong> 593
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Proceedings <stron
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Contents Title Author(s) Picture <s
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Title Author(s) Picture of<
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Volume Two The Influence of
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The Symbolic Innovation of<
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Biographies of <st
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Jan Pawlowski works as Prof
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in academia. He holds the</
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Biomedical Engineering Research Cen
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Mathematics and Ph
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Thi Hai Hang Nguyen is a lecturer a
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Dan Savescu graduate TCM Faculty, p
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Teaches Business Computing and Stat
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Abdallah Al-Shawabkeh, Alexander K<
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Table 1: Inn-KM approach contents 2
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Knowledge Dynamics and Organisation
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Eckhard Ammann We distinguish three
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Eckhard Ammann quality dimension ar
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Eckhard Ammann The second cycle, Cy
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Figure 7: Supervised learning-by-do
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Identifying and Ranking the
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Manouchehr Ansari et al users <stro
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2.5 leadership and strategy Manouch
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Manouchehr Ansari et al conducted s
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Serious Games in the</stron
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Albena Antonova and Anandasivakumar
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Albena Antonova and Aniko Csepregi
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Seyed Esmaeil Asgharpour and Gholam
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Attitude of Senior
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4.5 Comparative Advantage Seyed Esm
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Contextual Adaptive Visualization E
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Xiaoyan Bai, David White and David
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Simona-Clara Bârsan, Mihaela-Georg
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2.4 Rights transfer Simona-Clara B
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A Framework for the</strong
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Leila Beig et al Additionally, Wei
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Leila Beig et al The proposed KM au
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Leila Beig et al source context Ele
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Leila Beig et al Figure 1: Learning
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Leila Beig et al Figure 3 visually
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Leila Beig et al based on collectiv
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Experiential Knowledge Creation Pro
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Didiosky Benítez et al Figure 1: E
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Didiosky Benítez et al meanings ac
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Didiosky Benítez et al In previous
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Didiosky Benítez et al Academic p
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Pavel Bogolyubov Figure 1: number <
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4. Results and discussion Pavel Bog
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Pavel Bogolyubov Knowledge Manageme
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Pavel Bogolyubov Table 3: Countries
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Pavel Bogolyubov McNamee, R. C., N.
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Ettore Bolisani, Francesca Gambarot
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Table 2: Synthesis
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Ettore Bolisani, Marco Paiola and E
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Strategies for Increasing Knowledge
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Constantin Bratianu, Adriana Agapie
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Klaus Bredl, Amrei Groß and Jane F
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Camelia Burja and Vasile Burja Appr
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∀ , i ≥ 0 v u i where: ui repre
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Camelia Burja and Vasile Burja Tabl
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Camelia Burja and Vasile Burja info
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Camelia Burja and Vasile Burja Jack
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Adriana Schiopoiu Burlea With respe
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Adriana Schiopoiu Burlea 3. ESF rol
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Adriana Schiopoiu Burlea In <strong
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3.2 Discussions Adriana Schiopoiu B
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Adriana Schiopoiu Burlea Nonaka, I.
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Simon Cadez and Vlado Dimovski with
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Simon Cadez and Vlado Dimovski scho
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Simon Cadez and Vlado Dimovski It i
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Why Should I Share my new ideas? Cu
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Francesco Calza, Rossella Canestrin
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José Manuel Cardenas and Mauro Spi
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Vincenzo Cavaliere and Daria Sarti
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National Knowledge Management Strat
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Behiye Çavuşoğlu and Mustafa Sag
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Implementing a work-life balance cu
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Juan-Gabriel Cegarra-Navarro, Mª E
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The Influence of R
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Huei-Fang Chen and Yi-Wen Lin outco
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3.2.4 Knowledge-sharing Huei-Fang C
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Huei-Fang Chen and Yi-Wen Lin that
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Huei-Fang Chen and Yi-Wen Lin Hair,
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2.1.2 Social capital Marguerite Cro
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Marguerite Cronk 2.3 Social capital
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Marguerite Cronk Figure 4: The rela
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An Exploratory Study of</st
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Françoise de Viron, Thomas Lederer
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4.1.2 The researchers Françoise de
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The Application of
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Nasser Easa and Robin Fincham 3. Th
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Nasser Easa and Robin Fincham knowl
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Nasser Easa and Robin Fincham <stro
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Network Management as a way to Mana
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Eva Eckenhofer An
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Eva Eckenhofer par
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Initiation Configuration Interessem
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Eva Eckenhofer Eve
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The Essence of Kno
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Emmanuel Innocents Edoun and Valden
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4. Case study and analysis Emmanuel
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2. The PUS-project Anandasivakumar
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Anandasivakumar Ekambaram and Agnar
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Jamal El-Den and Xin Zhou This rese
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Jamal El-Den and Xin Zhou 5. After
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Jamal El-Den and Xin Zhou includes
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Jamal El-Den and Xin Zhou Boyce, C.
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Tiit Elenurm development visions. C
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Tiit Elenurm companies the<
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Tiit Elenurm their
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References Tiit Elenurm Accorsi, F.
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Isaac Enakimio and Abdallah Al-Shaw
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A proposed Framework for Discoverin
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2.3 Knowledge mapping Mohamad Ali F
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Mohamad Ali Feyz, Babak Akhgar and
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Mohamad Ali Feyz, Babak Akhgar and
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Knowledge Workers: A Typology Frame
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Jiří Franek and Eva Grublova crea
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Jiří Franek and Eva Grublova Know
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Jiří Franek and Eva Grublova memb
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Middle Managers’ Maturity <strong
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Zoltán Gaál et al investigated m
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Zoltán Gaál et al Table 2: Total
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Zoltán Gaál et al characterised b
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Zoltán Gaál et al Matzler, K., Re
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Charles Gagné et al This paper pre
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Charles Gagné et al attributable t
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Charles Gagné et al intermediary d
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Adoption of Knowle
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1.1.2 SMEs and KMS Tendayi Gondo an
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Tendayi Gondo and Edmore Kori ambit
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Tendayi Gondo and Edmore Kori <stro
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Tendayi Gondo and Edmore Kori demon
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The Chain Value Process and Knowled
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Manel González-Piñero et al Captu
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4. What a biofeedb
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Manel González-Piñero et al The p
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References Manel González-Piñero
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Nebojsa Graca and Ana Lucija Gojako
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creating new, added, essential qual
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Norbert Gronau et al Those processe
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Norbert Gronau et al systematic mod
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Instance Schema Figure 4: Cyclic tr
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Norbert Gronau et al 4.2 Feasibilit
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Receive suggestions Compare suggest
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The Not-Invented-Here Syndrome in A
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David Grosse Kathoefer and Jens Lek
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Opinion of colleag
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Appendix David Grosse Kathoefer and
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Individual Level Influencers on Tac
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Claire Gubbins et al that such know
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Claire Gubbins et al 3.2 Individual
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Claire Gubbins et al “my thing wi
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Claire Gubbins et al Cabrera, A., C
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Oscar Guerra and Janeth Rojas Accor
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Oscar Guerra and Janeth Rojas Figur
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Why does knowledge transfer process
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3.7 Monitoring and control Oscar Gu
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Understanding Personal Knowledge De
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Markus Haag and Yanqing Duan In ord
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Markus Haag and Yanqing Duan 4.2 In
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Markus Haag and Yanqing Duan have a
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Chaordic Knowledge Management - Shi
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Frank Habermann, Jörg Fehlinger an
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Intellectual Capital in Universitie
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Meliha Handzic and Kursad Ozlen rep
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Table 3: Opinions about Human Capit
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Meliha Handzic and Kursad Ozlen How
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Developing a Knowledge Strategy Usi
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Harold Harlow knowledge to create c
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3.1 TKI Regression results Harold H
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Harold Harlow of <
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Understanding the
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3.1 Acquire Ciara Heavin and Freder
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Table 3: Distribution of</s
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Ciara Heavin and Frederic Adam Trav
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Ciara Heavin and Frederic Adam Tabl
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Ciara Heavin and Frederic Adam prov
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Key Knowledge Sharing Points: Explo
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Tore Hoel and Jan Pawlowski identif
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Tore Hoel and Jan Pawlowski The arc
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4.2.1 Discussion - Case 2 Tore Hoel
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Using Web 2.0 Technologies to Suppo
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Marta Infante Abreu, Florian Mat<st
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Pamela Chidiogo Izunwanne 2. Knowle
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Pamela Chidiogo Izunwanne use <stro
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Pamela Chidiogo Izunwanne 5.3 Organ
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Pamela Chidiogo Izunwanne Davenport
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Thomas Janke room for communication
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Thomas Janke 4. Reference implement
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Thomas Janke The rule states that w
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Thomas Janke Softw
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Towards a Detailed View on
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Vincent de Jong and Remko Helms Fig
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4.1 Case study setup Vincent de Jon
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Vincent de Jong and Remko Helms The
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Vincent de Jong and Remko Helms mak
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Vincent de Jong and Remko Helms Man
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Claudia Jooß et al at RWTH Aachen
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Claudia Jooß et al Regarding <stro
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Claudia Jooß et al learning (cf. B
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Expert Knowledge: Does it Help or H
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Selvi Kannan are people who have ex
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Selvi Kannan Amabile (1983) states
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Selvi Kannan American Task Force, R
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Integration of Kno
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Eva-Maria Kern et al Figure 1: Rela
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Eva-Maria Kern et al identification
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Eva-Maria Kern et al be gat
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Eva-Maria Kern et al For visualizin
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Knowledge Management Practices (KMP
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Radwan Kharabsheh, Ihab Magableh an
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Knowledge Management Across <strong
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Aino Kianto, Tatiana Andreeva and X
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5. Conclusions Aino Kianto, Tatiana
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Facilitating Knowledge Sharing in V
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Andrea Kő, Péter Fehér and Krisz
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