Towards Very Large Knowledge Bases

In the early days of artificial intelligence it was widely believed that powerful computers would, in the future, enable mankind to solve many real-world problems through the use of very general inference procedures and very little domain-specific knowledge. With the benefit of hindsight, this view can now be called quite naive. The field of expert systems, which developed during the early 1970s, embraced the paradigm that Knowledge is Power - even very fast computers require very large amounts of very specific knowledge to solve non-trivial problems. Thus, the field of large knowledge bases has emerged.

Chapter one presents the Cyc "philosophy" or paradigm. Chapter 2 presents a global overview of Cyc, including its representation language, the ontology f its knowledge base, and teh environment which it functions. Chapter 3 goes into much more detail on the representation language, including the structure and function of Cyc's metalevel agenda mechanism. Chapter 4 presents heuristics for ontological engineering, the pricnples upon whcihc Cyc's ontology is based. Chapter 5 the provides a glimpse into the global ontology of knowledge. Chapter 6 explains how we "solve" (i.e., adequately handle) the various tough representation thorns (substances, time, space, structures, composite mental/physical objects, beliefs, uncertainty, etc. ). Chapter 7 surveys the mistakes that new knowledge tnereres most often commit. Chapter 8, the concluding chapter, includes a brief status report on the project, and a statement of goals and a timetable for the coming five years.

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.

Papers direct the focus of interest to the development and use of conceptual models in information systems of various kinds and aim at improving awareness about general or specific problems and solutions in conceptual modelling.

Annotation The three volume set LNAI 4692, LNAI 4693, and LNAI 4694, constitute the refereed proceedings of the 11th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2007, held in Vietri sul Mare, Italy, September 12-14, 2007. The 409 revised papers presented were carefully reviewed and selected from about 1203 submissions. The papers present a wealth of original research results from the field of intelligent information processing in the broadest sense; topics covered in the first volume are artificial neural networks and connectionists systems, fuzzy and neuro-fuzzy systems, evolutionary computation, machine learning and classical AI, agent systems, knowledge based and expert systems, hybrid intelligent systems, miscellaneous intelligent algorithms, intelligent vision and image processing, knowledge management and ontologies, Web intelligence, multimedia, e-learning and teaching, intelligent signal processing, control and robotics, other intelligent systems applications, papers of the experience management and engineering workshop, industrial applications of intelligent systems, as well as information engineering and applications in ubiquotous computing environments.

Motivation for this Book Ontologies have received increasing attention over the last two decades. Their roots can be traced back to the ancient philosophers, who were interested in a c- ceptualization of the world. In the more recent past, ontologies and ontological engineering have evolved in computer science, building on various roots such as logics, knowledge representation, information modeling and management, and (knowledge-based) information systems. Most recently, largely driven by the next generation internet, the so-called Semantic Web, ontological software engineering has developed into a scientific field of its own, which puts particular emphasis on the theoretical foundations of representation and reasoning, and on the methods and tools required for building ontology-based software applications in diverse domains. Though this field is largely dominated by computer science, close re- tionships have been established with its diverse areas of application, where - searchers are interested in exploiting the results of ontological software engine- ing, particularly to build large knowledge-intensive applications at high productivity and low maintenance effort. Consequently, a large number of scientific papers and monographs have been p- lished in the very recent past dealing with the theory and practice of ontological software engineering. So far, the majority of those books are dedicated to the th- retical foundations of ontologies, including philosophical treatises and their re- tionships to established methods in information systems and ontological software engineering.

Enter a magical world of friendship and fun! In the sixth book of the first Secret Kingdom series, every fairy in the kingdom is at Glitter Beach to watch the magic being renewed in the kingdom for another year. But Queen Malice is also nearby... Can Ellie, Summer and Jasmine save the glitter dust and keep the magic alive? Secret Kingdom is a brand new series full of the things girls love most: special friendships, secrets and magical adventures. Newly confident readers will be swept away by the magical stories of three children whose courage and resourcefulness save a fantastical land from disaster. Full of all the things little girls love best: special friendships, secrets and magical adventures, all set in an incredible kingdom! Eye-catching illustrations throughout. Become best friends with Ellie, Summer and Jasmine - plus Trixi the pixie! Help Ellie, Summer and Jasmine save the Secret Kingdom from wicked Queen Malice and her naughty storm sprites. A new exciting adventure in each and every book.

This six-volume set presents cutting-edge advances and applications of expert systems. Because expert systems combine the expertise of engineers, computer scientists, and computer programmers, each group will benefit from buying this important reference work. An "expert system" is a knowledge-based computer system that emulates the decision-making ability of a human expert. The primary role of the expert system is to perform appropriate functions under the close supervision of the human, whose work is supported by that expert system. In the reverse, this same expert system can monitor and double check the human in the performance of a task. Human-computer interaction in our highly complex world requires the development of a wide array of expert systems. Expert systems techniques and applications are presented for a diverse array of topics including Experimental design and decision support The integration of machine learning with knowledge acquisition for the design of expert systems Process planning in design and manufacturing systems and process control applications Knowledge discovery in large-scale knowledge bases Robotic systems Geograhphic information systems Image analysis, recognition and interpretation Cellular automata methods for pattern recognition Real-time fault tolerant control systems CAD-based vision systems in pattern matching processes Financial systems Agricultural applications Medical diagnosis

"This book investigates the creation and implementation of enterprise information systems, covering a wide array of topics such as flow-shop scheduling, information systems outsourcing, ERP systems utilization, Dietz transaction methodology, and advanced planning systems"--Provided by publisher.

Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.