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This paper presents a hybrid artificial intelligence (AI) system capable of integrating techniques of case‐based reasoning, rule induction and expert system, using them for knowledge acquisition and problem solving of selecting appropriate retaining wall systems at the project planning stage. The proposed hybrid system can eliminate the bottleneck of knowledge acquisition in developing a knowledge‐based system and improve the solution quality of the AI‐based system. Test results indicate that solutions generated by the proposed hybrid system are better than those generated by using a single technique.

The purpose of this paper is to address the knowledge acquisition bottleneck problem in natural language processing by introducing a new rule‐based approach for the automatic acquisition of linguistic knowledge.

The author has developed a new machine translation methodology that only requires a bilingual lexicon and a parallel corpus of surface sentences aligned at the sentence level to learn new transfer rules.

A first prototype of a web‐based Japanese‐English translation system called Japanese‐English translation using corpus‐based acquisition of transfer (JETCAT) has been implemented in SWI‐Prolog, and a Greasemonkey user script to analyze Japanese web pages and translate sentences via Ajax. In addition, linguistic information is displayed at the character, word, and sentence level to provide a useful tool for web‐based language learning. An important feature is customization; the user can simply correct translation results leading to an incremental update of the knowledge base.

This paper focuses on the technical aspects and user interface issues of JETCAT. The author is planning to use JETCAT in a classroom setting to gather first experiences and will then evaluate a real‐world deployment; also work has started on extending JETCAT to include collaborative features.

The research has a high practical impact on academic language education. It also could have implications for the translation industry by superseding certain translation tasks and, on the other hand, adding value and quality to others.

The paper presents an extended version of the paper receiving the Emerald Web Information Systems Best Paper Award at iiWAS2010.

The purpose of this paper is to present various knowledge‐acquisition methods and to show how existing empirical research can be used for mapping between marketing problem domains and knowledge acquisition techniques. The key to doing this is to create a taxonomy of marketing problem domains.

This paper combines a thorough literature review with prima facie conceptualization to map a generic problem domain, and thereby provide guidance in the choice of knowledge‐acquisition technique for developers of expert systems in the field of marketing.

Recent empirical research in the field of expert systems shows that certain knowledge‐acquisition techniques are significantly more efficient than others for the extraction of certain types of knowledge within specific problem domains. It is found that protocol analysis, while fairly commonly used, is relatively inefficient for analytic problems. In the synthetic problem domain, interviewing proves to perform better for simple problems and worse for more difficult‐to‐model synthetic domains.

The findings suggest that it may be worth exploring some of the non‐traditional knowledge‐acquisition techniques when working on some types of applications. Further research could offer guidance in choosing the appropriate technique, with the aim of improving the quality, efficiency and development of the resulting system.

Designers of expert systems for marketing should consider interviewing and card sorting as the main means of knowledge acquisition for analytic problem domains, rather than protocol analysis as the main knowledge‐acquisition technique for analytic problem domains.

This paper is the first to suggest mapping between knowledge‐acquisition research and marketing problem domains.

A wearable expert system (WES) is an expert system designed and implemented to obtain input from and give outputs to wearable devices. Among its distinguishing features are the direct cooperation between domain experts and users, and the interaction with a knowledge maintenance system devoted to dynamically update the knowledge base taking care of the evolving scenario. The paper aims to discuss these issues.

The WES development method is based on the Knowledge Acquisition Framework based on Knowledge Artifact (KAFKA) framework. KAFKA employs multiple knowledge artifacts, each devoted to the acquisition and management of a specific kind of knowledge. The KAFKA framework is introduced from both the conceptual and computational points of view. An example is given which demonstrates the interaction, within this framework, of taxonomies, Bayesian networks and rule-based systems. An experimental assessment of the framework usability is also given.

The most interesting characteristic of WESs is their capability to evolve over time, due both to the measurement of new values for input variables and to the detection of new input events, that can be used to modify, extend and maintain knowledge bases and to represent domains characterized by variability over time.

WES is a new and challenging concept, dealing with the possibility for a user to develop his/her own decision support systems and update them according to new events when they arise from the environment. The system fully supports domain experts and users with no particular skills in knowledge engineering methodologies, to create, maintain and exploit their expert systems, everywhere and when necessary.

This paper aims to explore the use of the knowledge management (KM) perspective for configuration projects. Configuration projects implement configurators as information technology systems that help companies manage the specification process of customised products. An effective method of retrieving and formalising knowledge for configurators is essential, because it can reduce the risk of unsuccessful implementation and the time and effort required for development. Unfortunately, no standard KM frameworks are available specifically for configuration projects. This study identifies the knowledge necessary for different phases of a configuration project (which knowledge, for what purpose and from what sources), examines how it is transformed during a configuration project (what KM activities and tools are used) and establishes how the knowledge can be documented for future maintenance and updates.

This paper proposes a four-step framework for making the KM process more efficient in configuration projects. The framework is based on the literature, developed in collaboration with industrial partners and tested on four configuration projects in two engineering companies. The framework is a structured KM approach designed to save time for both domain experts and the configuration team. The authors have used a qualitative exploratory design based on multiple data sources: documentation, workshops and participant observation.

The proposed framework comprises four steps: determination of the system’s scope, to establish the project’s goal based on stakeholders’ requirements and prioritise the required products and processes; knowledge acquisition, to classify the knowledge according to the desired output and identify different knowledge sources; modelling and knowledge validation; and documentation and maintenance, to ensure that the KM system can be maintained and updated in the future.

Because the framework is tested on a limited number of cases, its generalisability may be limited. However, focusing on a few case applications allows us to assess the effectiveness of the framework in detail and in depth to identify the practical challenges of applying it. The results of the tests support the framework’s validity. Although the framework is designed mainly for engineering companies, other industries could benefit from using it as well.

The individual steps of the framework create a structured approach for the KM process. Thus, the approach can save both time and resources for companies, without the need for additional investment.

A standard framework is lacking in the literature on KM for configuration projects. This study fills that gap by developing a KM framework for configuration projects, based on KM frameworks developed for IT projects, and KM tools.

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.

The purpose of this paper is to describe an innovative information and decision support tool (ToolSHeD™) developed to help construction designers to integrate the management of OHS risk into the design process. The underlying structure of the prototype web‐based system and the process of knowledge acquisition and modelling are described.

The ToolSHeD™ research and development project involved the capture of expert reasoning regarding design impacts upon occupational health and safety (OHS) risk. This knowledge was structured using an innovative method well‐suited to modelling knowledge in the context of uncertainty and discretionary decision‐making. Example “argument trees” are presented, representing the reasoning used by a panel of experts to assess the risk of falling from height during roof maintenance work. The advantage of using this method for modelling OHS knowledge, compared to the use of simplistic rules, is discussed

The ToolSHeD™ prototype development and testing reveals that argument trees can represent design safety risk knowledge effectively.

The translation of argument trees into a web‐based decision support tool is described and the potential impact of this tool in providing construction designers (architects and engineers) with easy and inexpensive access to expert OHS knowledge is discussed.

The paper describes a new computer application, currently undergoing testing in the Australian building and construction industry. Its originality lies in the fact that ToolSHeD™ deploys argument trees to represent expert OHS reasoning, overcoming inherent limitations in rule‐based expert systems.

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.

Traditional case-adaptation methods have poor accuracy, low efficiency and limited applicability, which cannot meet the needs of knowledge users. To address the shortcomings of the existing research in the industry, this paper proposes a case-adaptation optimization algorithm to support the effective application of tacit knowledge resources.

The attribute simplification algorithm based on the forward search strategy in the neighborhood decision information system is implemented to realize the vertical dimensionality reduction of the case base, and the fuzzy C-mean (FCM) clustering algorithm based on the simulated annealing genetic algorithm (SAGA) is implemented to compress the case base horizontally with multiple decision classes. Then, the subspace K-nearest neighbors (KNN) algorithm is used to induce the decision rules for the set of adapted cases to complete the optimization of the adaptation model.

The findings suggest the rapid enrichment of data, information and tacit knowledge in the field of practice has led to low efficiency and low utilization of knowledge dissemination, and this algorithm can effectively alleviate the problems of users falling into “knowledge disorientation” in the era of the knowledge economy.

This study provides a model with case knowledge that meets users’ needs, thereby effectively improving the application of the tacit knowledge in the explicit case base and the problem-solving efficiency of knowledge users.

The adaptation model can serve as a stable and efficient prediction model to make predictions for the effects of the many logistics and e-commerce enterprises' plans.

This study designs a multi-decision class case-adaptation optimization study based on forward attribute selection strategy-neighborhood rough sets (FASS-NRS) and simulated annealing genetic algorithm-fuzzy C-means (SAGA-FCM) for tacit knowledgeable exogenous cases. By effectively organizing and adjusting tacit knowledge resources, knowledge service organizations can maintain their competitive advantages. The algorithm models established in this study develop theoretical directions for a multi-decision class case-adaptation optimization study of tacit knowledge.

The purpose of this paper is to describe the CODO ontology (COviD-19 Ontology) that captures epidemiological data about the COVID-19 pandemic in a knowledge graph that follows the FAIR principles. This study took information from spreadsheets and integrated it into a knowledge graph that could be queried with SPARQL and visualized with the Gruff tool in AllegroGraph.

The knowledge graph was designed with the Web Ontology Language. The methodology was a hybrid approach integrating the YAMO methodology for ontology design and Agile methods to define iterations and approach to requirements, testing and implementation.

The hybrid approach demonstrated that Agile can bring the same benefits to knowledge graph projects as it has to other projects. The two-person team went from an ontology to a large knowledge graph with approximately 5 M triples in a few months. The authors gathered useful real-world experience on how to most effectively transform “from strings to things.”

This study is the only FAIR model (to the best of the authors’ knowledge) to address epidemiology data for the COVID-19 pandemic. It also brought to light several practical issues that generalize to other studies wishing to go from an ontology to a large knowledge graph. This study is one of the first studies to document how the Agile approach can be used for knowledge graph development.