Knowledge Engineering and Knowledge Management

In this study, the authors found it appropriate to demonstrate the role, place and importance of engineering knowledge in the management practice of managing with a manufacturing plant, considering these issues from the perspective of the cost leadership strategy. When elaborating on engineering knowledge, special attention here should be paid to the development and analysis of the quality of products, based on a study of customer expectations, and to express these expectations by defining the relevant parameters of quality and technology. Taking up the discussion on the issues of organizational learning, the authors formulated the hypothesis corresponding to the adopted goals of this development: The cost leadershipstrategy, which is the result of the organization learning process is an important factor affecting the growth of the implemented product. Therefore, managing with managerial- -engineering knowledge is a key factor in engineering management strategy, including the cost management strategies.

Knowledge is a critical enabling factor for healthy agri-food innovation systems (AIS). AIS and related knowledge management (KM) frameworks face significant implementation challenges. We review applications of KM to AIS, the current state of the art and shortcomings and present a new KM framework, Agricultural Knowledge Management for Innovation (AKM4I). Previous agricultural KM frameworks do not integrate innovation pragmatically, use linear, reductionist, top-down pathways to innovation, and do not explicitly incorporate issues of power, politics, ownership, and trust when combining scientific and local knowledge across multiple stakeholders. The AKM4I framework addresses systemic interactions favouring innovation outcomes by formalising flows and management of information and knowledge between diverse sets of stakeholders; and explicitly considering previously unresolved practical and relational barriers aiming to facilitate more equitable, rapidly evolving, and actionable knowledge generation and management for innovation and transformational change. An agricultural case study serves as an example of the implementation of AKM4I.

The dependence of any organization on knowledge management is clearly understood. Actually, we should distinguish between knowledge management (KM) and knowledge engineering (KE): KM is to define and support organizational structure, allocate personnel to tasks, and monitor knowledge engineering activities; KE is concerned with technical matters, such as tools for knowledge acquisition, knowledge representation, and data mining. We shall use the designation KMKE for knowledge management and knowledge engineering collectively. KM is a very young area—the three articles termed “classic works” in Morey, Maybury, and Thuraisingham (2000) date from 1990, 1995, and 1996, respectively. We could regard 1991 as the start of institutionalized KM. This is when the Skandia AFS insurance company appointed a director of intellectual capital. KE has a longer history—expert systems have been in place for many years. Because of its recent origin, KMKE is characterized by rapid change. To deal with the change, we need to come to a good understanding of the nature of KMKE.

The dependence of any organization on knowledge management is clearly understood. Actually, we should distinguish between knowledge management (KM) and knowledge engineering (KE): KM is to define and support organizational structure, allocate personnel to tasks, and monitor knowledge engineering activities; KE is concerned with technical matters, such as tools for knowledge acquisition, knowledge representation, and data mining. We shall use the designation KMKE for knowledge management and knowledge engineering collectively. KM is a very young area—the three articles termed “classic works” in Morey, Maybury, and Thuraisingham (2000) date from 1990, 1995, and 1996, respectively. We could regard 1991 as the start of institutionalized KM. This is when the Skandia AFS insurance company appointed a director of intellectual capital. KE has a longer history—expert systems have been in place for many years. Because of its recent origin, KMKE is characterized by rapid change. To deal with the change, we need to come to a good understanding of the nature of KMKE.

This paper presents a knowledge management system for crop disease. The aim of KMSCD is to provide a knowledge management tool for efficient knowledge acquisition, storage, knowledge engineering, processing and proper maintenance of knowledge that can be ultimately used by the diagnostic expert system. The development of the KMSCD simplifies the complete process of knowledge management by providing user-friendly interface to the domain expert for entering and storing the domain specific knowledge to solve the disease identification and control problem particularly for oilseeds crops. The system presently applies to the knowledge management of 25 prevalent diseases of three major oilseeds crops of India viz. soybean, groundnut and rapeseed mustard. The adopted development methodology and the experience acquired in the knowledge engineering and development of knowledge management system for crop disease are discussed in this paper.

In multi-domain product development organizations, there is a continuous need to transfer captured knowledge between engineers to enable better design decisions in the future. The objective of this paper is to evaluate how engineering knowledge can be captured, disseminated and (re)used by applying a knowledge reuse tool entitled Engineering Checksheet (ECS). The tool was introduced in 2012 and this evaluation has been performed over the 2017–2018 period. This case study focused on codified knowledge in incremental product development with a high reuse potential both in and over time. The evaluation draws conclusions from the perspectives of the knowledge workers (the engineers), knowledge owners and knowledge managers. The study concludes that the ECS has been found to be valuable in enabling a timely understanding of technological concepts related to low level engineering tasks in the product development process. Hence, this enables knowledge flow and, in particular, reuse among inexperienced engineers, as well as providing quick and accurate quality control for experienced engineers. The findings regarding knowledge ownership and management relate to the need for clearly defining a knowledge owner structure in which communities of practice take responsibility for empowering engineers to use ECS and as knowledge evolves managing updates to the ECS.

We began implementing contest-based learning with a blend of software engineering and business management 10 years ago. At first, a project subject was assigned. However, several problems occurred: For example, the students became absorbed in programming rather than design and analysis activities. Therefore, the curriculum changed from project subjects to contest-based learning. Business management, marketing, and accounting subjects were added to the new curriculum, and students made information technology (IT) business plans using their knowledge of software engineering and business management. The IT business plans were submitted to various contests held by public newspaper companies and the federation of economic organizations in Japan. As a result, in the 10 years of the contest-based learning implementation, 20 teams have received awards in various IT business plan contests. We investigated 10 persons who had experience submitting business plans. We confirmed that contest-based learning had clearer goals, such as to win the contest prize, compared to project-based learning. Further, the abilities to solve problems and to investigate increased more in comparison with lecture-style and project-style education.

Knowledge Discovery, Knowledge Engineering and Knowledge Management

Knowledge Discovery, Knowledge Engineering and Knowledge Management

In this chapter, we present knowledge engineering (KE) and knowledge management (KM) techniques and applications in design. Then, we introduce an original model of knowledge representation called Hypertopic, and different methods of building collective knowledge representations based on Hypertopic.

Knowledge Discovery, Knowledge Engineering and Knowledge Management

The first edition of the workshop Educational Knowledge Management (EKM 2014) was held at the 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW). The workshop took place in the city of Linköping, Sweden, on the 24th of November 2014. The workshop was organized by Inaya Lahoud and Lars Ahrenberg.KeywordsKnowledge ManagementKnowledge EngineeringQuestion AnsweringIdea ManagementEducational Data MiningThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Knowledge Discovery, Knowledge Engineering and Knowledge Management

The concept of knowledge management formed at the end of the 20th century. At the same time, the vector of this concept is evolving due to the emergence of new business models, technologies, and the recognition of the “soft” components of success or the so-called “soft skills”, and changes in the strategies of organizations.The article is a systematic scientific review, the purpose of which is to identify the mainstream in the field of economics and knowledge management. The methodology of the article is based on a set of approaches, including the knowledgebased theory of the firm, the concepts of knowledge engineering, organizational learning, dynamic capabilities and knowledge transformation, etc., and the research methodology is based on taxonomic and cluster analysis of the most cited English-language publications using tools embedded in the VOSviewer software product. The information base consisted of articles for 2018–2024, selected according to the research topic and keywords “Knowledge Management”, and placed in the Dimensions database. As a result, we identified 6 clusters and formalized the directions of modern research in the field of knowledge management. In each cluster, a content analysis of the most cited articles was carried out. The research agenda is currently focused on two main issues: the impact of digitalization and digital technologies on the choice, generation and dissemination of knowledge in an organization; the impact of knowledge on innovation as a mediator of business performance, including its environmental and social aspects. In fact, a consensus has been found in understanding the basic postulates of the concept of knowledge management. However, the problem of finding the “good” metrics for assessing the knowledge of an organization and the impact of their level on business performance remains relevant. It is shown that among the theoretical frameworks in knowledge management, the most popular are knowledge engineering and knowledge-based theory of the firm. The results of the study will be useful to managers-practitioners and researchers in the development of effective knowledge management systems.

Knowledge management (KM) has evolved from the status of business fad to established business practice. Today leading organizations in business and government worldwide are employing KM principles and techniques to leverage intellectual capital in order to improve effectiveness, efficiency and innovation. Universities have established graduate programs in KM and professional societies have formed to promote education and standards. KM as a distinct management concept is a decade old now, and its status is said to be similar to where systems engineering and project management were in their infancies. While KM is not a mature discipline it is possible to identify key practice trends and lessons learned. This paper summarizes the state of KM with particular focus on KM in the federal government.