The book constitutes the thoroughly refereed proceedings of the 11th International Workshop on Knowledge Management and Acquisition for Smart Systems and Services, held in Daegue, Korea in August 2010 in conjunction with the Pacific Rim International Conference on Artificial Intelligence, PRICAI 2010. The 26 revised full papers were selected from 94 submissions and are organized in topical sections on Machine Learning, Data Mining, Knowledge Engineering & Ontology, Incremental Knowledge Acquisition, KA Applications in Internet and Mobile Computing and KA Applications in Multimedia and Games.

This book constitutes the proceedings of the 13th International Workshop on Knowledge Management and Acquisition for Intelligent Systems, PKAW 2014, held in Gold Cost, Qld, Australia, in December 2014. The 18 full papers and 4 short papers included in this volume were carefully reviewed and selected from 69 initial submissions. They deal with knowledge acquisition, expert systems, intelligent agents, ontology engineering, foundations of artificial intelligence, machine learning, data mining, Web mining, information systems, Web and other applications.

Within educational organizations, administration and leadership are relied upon for the allocation of resources as well as the optimization of processes that can include data storage, knowledge management, and decision making. To support these expectations, technologies, knowledge, and smart systems must be put into place that allow administrators and leaders to accomplish these tasks as efficiently as possible. Utilizing Technology, Knowledge, and Smart Systems in Educational Administration and Leadership is an academic research book that examines knowledge regarding the scholarly exploration of the technologies, information/knowledge, and smart systems in educational administration and leadership. It provides a holistic, systematic, and comprehensive paradigm. Featuring a wide range of topics such as technology leadership in schools, technology integration in educational administration, and professional development, this book is ideal for school administrators, educational leaders, principals, IT consultants, educational software developers, academicians, researchers, professionals, educational policymakers, educators, and students.

Knowledge management has always been about the process of creating, sharing, using, and applying knowledge within and between organizations. Before the advent of information systems, knowledge management processes were manual or offline. However, the emergence and eventual evolution of information systems created the possibility for the gradual but slow automation of knowledge management processes. These digital technologies enable data capture, data storage, data mining, data analytics, and data visualization. The value provided by such technologies is enhanced and distributed to organizations as well as customers using the digital technologies that enable interconnectivity. Today, the fine line between the technologies enabling the technology-driven external pressures and data-driven internal organizational pressures is blurred. Therefore, how technologies are combined to facilitate knowledge management processes is becoming less standardized. This results in the question of how the current advancement in digital technologies affects knowledge management processes both within and outside organizations. Digital Technology Advancements in Knowledge Management addresses how various new and emerging digital technologies can support knowledge management processes within organizations or outside organizations. Case studies and practical tips based on research on the emerging possibilities for knowledge management using these technologies is discussed within the chapters of this book. It both builds on the available literature in the field of knowledge management while providing for further research opportunities in this dynamic field. This book highlights topics such as human-robot interaction, big data analytics, software development, keyword extraction, and artificial intelligence and is ideal for technology developers, academics, researchers, managers, practitioners, stakeholders, and students who are interested in the adoption and implementation of new digital technologies for knowledge creation, sharing, aggregation, and storage.

"This book provides an opportunity for readers to clearly understand the notion of ontology engineering and the practical aspects of this approach in the domains of two interest areas: Knowledge Management Systems and Enterprise Systems"--

Machine learning algorithms hold extraordinary promise, but the reality is that their success depends entirely on the suitability of the data available. This book is about Ripple-Down Rules (RDR), an alternative manual technique for rapidly building AI systems. With a human in the loop, RDR is much better able to deal with the limitations of data. Ripple-Down Rules: The Alternative to Machine Learning starts by reviewing the problems with data quality and the problems with conventional approaches to incorporating expert human knowledge into AI systems. It suggests that problems with knowledge acquisition arise because of mistaken philosophical assumptions about knowledge. It argues people never really explain how they reach a conclusion, rather they justify their conclusion by differentiating between cases in a context. RDR is based on this more situated understanding of knowledge. The central features of a RDR approach are explained, and detailed worked examples are presented for different types of RDR, based on freely available software developed for this book. The examples ensure developers have a clear idea of the simple yet counter-intuitive RDR algorithms to easily build their own RDR systems. It has been proven in industrial applications that it takes only a minute or two per rule to build RDR systems with perhaps thousands of rules. The industrial uses of RDR have ranged from medical diagnosis through data cleansing to chatbots in cars. RDR can be used on its own or to improve the performance of machine learning or other methods.