Knowledge engineering

Abstract: From the Publisher: The definitive primer on knowledge management, this book will establish the enduring vocabulary and concepts and serve as the hands-on resource of choice for fast companies that recognize knowledge as the only sustainable source of competitive advantage. Drawing on their work with more than 30 knowledge-rich firms, the authors-experienced consultants with a track record of success-examine how all types of companies can effectively understand, analyze, measure, and manage their intellectual assets, turning corporate knowledge into market value. They consider such questions as: What key cultural and behavioral issues must managers address to use knowledge effectively?; What are the best ways to incorporate technology into knowledge work?; What does a successful knowledge project look like-and how do you know when it has succeeded? In the end, say the authors, the human qualities of knowledge-experience, intuition, and beliefs-are the most valuable and the most difficult to manage. Applying the insights of Working Knowledge is every manager's first step on that rewarding road to long-term success. A Library Journal Best Business Book of the Year. "For an entire company...to have knowledge, that information must be coordinated and made accessible. Thomas H. Davenport...and Laurence Prusak... offer an elegantly simple overview of the 'knowledge market' aimed at fulfilling that goal.... Working Knowledge provides practical advice about implementing a knowledge-management system....A solid dose of common sense for any company looking to acquire -- or maintain -- a competitive edge."--Upside, June 1998

Abstract: Knowledge is a broad and abstract notion that has defined epistemological debate in western philosophy since the classical Greek era. In the past few years, however, there has been a growing interest in treating knowledge as a significant organizational resource. Consistent with the interest in organizational knowledge and knowledge management (KM), IS researchers have begun promoting a class of information systems, referred to as knowledge management systems (KMS). The objective of KMS is to support creation, transfer, and application of knowledge in organizations. Knowledge and knowledge management are complex and multi-faceted concepts. Thus, effective development and implementation of KMS requires a foundation in several rich literatures.

Abstract: The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation.

Abstract: With the continuous expansion of data availability in many large-scale, complex, and networked systems, such as surveillance, security, Internet, and finance, it becomes critical to advance the fundamental understanding of knowledge discovery and analysis from raw data to support decision-making processes. Although existing knowledge discovery and data engineering techniques have shown great success in many real-world applications, the problem of learning from imbalanced data (the imbalanced learning problem) is a relatively new challenge that has attracted growing attention from both academia and industry. The imbalanced learning problem is concerned with the performance of learning algorithms in the presence of underrepresented data and severe class distribution skews. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. In this paper, we provide a comprehensive review of the development of research in learning from imbalanced data. Our focus is to provide a critical review of the nature of the problem, the state-of-the-art technologies, and the current assessment metrics used to evaluate learning performance under the imbalanced learning scenario. Furthermore, in order to stimulate future research in this field, we also highlight the major opportunities and challenges, as well as potential important research directions for learning from imbalanced data.