The disciplines of knowledge engineering and knowledge management are closely tied. Knowledge engineering deals with the development of information systems in which knowledge and reasoning play pivotal roles. Knowledge management, a newly developed field at the intersection of computer science and management, deals with knowledge as a key resource in modern organizations. Managing knowledge within an organization is inconceivable without the use of advanced information systems; the design and implementation of such systems pose great organization as well as technical challenges.

Knowledge Management (KM) is strongly rooted in the discipline of Knowledge Engineering (KE), which in turn grew partly out of the artificial intelligence field. Despite their close relationship, however, many KM specialists have failed to fully recognize the synergy or acknowledge the power that KE methodologies, techniques, and tools hold for enh

This book constitutes the refereed proceedings of the 12th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2000, held in Juan-les-Pins, France in October 2000. The 28 revised full papers and six revised short papers presented were carefully reviewed and selected from a high number of high-quality submissions. The book offers topical sections on knowledge modeling languages and tools, ontologies, knowledge acquisition from texts, machine learning, knowledge management and electronic commerce, problem solving methods, knowledge representation, validation, evaluation and certification, and methodologies.

This is the first book on experience-based knowledge representation and knowledge management using the unique Decisional DNA (DDNA) technology. The DDNA concept is roughly a decade old, and is rapidly attracting increasing attention and interest among researchers and practitioners. This comprehensive book provides guidelines to help readers develop experience-based tools and approaches for smart engineering of knowledge, data and information. It does not attempt to offer ultimate answers, but instead presents ideas and a number of real-world case studies to explore and exemplify the complexities and challenges of modern knowledge engineering issues. It also increases readers’ awareness of the multifaceted interdisciplinary character of such issues to enable them to consider – in different ways – developing, evaluating, and supporting smart knowledge engineering systems that use DDNA technology based on experience.

Using robust software, this book focuses on learning assistants for evidence-based reasoning that learn complex problem solving from humans.

An authoritative guide to key engineering management principles and practices, this book is divided into eight concise domains of engineering management knowledge, which are further broken down into 46 knowledge areas and 210 sub-knowledge areas. This guide covers a wide range of management topics and practices, including market research, product development, organizational leadership and the management of engineering projects and processes. A diverse panel of practicing engineers and subject matter experts from across industry, government and academia, formed a committee of professionals to develop a readable, comprehensive, user-friendly body of knowledge guide. Whether you're a practicing engineer, an engineering manager, or a trainer of engineers, you'll find this easy-to-use guide an indispensable resource.

Knowledge Engineering (KE) is a field within artificial intelligence that develops knowledgebased systems. KE is the process of imitating how a human expert in a specific domain would act and take decisions. It contains large amounts of knowledge, like metadata and information about a data object that describes characteristics such as content, quality, and format, structure and processes. Such systems are computer programs that are the basis of how a decision is made or a conclusion is reached. It is having all the rules and reasoning mechanisms to provide solutions to real-world problems. This book presents an extensive collection of the recent findings and innovative research in the information system and KE domain. Highlighting the challenges and difficulties in implementing these approaches, this book is a critical reference source for academicians, professionals, engineers, technology designers, analysts, undergraduate and postgraduate students in computing science and related disciplines such as Information systems, Knowledge Engineering, Intelligent Systems, Artifi cial Intelligence, Cognitive Neuro - science, and Robotics. In addition, anyone who is interested or involved in sophisticated information systems and knowledge engineering developments will find this book a valuable source of ideas and guidance.