Knowledge Management And Acquisition For Smart Systems And Services

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.

th The 11 International Workshop on Knowledge Management and Acquisition for Smart Systems and Services (PKAW 2010) has provided a forum for the past two decades for researchers and practitioners working in the area of machine intelligence. PKAW covers a spectrum of techniques and approaches to implement smartness in IT applications. As evidenced in the papers in this volume, machine intelligence solutions incorporate many areas of AI such as ontological engineering, agent-based techn- ogy, robotics, image recognition and the Semantic Web as well as many other fields of computing such as software engineering, security, databases, the Internet, information retrieval, language technology and game technology. PKAW has evolved to embrace and foster advances in theory, practice and te- nology not only in knowledge acquisition and capture but all aspects of knowledge management including reuse, sharing, maintenance, transfer, merging, reconciliation, creation and dissemination. As many nations strive to be knowledge economies and organizations seek to maximize their knowledge assets and usage, solutions to handle the complex task of knowledge management are more important than ever. This v- ume contributes towards this goal. This volume seeks to disseminate the latest solutions from the International Wo- shop on Knowledge Management and Acquisition for Smart Systems and Services (PKAW 2010) held in Daegu, Korea during August 30–31, 2010 in conjunction with the Pacific Rim International Conference on Artificial Intelligence (PRICAI 2010).

Knowledge Management is concerned with all aspects of eliciting, acquiring, modelling, and managing knowledge. Application of knowledge resources successfully helps the organization to deliver creative products and services. Especially in service business, service job experience and information about the customer, as well as the installed site equipment, are key factors to deliver services efficiently and with high quality. In many cases supporting information is stored in different backend systems and it needs to be retrieved, aggregated, and presented on demand. Knowledge Management and Drivers of Innovation in Services Industries provides a comprehensive collection of knowledge from experts within the Information and Knowledge Management field. Outlining areas on Knowledge Management, Innovation, Information Technologies and Systems, and Services Industry, this book provides insight for academic professors, policymakers, and students alike.

"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.

This book constitutes the proceedings of the 15th International Workshop on Knowledge Management and Acquisition for Intelligent Systems, PKAW 2018, held in Nanjing, China, in August 2018. The 15 full papers and 7 short papers included in this volume were carefully reviewed and selected from 51 initial submissions. They cover the methods and tools as well as the applications related to developing a knowledge base, healthcare, financial systems, and intelligent systems.