Knowledge Elicitation Process Research Articles

A participatory approach to model the neighbourhood food environment.

Inequalities in exposure to a health-promoting local food environment have been implicated in the generation of inequalities in diet-related behaviours and outcomes, including obesity and diabetes. Increasingly, poor diet and diet-related disease have been characterised as an emergent property of a complex system and, as such, the drivers of poor diet may be better understood by using a complex system perspective. In this study, we describe a participatory approach for understanding the system drivers of unhealthy food consumption. System dynamics (SD) was used to identify, understand, and visualise the elements of the neighbourhood food retail system that influence individuals' eating behaviour. Group Model Building (GMB), undertaken online with stakeholders (n = 11), was used to funnel existing knowledge and evidence on urban food environments and to build a conceptual system map of the local food retail environment inclusive of the drivers that influence the decision to purchase and consume meals that are high in fat, salt, and sugar (HFSS), and calories. The GMB was organised as a knowledge elicitation process involving a questionnaire, a workbook, and a structured workshop. The GMB generated a comprehensive causal loop diagram (CLD) of the retail environment inclusive of the drivers that influence the decision to purchase and consume unhealthy meals. The CLD was designed around two main variables (i) exposure to food outlets and (ii) food consumption. The system map built during the Group Model Building session linked exposure to food outlets with the possibility to purchase and consume unhealthy meals. The effectiveness of this link will be tested in an Agent-Based model. The conceptual model illustrates the complexity of the factors responsible for inequalities in unhealthy eating. The GMB approach provides a basis for building an agent-based model for local authorities to characterise their food retail environment, uncover potential leverage points for interventions and test them 'in silico' in a virtual environment.

Development of a Knowledge-Based Expert System for Diagnosing Post-Harvest Diseases of Apple

Post-harvest diseases are one of the main causes of economical losses in the apple fruit production sector. Therefore, this paper presents an application of a knowledge-based expert system to diagnose post-harvest diseases of apple. Specifically, we detail the process of domain knowledge elicitation for constructing a Bayesian network reasoning system. We describe the developed expert system, dubbed BN-DSSApple, and the diagnostic mechanism given the evidence provided by the user, as well as a likelihood evidence method, learned from the estimated consensus of users’ and expert’s interactions, to effectively transfer the performance of the model to different cohorts of users. Finally, we detail a novel technique for explaining the provided diagnosis, thus increasing the trust in the system. We evaluate BN-DSSApple with three different types of user studies, involving real diseased apples, where the ground truth of the target instances was established by microbiological and DNA analysis. The experiments demonstrate the performance differences in the knowledge-based reasoning mechanism due to heterogeneous users interacting with the system under various conditions and the capability of the likelihood-based method to improve the diagnostic performance in different environments.

Parameterisation of domain knowledge for rapid and iterative prototyping of knowledge-based systems

In critical infrastructure applications, timely and consistent fault detection and diagnosis is an increasingly important operational process, especially in the energy sector where safety is of the utmost importance. To realise this, engineers have to manually analyse data acquired from several assets using predefined diagnostic processes, but this is a time-consuming process requiring significant amounts of specialist expert knowledge. Data-driven approaches to support fault detection and diagnosis, and other similar problems, can produce accurate results comparable to what the engineers can achieve in a fraction of the time. However, the majority of these data-driven techniques are black box techniques and lack explainability which is often necessary for explaining decisions about critical assets in the power generation industry. Knowledge-based systems, such as rule-based expert systems have been shown to provide not only accurate decisions but also the explanation and reasoning behind these decisions in some related applications. However, there is a significant time cost associated with the development of knowledge-based systems, and in particular with the knowledge elicitation process, where the domain expert’s knowledge is formalised and is encoded into the system. This challenge is commonly referred to as the knowledge elicitation bottleneck.In this paper, we present a novel approach to performing the knowledge elicitation using a set of symbolic primitives (rise, fall, fluctuate, and stable) to parameterise typical time-series condition monitoring data. The knowledge is represented by using a common language that can easily be communicated with (and from) the domain experts. This allows for the quick and accurate elicitation of the domain experts knowledge, but also the formalisation and implementation of the knowledge into a rapidly produced diagnostic system. Further to this, due to the parametrisation of the knowledge, it is possible to iteratively improve the knowledge base by updating these parameters based on new unseen data. This approach was applied to the Tennessee Eastman dataset, which is simulated data of a real-world industrial process. It was found that by using this approach it was possible to accurately and quickly capture the knowledge required to detect several faults within the case study dataset, but also provided fully explained reasons why each fault was detected by relating the explanations to the symbolic primitives previously defined.

Updating conditional probabilities of Bayesian belief networks by merging expert knowledge and system monitoring data

Bayesian Belief Network (BBN) methods can be adopted for reliability analysis and real-time monitoring of critical infrastructure. Conditional Probability Tables (CPTs) are used to describe the dependencies between nodes in the BBN. The CPTs are usually defined by using either expert knowledge elicitation process or data-driven learning methods, and in particular, these methods define the CPTs statically, i.e. the CPTs are defined based only on the information available at the beginning of the analysis process. Therefore, the CPTs represent the correlation between the different components at one point in time, and potential correlations, evolving over time, are neglected.In this paper, a novel method to update the CPTs over time by merging expert judgement and the data about the system behaviour is proposed, which allows a process of continuous updating of CPTs. First of all, the method defines the CPTs by using the expert knowledge elicitation process. Then the CPTs are continuously updated whenever new data-driven evidence about the system becomes available. The proposed method is verified by assessing the health state of an in-field bridge, which experiences different magnitudes of damage. The BBN analysis of the health state of the bridge shows that the proposed method improves the accuracy of the BBN analysis, improving the diagnostics capabilities of the BBN up to 20%, by relying on a more robust and continuous updating of the CPTs.

Tacit knowledge elicitation process for industry 4.0

Manufacturers migrate their processes to Industry 4.0, which includes new technologies for improving productivity and efficiency of operations. One of the issues is capturing, recreating, and documenting the tacit knowledge of the aging workers. However, there are no systematic procedures to incorporate this knowledge into Enterprise Resource Planning systems and maintain a competitive advantage. This paper describes a solution proposal for a tacit knowledge elicitation process for capturing operational best practices of experienced workers in industrial domains based on a mix of algorithmic techniques and a cooperative game. We use domain ontologies for Industry 4.0 and reasoning techniques to discover and integrate new facts from textual sources into an Operational Knowledge Graph. We describe a concepts formation iterative process in a role game played by human and virtual agents through socialization and externalization for knowledge graph refinement. Ethical and societal concerns are discussed as well.

Statement on the active substance flupyradifurone.

Flupyradifurone is a novel butenolide insecticide, first approved as an active substance for use in plant protection products by Commission Implementing Regulation (EU) 2015/2084. Following concerns that this substance may pose high risks to humans and the environment, the French authorities, in November 2020, asked the Commission to restrict its uses under Article 69 of Regulation (EC) No 1107/2009. To support this request, competent Authorities from France cited a series of literature papers investigating its hazards and/or exposure to humans and the environment. In addition, in June 2020, the Dutch Authorities notified the Commission, under Article 56 of Regulation (EC) No 1107/2009, of new information on flupyradifurone on the wild bee species Megachile rotundata. This notification is also referred to in the French notification on flupyradifurone. Consequently, the EFSA PPR Panel was mandated to quantify the likelihood of this body of evidence constituting proof of serious risks to humans or the environment. Therefore, the EFSA PPR Panel evaluated the likelihood of these studies indicating new or higher hazards and exposure to humans and the environment compared to previous EU assessments. A stepwise methodology was designed, including: (i) the initial screening; (ii) data extraction and critical appraisal based on the principles of OHAT/NTP; (iii) weight of evidence, including consideration of the previous EU assessments; (iv) uncertainty analysis, followed, whenever relevant, by an expert knowledge elicitation process. For the human health, only one study was considered relevant for the genotoxic potential of flupyradifurone in vitro. These data did not provide sufficient information to overrule the EU assessment, as in vivo studies already addressed the genotoxic potential of flupyradifurone. Environment: All available data investigated hazards in bee species. For honey bees, the likelihood of the new data indicating higher hazards than the previous EU assessment was considered low or moderate, with some uncertainties. However, among solitary bee species – which were not addressed in the previous EU assessment – there was evidence that Megachile rotundata may be disproportionately sensitive to flupyradifurone. This sensitivity, which may partially be explained by the low bodyweight of this species, was mechanistically linked to inadequate bodily metabolisation processes.

Statement on the active substance acetamiprid.

Acetamiprid is a pesticide active substance with insecticidal action currently under the third renewal (AIR3) of the Commission implementing regulation (EU) No 844/2012. Following concerns that this substance may pose high risks to humans and the environment, the French authorities asked the Commission to restrict its uses under Article 69 of Regulation (EC) No 1107/2009. To support this request, competent Authorities from France cited a series of literature papers investigating its hazards and/or exposure to humans and the environment. Consequently, the EFSA PPR Panel was mandated to advise on the likelihood that body of evidence would constitute proof of serious risks to humans or the environment. Therefore, the EFSA PPR Panel evaluated the likelihood of these studies indicating new or higher hazards and exposure to humans and the environment compared to previous EU assessments.A stepwise methodology was designed, including: (i) the initial screening; (ii) the data extraction and critical appraisal based on the principles of OHAT/NTP; (iii) the weight of evidence, including consideration of the previous EU assessments; (iv) the uncertainty analysis, followed, whenever relevant, by an expert knowledge elicitation process. For human health, no conclusive evidence of higher hazards compared to previous assessment was found for genotoxicity, developmental toxicity, neurotoxicity including developmental neurotoxicity and immunotoxicity. However, due to the lack of adequate assessment of the current data set, the PPR Panel recommends conducting an assessment of endocrine disrupting properties for acetamiprid in line with EFSA/ECHA guidance document for the identification of endocrine disruptors. For environment, no conclusive, robust evidence of higher hazards compared to the previous assessment was found for birds, aquatic organisms, bees and soil organisms. However, the potential of high inter‐species sensitivity of birds and bees towards acetamiprid requires further consideration.

INFLUENCE FACTORS OF THE TACIT KNOWLEDGE ELICITATION PROCESS: SYSTEMATIC LITERATURE REVIEW

Goal: This systematic review aimed at highlighting and performing an integrated discussion of the factors of the tacit knowledge elicitation process. Design/Methodology/Approach: The research method adopted in this study was (PRISMA) Preferred Reporting Items for Systematic Reviews and Meta-Analyses. The databases, Science Direct, Web of Science, Scopus, and Emerald Insight were chosen based on the possibility of finding articles from collections such as Elsevier, Springer e Taylor & Francis. Search terms related to “knowledge Elicitation” are the following: "knowledge Elicitation" OR "knowledge acquisition" AND technique AND “Tacit Knowledge". Results: The main research findings listed by the article were the inclusion of a knowledge engineer considering the SECI model, the indication of predictability in the perception of episodic knowledge in a tacit knowledge elicitation process, and the hybrid adoption of knowledge elicitation techniques. Limitations of the research: The selection criteria were based only on articles written in the English language and taking into consideration the period from 2008 to 2020. Originality/value: The structure of this article was based on the indication of theoretical gaps and the need to deepen the themes underlying the process of eliciting tacit knowledge, which allowed a systematic exposure of a broad scenario that represents the scope and complexity.

An analytic network process model for risk quantification of mega construction projects

Risk, complexity, and uncertainty are inherent components of megaprojects due to their unique features. However, existing project management practices lack a structured synthesis of these concepts, which leads to unrealistic risk assessments, ineffective management strategies, and poor project performance. In order to fill this gap, this research aims to develop a holistic risk quantification approach incorporating risk-related concepts. For this purpose, a conceptual risk assessment process developed for mega construction projects was operationalized with an Analytic Network Process (ANP) model. The weights of the risk sources in the ANP model were determined by the domain experts through a two-round Delphi study. With the purpose of improving the efficiency and reliability of the knowledge elicitation process, the Delphi study was supported by an interactive data collection tool capable of ANP calculations. The resulting model helped to prioritize the risk sources in mega construction projects. The validity of the findings was tested through the data of 11 mega construction projects. Validation studies revealed the potential of the ANP-based model in quantifying the project risks. Hence, the novel approach proposed in this study is expected to contribute both to the literature by unveiling the interactions between risk-related concepts and to the practitioners by assisting them in assessing the project risks more realistically. Although the risk quantification model has been developed for mega construction projects, it can also be implemented in other project-based industries with minor modifications.

Health and welfare of rabbits farmed in different production systems.

The AGRI committee of the European Parliament requested EFSA to assess the welfare of rabbits farmed in different production systems, including organic production, and to update its 2005 scientific opinion about the health and welfare of rabbits kept for meat production. Considering reproducing does, kits and growing rabbits, this scientific opinion focusses on six different housing systems, namely conventional cages, structurally enriched cages, elevated pens, floor pens, outdoor/partially outdoor systems and organic systems. To compare the level of welfare in the different housing systems and rabbit categories, welfare impact scores for 20 welfare consequences identified from the literature were calculated, taking their occurrence, duration and severity into account. Based on the overall welfare impact score (sum of scores for the single welfare consequences), obtained via a 2‐step expert knowledge elicitation process, the welfare of reproducing does is likely (certainty 66–90%) to be lower in conventional cages compared to the five other housing systems. In addition, it is likely to extremely likely (certainty 66–99%) that the welfare of kits is lower in outdoor systems compared to the other systems and that the welfare is higher in elevated pens than in the other systems. Finally, it is likely to extremely likely (certainty 66–99%) that the welfare of growing rabbits is lower in conventional cages compared to the other systems and that the welfare is higher in elevated pens than in the other systems. Ranking of the welfare consequences allowed an analysis of the main welfare consequences within each system and rabbit category. It was concluded that for reproducing does, as well as growing rabbits, welfare consequences related to behavioural restrictions were more prominent in conventional cages, elevated pens and enriched cages, whereas those related to health problems were more important in floor pens, outdoor and organic systems. Housing in organic rabbit farming is diverse, which can result in different welfare consequences, but the overall welfare impact scores suggest that welfare in organic systems is generally good.

Identifying typical approaches and errors in Prolog programming with argument-based machine learning

Students learn programming much faster when they receive feedback. However, in programming courses with high student-teacher ratios, it is practically impossible to provide feedback to all homeworks submitted by students. In this paper, we propose a data-driven tool for semi-automatic identification of typical approaches and errors in student solutions. Having a list of frequent errors, a teacher can prepare common feedback to all students that explains the difficult concepts. We present the problem as supervised rule learning, where each rule corresponds to a specific approach or error. We use correct and incorrect submitted programs as the learning examples, where patterns in abstract syntax trees are used as attributes. As the space of all possible patterns is immense, we needed the help of experts to select relevant patterns. To elicit knowledge from the experts, we used the argument-based machine learning (ABML) method, in which an expert and ABML interactively exchange arguments until the model is good enough. We provide a step-by-step demonstration of the ABML process, present examples of ABML questions and corresponding expert’s answers, and interpret some of the induced rules. The evaluation on 42 Prolog exercises further shows the usefulness of the knowledge elicitation process, as the models constructed using ABML achieve significantly better accuracy than the models learned from human-defined patterns or from automatically extracted patterns.

Combining tacit knowledge elicitation with the SilverKnETs tool and random forests – The example of residential housing choices in Leipzig

Residential choice behaviour is a complex process underpinned by both housing market restrictions and individual preferences, which are partly conscious and partly tacit knowledge. Due to several limitations, common survey methods cannot sufficiently tap into such tacit knowledge. Thus, this paper introduces an advanced knowledge elicitation process called SilverKnETs and combines it with data mining using random forests to elicit and operationalize this type of knowledge. For the application case of the city of Leipzig, Germany, our findings indicate that rent, location and type of housing form the three predictors strongly influencing the decision making in residential choices. Other explanatory variables appear to have a much lower influence. Random forests have proven to be a promising tool for the prediction of residential choices, although the design and scope of the study govern the explanatory power of these models.

Assessment of a personalized and distributed patient guidance system

ObjectivesThe MobiGuide project aimed to establish a ubiquitous, user-friendly, patient-centered mobile decision-support system for patients and for their care providers, based on the continuous application of clinical guidelines and on semantically integrated electronic health records. Patients would be empowered by the system, which would enable them to lead their normal daily lives in their regular environment, while feeling safe, because their health state would be continuously monitored using mobile sensors and self-reporting of symptoms. When conditions occur that require medical attention, patients would be notified as to what they need to do, based on evidence-based guidelines, while their medical team would be informed appropriately, in parallel. We wanted to assess the system’s feasibility and potential effects on patients and care providers in two different clinical domains. Materials and methodsWe describe MobiGuide’s architecture, which embodies these objectives. Our novel methodologies include a ubiquitous architecture, encompassing a knowledge elicitation process for parallel coordinated workflows for patients and care providers; the customization of computer-interpretable guidelines (CIGs) by secondary contexts affecting remote management and distributed decision-making; a mechanism for episodic, on demand projection of the relevant portions of CIGs from a centralized, backend decision-support system (DSS), to a local, mobile DSS, which continuously delivers the actual recommendations to the patient; shared decision-making that embodies patient preferences; semantic data integration; and patient and care provider notification services.MobiGuide has been implemented and assessed in a preliminary fashion in two domains: atrial fibrillation (AF), and gestational diabetes Mellitus (GDM). Ten AF patients used the AF MobiGuide system in Italy and 19 GDM patients used the GDM MobiGuide system in Spain. The evaluation of the MobiGuide system focused on patient and care providers’ compliance to CIG recommendations and their satisfaction and quality of life. ResultsOur evaluation has demonstrated the system’s capability for supporting distributed decision-making and its use by patients and clinicians. The results show that compliance of GDM patients to the most important monitoring targets – blood glucose levels (performance of four measurements a day: 0.87±0.11; measurement according to the recommended frequency of every day or twice a week: 0.99±0.03), ketonuria (0.98±0.03), and blood pressure (0.82±0.24) – was high in most GDM patients, while compliance of AF patients to the most important targets was quite high, considering the required ECG measurements (0.65±0.28) and blood-pressure measurements (0.75±1.33). This outcome was viewed by the clinicians as a major potential benefit of the system, and the patients have demonstrated that they are capable of self-monitoring – something that they had not experienced before. In addition, the system caused the clinicians managing the AF patients to change their diagnosis and subsequent treatment for two of the ten AF patients, and caused the clinicians managing the GDM patients to start insulin therapy earlier in two of the 19 patients, based on system’s recommendations. Based on the end-of-study questionnaires, the sense of safety that the system has provided to the patients was its greatest asset. Analysis of the patients’ quality of life (QoL) questionnaires for the AF patients was inconclusive, because while most patients reported an improvement in their quality of life in the EuroQoL questionnaire, most AF patients reported a deterioration in the AFEQT questionnaire. DiscussionFeasibility and some of the potential benefits of an evidence-based distributed patient-guidance system were demonstrated in both clinical domains. The potential application of MobiGuide to other medical domains is supported by its standards-based patient health record with multiple electronic medical record linking capabilities, generic data insertion methods, generic medical knowledge representation and application methods, and the ability to communicate with a wide range of sensors. Future larger scale evaluations can assess the impact of such a system on clinical outcomes. ConclusionMobiGuide’s feasibility was demonstrated by a working prototype for the AF and GDM domains, which is usable by patients and clinicians, achieving high compliance to self-measurement recommendations, while enhancing the satisfaction of patients and care providers.

Generic Educational Nomination for Internationals University Structures – Ontology - GENIUS-Ontology

Purpose – This paper aims to present the results of the research and development of an Generic Ontology with terms nominations that would be applied to different Educational organizations. Design/methodology/approach – the research approach is qualitative, a applied research based on research and development. As Methodological type, it has a component of exploratory and documental research, had as field Research with specialist interviews. As technics and tools, it was used Internet for online research on universities organizational charts, open and unstructured interviews in the field research. In the development stage it was used two main software tools: OntoKEM e Protege ontology editor. The ontology development was applied to a real case - the Federal University of Santa Catarina in Brazil – with a knowledge elicitation process by extensive interviews with a domain specialist. Findings – Although universities use different names the roles in academic organizations of high education as Universities do not differ significantly. In fact the work of find common nomination for the same role demanded a lot of discussion and, therefore, a lot of time. However the feasibility of direct application is very high. Practical implications and originality – As result, differently from what was available as domain ontology till then, now there is available online an ontology that may be used directly for virtually any University organization.

Information processing and intuitive decision-making on the fireground: towards a model of expert intuition

In addition to other cognitive tasks that need attending to, experienced fireground commanders are also faced with a crucial task of identifying various environmental and informational cues that could affect their performance on the fireground. Although these cues play a crucial role in activating the pattern recognition or intuitive decision-making process, the major challenge remains that they usually emerge from multiple sources, thereby increasing the cognitive load in working memory. Previous studies have shown that attending to multiple informational sources has serious implications for intuitive decision-making as it then becomes more difficult to select the most relevant cues amidst the rapidly evolving conditions. In order to determine how firefighters cope with this difficult task of processing information from multiple sources, 16 experienced fireground commanders were interviewed using a semi-structured critical decision method protocol. Following the insights derived from the knowledge elicitation process, this paper presents and describes an expert intuition model, which we termed the information filtering and intuitive decision-making model. The model attempts to conceptualize how experienced firefighters scan through multiple information sources from which they are then able to select the most relevant cues that eventually aid the development of workable action plans.

First steps in translating human cognitive processes of cane pruning grapevines into AI rules for automated robotic pruning

Cane pruning of grapevines is a skilled task for which, internationally, there is a dire shortage of human pruners. As part of a larger project developing an automated robotic pruner, we have used artificial intelligence (AI) algorithms to create an expert system for selecting new canes and cutting off unwanted canes. A domain and ontology has been created for AI, which reflects the expertise of expert human pruners. The first step in the creation of an expert system was to generate virtual vines, which were then ‘pruned’ by human pruners and also by the expert system in its infancy. Here we examined the decisions of 12 human pruners, for consistency of decision, on 60 virtual vines. 96.7% of the 12 pruners agreed on at least one cane choice after which there was diminishing agreement on which further canes to select for laying. Our results indicate that techniques developed in computational intelligence can be used to co-ordinate and synthesise the expertise of human pruners into a best practice format. This paper describes first steps in this knowledge elicitation process, and discusses the fit between cane pruning expertise and the expertise that can be elicited using AI based expert system techniques.

Visual representation of connectivity information for efficient system understanding

This paper describes how User Center Design (UCD) methodology has been applied to develop a new visualization concept to support operation and maintenance of an oil and gas facility. The ultimate goal of the work is to improve energy efficiency in plant operation by creating an interactive visual representation of process and equipment dependencies at a modern industrial plant. This visualization concept aims to highlight important information which is today hidden in disparate information sources, and to support collaboration between personnel from different technical disciplines. The paper explains how user studies were used to promote the knowledge elicitation process and how UCD methods are being applied to the definition of functional requirements and to the creation of early concept prototypes.

Elicitation of neurological knowledge with argument-based machine learning

The paper describes the use of expert's knowledge in practice and the efficiency of a recently developed technique called argument-based machine learning (ABML) in the knowledge elicitation process. We are developing a neurological decision support system to help the neurologists differentiate between three types of tremors: Parkinsonian, essential, and mixed tremor (comorbidity). The system is intended to act as a second opinion for the neurologists, and most importantly to help them reduce the number of patients in the "gray area" that require a very costly further examination (DaTSCAN). We strive to elicit comprehensible and medically meaningful knowledge in such a way that it does not come at the cost of diagnostic accuracy. To alleviate the difficult problem of knowledge elicitation from data and domain experts, we used ABML. ABML guides the expert to explain critical special cases which cannot be handled automatically by machine learning. This very efficiently reduces the expert's workload, and combines expert's knowledge with learning data. 122 patients were enrolled into the study. The classification accuracy of the final model was 91%. Equally important, the initial and the final models were also evaluated for their comprehensibility by the neurologists. All 13 rules of the final model were deemed as appropriate to be able to support its decisions with good explanations. The paper demonstrates ABML's advantage in combining machine learning and expert knowledge. The accuracy of the system is very high with respect to the current state-of-the-art in clinical practice, and the system's knowledge base is assessed to be very consistent from a medical point of view. This opens up the possibility to use the system also as a teaching tool.

The development of a modified TRIZ Technical System ontology

This paper considers the development of a TRIZ Technical System ontology with the aim of facilitating the indexing of knowledge contained within available resources in a way that will make it accessible and useful to a user undertaking a design engineering task. The function of the developed ontology is evaluated using a case study, in which a patent is classified using the defined scheme. The quantitative nature of the classification structure developed to support this procedure offers a mechanism that may be further developed and integrated into research aiming to support the computerisation of the knowledge elicitation and representation process. The ontology developed to enable the classification methodology will support the integration of design by analogy procedures and enable effective information retrieval via the TRIZ based approach.

Combining Software Engineering Elicitation Technique with the Knowledge Management Lifecycle

Knowledge elicitation process allows acquiring and transferring the knowledge. This process presents difficulties to select the appropriate elicitation technique. This paper presents a classification of the elicitation techniques used in software engineering and the relationship between the elicitation techniques and some elements of knowledge management as assets knowledge, epistemological dimension of knowledge and the knowledge creation phases. This classification provides a guideline to select a technique or a set of techniques for knowledge elicitation based on phases of Nonaka’s model.