Knowledge Discovery, Knowledge Engineering and Knowledge Management

Knowledge Discovery, Knowledge Engineering and Knowledge Management

Knowledge Discovery, Knowledge Engineering and Knowledge Management

Knowledge Discovery, Knowledge Engineering and Knowledge Management

During the last decade, Knowledge Discovery and Management (KDM or, in French, EGC for Extraction et Gestion des connaissances) has been an intensive and fruitful research topic in the French-speaking scientific community. In 2003, this enthusiasm for KDM led to the foundation of a specific French-speaking association, called EGC, dedicated to supporting and promoting this topic. More precisely, KDM is concerned with the interface between knowledge and data such as, among other things, Data Mining, Knowledge Discovery, Business Intelligence, Knowledge Engineering and Semantic Web. The recent and novel research contributions collected in this book are extended and reworked versions of a selection of the best papers that were originally presented in French at the EGC 2010 Conference held in Tunis, Tunisia in January 2010. The volume is organized in three parts. Part I includes four chapters concerned with various aspects of Data Cube and Ontology-based representations. Part II is composed of four chapters concerned with Efficient Pattern Mining issues, while in Part III the last four chapters address Data Preprocessing and Information Retrieval.

Current research activities are leveraging the Grid to create generic- and domain-specific solutions and services for data management and knowledge discovery. Knowledge and Data Management in Grids is the third volume of the CoreGRID series; it gathers contributions by researchers and scientists working on storage, data, and knowledge management in Grid and Peer-to-Peer systems. This volume presents the latest Grid solutions and research results in key areas such as distributed storage management, Grid databases, Semantic Grid and Grid-aware data mining. Written for a professional audience of researchers and practitioners in industry, it is suitable for graduate-level students in computer science.

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.

Human-machine interaction (HMI) is an important field in knowledge discovery and management, allowing for improved user engagement, decision-making, and efficiency. It involves user-centered design, information architecture, visualization, recommendation systems, collaborative filtering, machine learning, feature extraction, dimensionality reduction, clustering, classification, evaluation, validation, text mining, sentiment analysis, topic modelling, information extraction, opinion mining, and language generation. A case study in the healthcare and agricultural domains demonstrated the practical implementation of HMI in knowledge discovery and highlighted the positive impact of HMI approaches in addressing challenges specific to healthcare knowledge management. In conclusion, HMI plays a pivotal role in knowledge discovery and management, enabling users to effectively explore and extract valuable insights from complex datasets.

Knowledge management (KM) and business intelligence (BI) allow businesses to acquire, store, and access important information to create competitive advantages. While large businesses have used BI and KM integrations, small- and medium-sized enterprises (SMEs) have lagged behind in these areas. This qualitative exploratory case study used interviews from 11 knowledge management (KM) experts to gather their perceptions of integrating business intelligence (BI) into KM programs. The goal was to identify factors that could improve the performance of structured KM programs within SMEs. A scholarly literature review identified a gap in practice and knowledge; few studies have covered SMEs’ adoption of KM and BI tools. The study’s framework included a technology acceptance model and absorptive capacity (ACAP) theory. Research findings showed that integrating KM and BI tools can lead to KM and BI tool adoption, knowledge discovery, performance improvement, tacit knowledge assets, knowledge sharing, KMS/KB, and ACAP.

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.

Purpose The extraction of relevant knowledge from data is called knowledge discovery (KD). The KD process requires a large amount of data and it must be reliable before mining. Complexity is not only in deriving knowledge from data but also in improving system performance with a psycho-cognitive approach. KD demands a high level of human cognition and mental activity to generate and retrieve knowledge. Therefore, this study aims to explain how psychological knowledge is involved in KD. Design/methodology/approach By understanding the cognitive processes that lead to knowledge production, KD can be improved through interventions that target psychological processes, such as attention, learning and memory. In addition, psycho-cognitive approaches can help us to better grasp the process of KD and the factors that influence its effectiveness. The study attempted to correlate interdependence by interpreting cognitive approaches to KD from a psychological perspective. The authors of this paper draw on both primary and secondary literary warrants to empirically prove psychological bending in KD. Findings Understanding the psychological aspects of data and KD can identify the development of tools, process and environments that support individual and teams in making sense of data and extracting valuable knowledge. The study also finds that interdisciplinary collaboration, bringing together expertise in psychology, data science and domain specific knowledge fosters effective KD processes. Originality/value The KD system cannot function well and will not be able to achieve its full potential without psycho-cognitive foundation. It was found that KD in the KD system is influenced by human cognition. The authors made a contribution to KD by fusing psycho-cognitive approaches with data-driven technology and machine learning.

Inter-organization knowledge management in the context of collaborative networks is a critical activity for business success. During the evolution of collaborative network specific technologies, increasingly performant instruments were created to exploit this knowledge. As a development cycle, inter-organizational knowledge is built on the foundation of information and data owned by the participants in the collaborative networks. One of the most widely used instruments to exploit this data and knowledge, with the purpose of creating new knowledge, is Data Mining. In the context of this paper, data mining is the process of discovering patterns and hidden relations in very large data collections, stored in data banks or data bases. Because only in extremely rare cases reading data tables record by record leads to the discovery of useful patterns, the information must be processed automatically, process known as Knowledge Discovery. Knowledge Discovery is a component that combines the power of computers with a human operator that has the ability to find the visual patterns revealed by the system. Using an automated data mining system, the computer finds the existing informational patterns and the human factor (the analyst) evaluates those patterns and picks the ones that are really relevant for the current analysis. Considering the current technological context, where storage devices are more and more accessible and performant, the storage capacity is no longer a barrier preventing storage of all required data. Exploitation of inter-organizational knowledge in collaborative networks leads the research to the field of business intelligence applied even on social environment. This approach belongs in literature to the general branch of social business intelligence.Keywords: Collaborative Networks, Information Technology, Inter-Organizational Knowledge, Knowledge Management, Knowledge Discovery, Data Mining1 IntroductionT he importance of knowledge and organizational networks in obtaining a competitive advantage on the market is recognized both by theoreticians and by practitioners as well. The increase of the number of collaborative networks, the accent on knowledge within the society based on knowledge and innovation come to support those who want to adhere to new organizational forms that would lead to obtaining success on the global market.Knowledge is known as one of the most important assets of management within organizations, as knowledge allows organizations to use and develop resources, to increase the competitive ability and to obtain a substantial competitive advantage [1]. Knowledge also represents an important source that allows nations, organizations, and persons to obtain benefits, such as: learning improvement, innovation, and decision making. Any organization, public or private, needs a knowledge management process in order to obtain the best performances [2].In global economy, the strong competition, the frequent changes on the market, the higher and higher demands concerning quality, lead to the necessity of new organizational forms. Organizational networks are acknowledged as organizational forms characterized by an increased flexibility and that may lead to obtaining the competitive advantage on the market. Apart from the potential advantages, organizational networks are also confronted with problems and challenges that are particularly connected to the complexity of the collaborative environment.Inter-organizational knowledge can be defined as an explicit set of knowledge that is formalized and created by organizations [3]. The interactions within the network allow organizations to develop the collaborative and relational tacit knowledge and to generate inter-organizational tacit knowledge that can be capitalized within the interorganizational memory [4]. The interorganizational knowledge allows organizations to develop distinctive abilities, which may lead to the increase of the innovation ability. …

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.