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A significant part of knowledge and experience in an organization belongs not to the organization itself, but to the individuals it employs. Therefore, knowledge management (KM) tasks should include eliciting knowledge from knowledgeable individuals. The paper aims to argue that the current palette of methods proposed for this in KM discourse is limited by idealistic assumptions about the behavior of knowledge owners. This paper also aims to enrich the repertoire of methods that can be used in an organization to extract knowledge (both tacit and explicit) from its employees by bridging KM and knowledge engineering and its accomplishments in the knowledge elicitation field.

This paper is based on extensive literature review and 20 years of experience of one of the authors in applying various knowledge elicitation techniques in multiple companies and contexts.

The paper proposes that the special agent (analyst) might be needed to elicit knowledge from individuals (experts) in order to allow further knowledge sharing and knowledge creation. Based on this idea, the paper proposes a new classification of the knowledge elicitation techniques that highlights the role of analyst in the knowledge elicitation process.

The paper contributes to managerial practice by describing a systemic variety of knowledge elicitation techniques with direct recommendations of their feasibility in the KM context.

The paper contributes to a wider use of knowledge engineering methodologies and technologies by KM researchers and practitioners in organizations.

This paper aims to contribute towards understanding how safety knowledge can be elicited from railway experts for the purposes of supporting effective decision-making.

A consortium of safety experts from across the British railway industry is formed. Collaborative modelling of the knowledge domain is used as an approach to the elicitation of safety knowledge from experts. From this, a series of knowledge models is derived to inform decision-making. This is achieved by using Bayesian networks as a knowledge modelling scheme, underpinning a Safety Prognosis tool to serve meaningful prognostics information and visualise such information to predict safety violations.

Collaborative modelling of safety-critical knowledge is a valid approach to knowledge elicitation and its sharing across the railway industry. This approach overcomes some of the key limitations of existing approaches to knowledge elicitation. Such models become an effective tool for prediction of safety cases by using railway data. This is demonstrated using passenger–train interaction safety data.

This study contributes to practice in two main directions: by documenting an effective approach to knowledge elicitation and knowledge sharing, while also helping the transport industry to understand safety.

By supporting the railway industry in their efforts to understand safety, this research has the potential to benefit railway passengers, staff and communities in general, which is a priority for the transport sector.

This research applies a knowledge elicitation approach to understanding safety based on collaborative modelling, which is a novel approach in the context of transport.

The purpose of this paper is to develop a knowledge elicitation strategy to elicit and compile home energy retrofit knowledge that can be incorporated into the development of an intelligent decision support system to help increase the uptake of home energy retrofits. Major problems accounting for low adoption rates despite well-established benefits are: lack of information or information in unsuitable and usable format for decision making by homeowners. Despite the important role of expert knowledge in developing such systems, its elicitation has been fraught with challenges.

Using extensive literature review and a Delphi-dominated data collection technique, the relevant knowledge of 19 industry experts, selected based on previously developed determinants of expert knowledge and suitable for decision making was elicited and compiled. Boolean logic was used to model and represent such knowledge for use as an intelligent decision support system.

A combination of comprehensive knowledge elicitor training, Delphi technique, semi-structured interview, and job shadowing is a good elicitation strategy. It encourages experts to describe their knowledge in a natural way, relate to specific problems, and reduces bias. Relevant and consensus-based expert knowledge can be incorporated into the development of an intelligent decision support system.

The consensus-based and relevant expert knowledge can assist homeowners with decision making and industry practitioners and academia with corroboration and enhancement of existing knowledge. The strategy contributes to solving the knowledge elicitation challenge.

No previous study regarding a knowledge elicitation strategy for developing an intelligent decision support system for the energy retrofit industry exists.

The purpose of this paper is to present an automatic Medical Knowledge Elicitation System (MediKES), which is designed to improve elicitation and sharing of tacit knowledge acquired by physicians. The system leverages the clinical information stored in electronic medical record systems, by representing the acquired information in a series of knowledge maps.

The system architecture of the proposed MediKES is first discussed, and then a case study on an application of the proposed system in a Hong Kong medical organization is presented to illustrate the adoption process and highlight the benefits that can be realized from deployment of the MediKES.

The results of the case study show that the proposed solution is more reliable and powerful than traditional knowledge elicitation approaches in capturing physicians' tacit knowledge, transforming it into a machine‐readable form, as well as enhancing the quality of the medical judgment made by physicians.

A prototype system has been constructed and implemented on a trial basis in a medical organization. It has proven to be of benefit to healthcare professionals through its automatic functions in representing and visualizing physicians' diagnostic decisions.

Knowledge is key to improving the quality of the medical judgment of physicians. However, researchers and practitioners are still striving for more effective ways of capturing tacit knowledge and transforming it into a machine‐readable form so as to enhance knowledge sharing. In this paper, the authors reveal that the knowledge retrieval and the visual knowledge representation functions of the proposed system are able to facilitate knowledge sharing among physicians. Thus, junior physicians can use it as a decision support tool in making better diagnostic decisions.

After setting the stage by briefly surveying knowledge elicitation techniques, this article presents a description of an iterative approach to the elicitation and representation of organizational knowledge called PreSERVe, which stands for prepare, scope, elicit, render, and verify. The method involves an initial process of preparing for knowledge elicitation, followed by an iterative process of assessing the scope of the endeavor, knowledge elicitation and rendering, and, verification. Use of the PreSERVe method is illustrated by a case study involving work with six senior engineers at NASA Glenn Research Center (NASA GRC), Cleveland, OH, USA.

This paper presents a recommendation on how one requirement elicitation technique supports the other techniques in defining system requirement for a case-based system. A case-based lesson planning system aims to assist teachers in constructing quality lesson plans through its cycle which begins with case retrieval. To retrieve relevant lesson plans, appropriate inputs should be used and the intended output needs to be identified via suitable requirement elicitation techniques. The use of a single technique might result in inadequate requirement specification, thus affecting the quality of the output requirements as well as quality of the final information system.

Requirement elicitation was carried out in three phases: phase I involved document review, phase II was an interview and phase III used a survey. Respondents of the study comprised experienced teachers as well as new teachers. This research used both qualitative and quantitative approaches to answer the research questions, which involved semi-structured interviews, document review and survey to collect the relevant data. Documents were reviewed by analysing lesson plans from three different countries. In addition, a review of lesson plans prepared by teachers and the standard syllabus were carried out. Findings from the document review were used in structured interviews using a teach-back technique, sorting and matrix of attribute-values. A questionnaire was then constructed based on the interviews and document review.

The findings of this initial study, as part of a larger research investigation, would help in knowledge modelling and representation. This will contribute to effective case retrieval via good design of the system input and output. The study identifies important elements of a lesson plan according to their ranking. Keywords that were used by teachers as input for retrieval were identified together with the expected output.

The main goal of requirement elicitation is to specify complete and detailed requirements of the proposed system. There are two main types of requirement: functional and non-functional requirements. This paper only focuses on functional requirements – specifically case retrieval with appropriate input and output.

Various requirement engineering (RE) techniques can be applied in different phases of requirement elicitation. Suitable technique should be chosen at different phases of RE, as it is important for triangulation purposes. Incomplete RE will affect the modelling part of system development, and, thus, affect the design and implementation of an information system.

Software engineer or anybody involved in system development should plan accordingly for the RE process. They should be creative and reasonable in selecting suitable RE techniques to be applied.

This study aims to gain understanding of the various aspects of lesson planning. Crucial knowledge in lesson planning that was gathered from the elicitation phase is modelled to have a good understanding of the problems and constraints among teachers. The findings of this initial study, as part of a larger research investigation, would help in knowledge modelling and representation. This will contribute to effective case retrieval via a good design of the system input and output.

The purpose of this paper was to build the MACTAK methodology, which aims to transform collective tacit knowledge into the explicit one using knowledge elicitation techniques, associated to quality tools structured by systemography, represent it in a symbolic language and production rules and model it in two expert systems which can assist the investigation of defect causes during the metal packaging production process.

The method applied in the research was classified as exploratory, because a preliminary study was conducted, to better suit the mapping methodology for eliciting collective tacit knowledge, to the reality which was intended to be known. Through studies and the application of the systemography technique, a methodology for the elicitation of collective tacit knowledge has been developed. It suggests a systematic sequence of activities, to map and transform collective tacit knowledge into the explicit one, on the production process which was studied.

The types of tacit knowledge were mapped and became explicit through the application of the methodology proposed. A knowledge management system was created, as such knowledge was validated by other mechanics during their training on the shop-floor, which resulted in a structure of unique and shared knowledge. They became explicit for being stored in a knowledge base and presented to its users through the expert system. It is concluded that the methodology of acquiring collective tacit knowledge helped on the reduction of rework index by standardizing the way used to investigate the cause of the defect, in the studied company.

The MACTAK methodology was developed for exclusive use in industrial processes where the following elements are presented: process, method, environment, raw materials, labor work, measurement and machine. In this method, the detection of the problem occurs from statistical data.

The methodology began in August 2010, and in October 2011, obtained as a result a reduction in rework cost equivalent to US$17,780.95.

The methodology is unique, as it refers to the systematic use of knowledge acquisition techniques and tools of quality, and the methodology has a characteristic of direct application in manufacturing processes. The beneficiaries, in this case, are mechanicals of production and quality inspectors that work at the operation level in the company. For the organizational and tactical level, the beneficiaries are engineers of production.

The purpose of this paper is to propose an effective knowledge elicitation method and representation scheme that empowers humanitarian assistance/disaster relief (HA/DR) analysts and experts to create analytic models without the aid of data scientists and methodologists while addressing the issues of complexity, collaboration, and emerging technology across a diverse global network of HA/DR organizations.

The paper used a mixed-methods research approach, with qualitative research and analysis to select the model elicitation method, followed by quantitative data collection and evaluation to test the representation scheme. A simplified analytic modeling approach was created based on emerging activity-based intelligence (ABI) analytic methods.

Using open source data on the Syrian humanitarian crisis as the reference mission, ABI analytic models were proven capable in modeling HA/DR scenarios of physical systems, nonphysical systems, and thinking.

As a data-agnostic approach to develop object and network knowledge, ABI aligns with the objectives of modeling within multiple HA/DR organizations.

Using an analytic method as the basis for model creation allows for immediate adoption by analysts and removes the need for data scientists and methodologists in the elicitation phase. Applying this highly effective cross-domain ABI data fusion technique should also supplant the accuracy weaknesses created by traditional simplified analytic models.

Describes how, in ill‐defined, emerging situations, team members struggle to make sense of the situation, react to stimuli from the external environment, and interact with each other and human artefacts to develop an interpretation of the environment. Presents a general model of this process, lessons derived from experiences in trying to support it, and issues for future development.

Experienced fire ground commanders are known to make decisions in time-pressured and dynamic environments. The purpose of this paper is to report some of the tacit knowledge and skills expert firefighters use in performing complex fire ground tasks.

This study utilized a structured knowledge elicitation tool, known as the critical decision method (CDM), to elicit expert knowledge. Totally, 17 experienced firefighters were interviewed in-depth using a semi-structured CDM interview protocol. The CDM protocol was analysed using the emergent themes analysis approach.

Findings from the CDM protocol reveal both the salient cues sought, which the authors termed critical cue inventory (CCI), and the goals pursued by the fire ground commanders at each decision point. The CCI is categorized into five classes based on the type of information each cue generates to the incident commanders.

Since the CDM is a useful tool for identifying training needs, this study discussed the practical implications for transferring experts’ knowledge to novice firefighters.

Although many authors recognize that experts perform exceptionally well in their domains of practice, the difficulty still lies in getting a structured method for unmasking experts’ tacit knowledge. This paper is therefore relevant as it presents useful findings following a naturalistic knowledge elicitation study that was conducted across different fire stations in the UK and Nigeria.