Foundations Of Knowledge Base Management

Current experimental systems in industry, government, and the military take advantage of knowledge-based processing. For example, the Defense Advanced Research Projects Agency (DARPA), and the United States Geological Survey (USGS) are supporting the develop ment of information systems that contain diverse, vast, and growing repositories of data (e.g., vast databases storing geographic informa tion). These systems require powerful reasoning capabilities and pro cessing such as data processing, communications, and multidisciplinary of such systems will scientific analysis. The number and importance grow significantly in the near future. Many of these systems are severely limited by current knowledge base and database systems technology. Currently, knowledge-based system technology lacks the means to provide efficient and robust knowledge bases, while database system technology lacks knowledge representation and reasoning capabilities. The time has come to face the complex research problems that must be solved before we can design and implement real, large scale software systems that depend on knowledge-based processing. To date there has been little research directed at integrating knowledge base and database technologies. It is now imperative that such coordinated research be initiated and that it respond to the urgent need for a tech nology that will enable operational large-scale knowledge-based system applications.

In the past, applied artificial intelligence systems were built with particular emphasis on general reasoning methods intended to function efficiently, even when only relatively little domain-specific knowledge was available. In other words, AI technology aimed at the processing of knowledge stored under comparatively general representation schemes. Nowadays, the focus has been redirected to the role played by specific and detailed knowledge, rather than to the reasoning methods themselves. Many new application systems are centered around knowledge bases, i. e. , they are based on large collections offacts, rules, and heuristics that cap ture knowledge about a specific domain of applications. Experience has shown that when used in combination with rich knowledge bases, even simple reasoning methods can be extremely effective in a wide variety of problem domains. Knowledge base construction and management will thus become the key factor in the development of viable knowledge-based ap plications. Knowledge Base Management Systems (KBMSs) are being proposed that provide user-friendly environments for the construction, retrieval, and manipUlation of large shared knowledge bases. In addition to deductive reasoning, KBMSs require operational characteristics such as concurrent access, integrity maintenance, error recovery, security, and perhaps distribution. For the development ofKBMSs, the need to integrate concepts and technologies from different areas, such as Artificial Intel ligence, Databases, and Logic, has been widely recognized. One of the central issues for KBMSs is the framework used for knowledge representation-semantic networks, frames, rules, and logics are proposed by the AI and logic communities.

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In the past, applied artificial intelligence systems were built with particular emphasis on general reasoning methods intended to function efficiently, even when only relatively little domain-specific knowledge was available. In other words, AI technology aimed at the processing of knowledge stored under comparatively general representation schemes. Nowadays, the focus has been redirected to the role played by specific and detailed knowledge, rather than to the reasoning methods themselves. Many new application systems are centered around knowledge bases, i. e. , they are based on large collections offacts, rules, and heuristics that cap ture knowledge about a specific domain of applications. Experience has shown that when used in combination with rich knowledge bases, even simple reasoning methods can be extremely effective in a wide variety of problem domains. Knowledge base construction and management will thus become the key factor in the development of viable knowledge-based ap plications. Knowledge Base Management Systems (KBMSs) are being proposed that provide user-friendly environments for the construction, retrieval, and manipUlation of large shared knowledge bases. In addition to deductive reasoning, KBMSs require operational characteristics such as concurrent access, integrity maintenance, error recovery, security, and perhaps distribution. For the development ofKBMSs, the need to integrate concepts and technologies from different areas, such as Artificial Intel ligence, Databases, and Logic, has been widely recognized. One of the central issues for KBMSs is the framework used for knowledge representation-semantic networks, frames, rules, and logics are proposed by the AI and logic communities.

Knowledge management is an ongoing process designed to dispense company information in a user-friendly way for both current and future employees. Knowledge Management Basics explains the key concepts and the overall benefits of implementing that process, offers information assessment techniques and tips and assists in the design of a practical system to capture, organize, and maintain company information. This title focuses on the learning professional's role in the knowledge management process and aims to teach those professionals how to get the process started within their organizations.Knowledge Management Basics presents a five-step process that will enable you, the learning professional, to: define your role in a knowledge management initiative and prepare to lead that effort determine your organization's needs locate knowledge sources choose systems to gather and house information that make sense for your organization's culture compile, confirm, and circulate knowledge maintain the knowledge system.

One of the main uses of computer systems is the management of large amounts of symbolic information representing the state of some application domain, such as information about all the people I communicate with in my personal address database, or relevant parts of the outer space in the knowledge base of a NASA space mission. While database management systems offer only the basic services of information storage and retrieval, more powerful knowledge systems offer, in addition, a number of advanced services such as deductive and abductive reasoning for the purpose of finding explanations and diagnoses, or generating plans. In order to design and understand database and knowledge-based applications it is important to build upon well-established conceptual and mathematical foundations. What are the principles behind database and knowledge systems? What are their major components? Which are the important cases of knowledge systems? What are their limitations? Addressing these questions, and discussing the fundamental issues of information update, knowledge assimilation, integrity maintenance, and inference-based query answering, is the purpose of this book. Foundations of Databases and Knowledge Systems covers both basic and advanced topics. It may be used as the textbook of a course offering a broad introduction to databases and knowledge bases, or it may be used as an additional textbook in a course on databases or Artificial Intelligence. Professionals and researchers interested in learning about new developments will benefit from the encyclopedic character of the book, which provides organized access to many advanced concepts in the theory of databases and knowledge bases.

'Knowledge Management Foundations' is just what it claims, the first attempt to provide a secure intellectual footing for the myriad of practices called "knowledge management." A breath of fresh air from the usual KM gurus, Fuller openly admits that the advent of KM is a mixed blessing that often amounts to the conduct of traditional management by subtler means. However, Fuller's deep understanding of both the history of management theory and knowledge production more generally enables him to separate the wheat from the chaff of the KM literature. This ground-breaking book will prove of interest to both academics and practitioners of knowledge management. It highlights the ways in which KM has challenged the values associated with knowledge that academics have taken for granted for centuries. At the same time, Fuller resists the conclusion of many KM gurus, that the value of knowledge lies in whatever the market will bear in the short term. He pays special attention to how information technology has not only facilitated knowledge work but also has radically altered its nature. There are chapters devoted to the revolution in intellectual property and an evaluation of peer review as a quality control mechanism. The book culminates in a positive re-evaluation of universities as knowledge producing institutions from which the corporate sector still has much to learn.

Creating Knowledge Based Organizations brings together high quality concepts and techniques closely related to organizational learning, knowledge workers, intellectual capital, and knowledge management. It includes the methodologies, systems and approaches that are needed to create and manage knowledge based organizations.