Handbook of Knowledge Representation

Handbook of Knowledge Representation describes the essential foundations of Knowledge Representation, which lies at the core of Artificial Intelligence (AI). The book provides an up-to-date review of twenty-five key topics in knowledge representation, written by the leaders of each field. It includes a tutorial background and cutting-edge developments, as well as applications of Knowledge Representation in a variety of AI systems. This handbook is organized into three parts. Part I deals with general methods in Knowledge Representation and reasoning and covers such topics as classical logic in Knowledge Representation; satisfiability solvers; description logics; constraint programming; conceptual graphs; nonmonotonic reasoning; model-based problem solving; and Bayesian networks. Part II focuses on classes of knowledge and specialized representations, with chapters on temporal representation and reasoning; spatial and physical reasoning; reasoning about knowledge and belief; temporal action logics; and nonmonotonic causal logic. Part III discusses Knowledge Representation in applications such as question answering; the semantic web; automated planning; cognitive robotics; multi-agent systems; and knowledge engineering. This book is an essential resource for graduate students, researchers, and practitioners in knowledge representation and AI. * Make your computer smarter * Handle qualitative and uncertain information * Improve computational tractability to solve your problems easily

Knowledge representation, which lies at the core of artificial intelligence, is concerned with encoding knowledge on computers to enable systems to reason automatically. The aims are to help readers make their computer smarter, handle qualitative and uncertain information, and improve computational tractability.

What is a "heuristic problem-solving program?" How do computers understand English? What are "semantic nets" or "frames?" Can computer programs outperform human experts? Such questions -- asked by scientists, engineers, students, and hobbyists encountering Artificial Intelligence for the first time -- can now be readily answered by The Handbook of Artificial Intelligence, a work which makes the full scope of important techniques and concepts of AI available for the first time to the rapidly expanding world of computer technologists and users. The scope of this handbook is broad: over 200 short articles covering all of the important ideas, techniques, and systems developed during 25 years of research in the AI field. The articles are written for people with no background in AI. Some articles serve as overviews, discussing the various approaches within a subfield, the issues, and the problems. The handbook is a reference work, a textbook, a guide to programming techniques and to the extensive literature of the field, and a book for intellectual browsing. Jargon has been eliminated in each of the short, penetrating articles, and the hierarchical organization of the book allows readers to choose how deeply they wish to delve into a particular subject. Conceived and produced at Stanford University's Department of Computer Science, with contributions from universities and laboratories across the nation, The Handbook of Artificial Intelligence promises to become the standard reference work in the rapidly growing AI field. - Jacket.

Knowledge representation is at the very core of a radical idea for understanding intelligence. This book talks about the central concepts of knowledge representation developed over the years. It is suitable for researchers and practitioners in database management, information retrieval, object-oriented systems and artificial intelligence.

Table of contents

The Handbook of Artificial Intelligence, Volume I focuses on the progress in artificial intelligence (AI) and its increasing applications, including parsing, grammars, and search methods. The book first elaborates on AI, AI handbook and literature, problem representation, search methods, and sample search programs. The text then ponders on representation of knowledge, including survey of representation techniques and representation schemes. The manuscript explores understanding natural languages, as well as machine translation, grammars, parsing, test generation, and natural language processing systems. The book also takes a look at understanding spoken language, including systems architecture and the ARPA SUR projects. The text is a valuable source of information for computer science experts and researchers interested in pursuing further research in artificial intelligence.

Dealing with information is one of the vital skills in the 21st century. It takes a fair degree of information savvy to create, represent and supply information as well as to search for and retrieve relevant knowledge. How does information (documents, pieces of knowledge) have to be organized in order to be retrievable? What role does metadata play? What are search engines on the Web, or in corporate intranets, and how do they work? How must one deal with natural language processing and tools of knowledge organization, such as thesauri, classification systems, and ontologies? How useful is social tagging? How valuable are intellectually created abstracts and automatically prepared extracts? Which empirical methods allow for user research and which for the evaluation of information systems? This Handbook is a basic work of information science, providing a comprehensive overview of the current state of information retrieval and knowledge representation. It addresses readers from all professions and scientific disciplines, but particularly scholars, practitioners and students of Information Science, Library Science, Computer Science, Information Management, and Knowledge Management. This Handbook is a suitable reference work for Public and Academic Libraries.

Knowledge representation is fundamental to the study of mind. All theories of psychological processing are rooted in assumptions about how information is stored. These assumptions, in turn, influence the explanatory power of theories. This book fills a gap in the existing literature by providing an overview of types of knowledge representation techniques and their use in cognitive models. Organized around types of representations, this book begins with a discussion of the foundations of knowledge representation, then presents discussions of different ways that knowledge representation has been used. Both symbolic and connectionist approaches to representation are discussed and a set of recommendations about the way representations should be used is presented. This work can be used as the basis for a course on knowledge representation or can be read independently. It will be useful to students of psychology as well as people in related disciplines--computer science, philosophy, anthropology, and linguistics--who want an introduction to techniques for knowledge representation.

The Handbook of Applied Expert Systems is a landmark work dedicated solely to this rapidly advancing area of study. Edited by Jay Liebowitz, a professor, author, and consultant known around the world for his work in the field, this authoritative source covers the latest expert system technologies, applications, methodologies, and practices. The book features contributions from more than 40 of the world's foremost expert systems authorities in industry, government, and academia. The Handbook is organized into two major sections. The first section explains expert systems technologies while the second section focuses on applied examples in a wide variety of industries. Key topics covered include fuzzy systems, genetic algorithm development, machine learning, knowledge representation, and much more.

The papers collected in this book cover a wide range of topics in asymptotic statistics. In particular up-to-date-information is presented in detection of systematic changes, in series of observation, in robust regression analysis, in numerical empirical processes and in related areas of actuarial sciences and mathematical programming. The emphasis is on theoretical contributions with impact on statistical methods employed in the analysis of experiments and observations by biometricians, econometricians and engineers.

In and out of formal schooling, online and off, today’s learners must consume and integrate a level of information that is exponentially larger and delivered through a wider range of formats and viewpoints than ever before. The Handbook of Learning from Multiple Representations and Perspectives provides a path for understanding the cognitive, motivational, and socioemotional processes and skills necessary for learners across educational contexts to make sense of and use information sourced from varying inputs. Uniting research and theory from education, psychology, literacy, library sciences, media and technology, and more, this forward-thinking volume explores the common concerns, shared challenges, and thematic patterns in our capacity to make meaning in an information-rich society.

In Artificial Intelligence, it is often said that the representation of knowledge is the key to the design of robust intelligent systems. In one form or another the principles of Knowledge Representation are fundamental to work in natural language processing, computer vision, knowledge-based expert systems, and other areas. The papers reprinted in this volume have been collected to allow the reader with a general technical background in AI to explore the subtleties of this key subarea. These seminal articles, spanning a quarter-century of research, cover the most important ideas and developments in the representation field. The editors introduce each paper, discuss its relevance and context, and provide an extensive bibliography of other work. "Readings in Knowledge Representation" is intended to serve as a complete sourcebook for the study of this crucial subject.

An authoritative, up-to-date survey of the state of the art in artificial intelligence, written for non-specialists.

"The new discipline of chemoinformatics covers the application of computer-assisted methods to chemical problems such as information storage and retrieval, the prediction of physical, chemical or biological properties of compounds, spectra simulation, structure elucidation, reaction modeling, synthesis planning and drug design. ... this four-volume Handbook contains in-depth contributions from top authors from around the world, with the content organized into chapters dealing with the representation of molecular structures and reactions, data types and databases/data sources, search methods, methods for data analysis as well as applications"--Back cover.

An ontology is a description (like a formal specification of a program) of concepts and relationships that can exist for an agent or a community of agents. The concept is important for the purpose of enabling knowledge sharing and reuse. The Handbook on Ontologies provides a comprehensive overview of the current status and future prospectives of the field of ontologies. The handbook demonstrates standards that have been created recently, it surveys methods that have been developed and it shows how to bring both into practice of ontology infrastructures and applications that are the best of their kind.

This book consists of various contributions in conjunction with the keywords “reasoning” and “intelligent systems”, which widely covers theoretical to practical aspects of intelligent systems. Therefore, it is suitable for researchers or graduate students who want to study intelligent systems generally.

As the most comprehensive reference work dealing with knowledge management (KM), this work, consisting of 2 volumes, is essential for the library of every KM practitioner, researcher, and educator. Written by an international array of KM luminaries, its approx. 60 chapters approach knowledge management from a wide variety of perspectives ranging from classic foundations to cutting-edge thought, informative to provocative, theoretical to practical, historical to futuristic, human to technological, and operational to strategic. Novices and experts alike will refer to the authoritative and stimulating content again and again for years to come.

Recording knowledge in a common framework that would make it possible to seamlessly share global knowledge remains an important challenge for researchers. This brief examines several ideas about the representation of knowledge addressing this challenge. A widespread general agreement is followed that states uniform knowledge representation should be achievable by using ontologies populated with concepts. A separate chapter is dedicated to each of the three introduced topics, following a uniform outline: definition, organization, and use. This brief is intended for those who want to get to know the field of knowledge representation quickly, or would like to be up to date with current developments in the field. It is also useful for those dealing with implementation as examples of numerous operational systems are also given.

This book constitutes the thoroughly refereed post-conference proceedings of the 5th International Workshop on Graph Structures for Knowledge Representation and Reasoning, GKR 2017, held in Melbourne, VIC, Australia, in August 2017, associated with IJCAI 2017, the 26th International Joint Conference on Artificial Intelligence. The 7 revised full papers presented were reviewed and selected from 9 submissions. The contributions address various issues for knowledge representation and reasoning and the common graph-theoretic background allows to bridge the gap between the different communities.

Provides the requisite theoretical and methodological guidelines for undertaking social research addressing relevant contemporary social issues.