Understanding Meaning And Knowledge Representation

Download or read book A Knowledge Representation Practionary written by Michael K. Bergman and published by Springer. This book was released on 2018-12-12 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: This major work on knowledge representation is based on the writings of Charles S. Peirce, a logician, scientist, and philosopher of the first rank at the beginning of the 20th century. This book follows Peirce's practical guidelines and universal categories in a structured approach to knowledge representation that captures differences in events, entities, relations, attributes, types, and concepts. Besides the ability to capture meaning and context, the Peircean approach is also well-suited to machine learning and knowledge-based artificial intelligence. Peirce is a founder of pragmatism, the uniquely American philosophy. Knowledge representation is shorthand for how to represent human symbolic information and knowledge to computers to solve complex questions. KR applications range from semantic technologies and knowledge management and machine learning to information integration, data interoperability, and natural language understanding. Knowledge representation is an essential foundation for knowledge-based AI. This book is structured into five parts. The first and last parts are bookends that first set the context and background and conclude with practical applications. The three main parts that are the meat of the approach first address the terminologies and grammar of knowledge representation, then building blocks for KR systems, and then design, build, test, and best practices in putting a system together. Throughout, the book refers to and leverages the open source KBpedia knowledge graph and its public knowledge bases, including Wikipedia and Wikidata. KBpedia is a ready baseline for users to bridge from and expand for their own domain needs and applications. It is built from the ground up to reflect Peircean principles. This book is one of timeless, practical guidelines for how to think about KR and to design knowledge management (KM) systems. The book is grounded bedrock for enterprise information and knowledge managers who are contemplating a new knowledge initiative. This book is an essential addition to theory and practice for KR and semantic technology and AI researchers and practitioners, who will benefit from Peirce's profound understanding of meaning and context.

Download or read book Understanding Meaning and Knowledge Representation written by Eva Mestre Mestre and published by Cambridge Scholars Publishing. This book was released on 2016-01-14 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today, there is a need to develop natural language processing (NLP) systems from deeper linguistic approaches. Although there are many NLP applications which can work without taking into account any linguistic theory, this type of system can only be described as “deceptively intelligent”. On the other hand, however, those computer programs requiring some language comprehension capability should be grounded in a robust linguistic model if they are to display the expected behaviour. The purpose of this book is to examine and discuss recent work in meaning and knowledge representation within theoretical linguistics and cognitive linguistics, particularly research which can be reused to model NLP applications.

Download or read book Knowledge Representation and the Semantics of Natural Language written by Hermann Helbig and published by Springer Science & Business Media. This book was released on 2005-12-19 with total page 652 pages. Available in PDF, EPUB and Kindle. Book excerpt: Natural Language is not only the most important means of communication between human beings, it is also used over historical periods for the pres- vation of cultural achievements and their transmission from one generation to the other. During the last few decades, the ?ood of digitalized information has been growing tremendously. This tendency will continue with the globali- tion of information societies and with the growing importance of national and international computer networks. This is one reason why the theoretical und- standing and the automated treatment of communication processes based on natural language have such a decisive social and economic impact. In this c- text, the semantic representation of knowledge originally formulated in natural language plays a central part, because it connects all components of natural language processing systems, be they the automatic understanding of natural language (analysis), the rational reasoning over knowledge bases, or the g- eration of natural language expressions from formal representations. This book presents a method for the semantic representation of natural l- guage expressions (texts, sentences, phrases, etc. ) which can be used as a u- versal knowledge representation paradigm in the human sciences, like lingu- tics, cognitive psychology, or philosophy of language, as well as in com- tational linguistics and in arti?cial intelligence. It is also an attempt to close the gap between these disciplines, which to a large extent are still working separately.

Download or read book Knowledge Representation and Reasoning written by Ronald Brachman and published by Morgan Kaufmann. This book was released on 2004-05-19 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Representation Learning for Natural Language Processing written by Zhiyuan Liu and published by Springer Nature. This book was released on 2020-07-03 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.

Download or read book Understanding Representation written by Jen Webb and published by SAGE. This book was released on 2008-12-01 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This is an extraordinarily lucid book. I am not sure that there is anyone who can do this sort of thing better than Jen Webb. It is a gift to students; extremely accessible yet complex and sophisticated in its treatment of theories and concepts of representation." - Jim McGuigan, Loughborough University Understanding Representation offers a contemporary, coherent and genuinely interdisciplinary introduction to the concept of representation. Drawing together the full range of ideas, practices, techniques and disciplines associated with the subject, this book locates them in a historical context, presents them in a readable fashion, and shows their relevance to everyday life in an engaging and accessible manner. Readers will be shown how to develop a sophisticated attitude to meaning, and understand the relationship to truth and identity that is brought into focus by communicative practices. With chapters on linguistic and political representation, art and media, and philosophical and cognitive approaches, this book: Guides readers through complex theoretical terrain with a highly readable and refreshing writing style. Explains the techniques and perspectives offered by semiotics, discourse analysis, poetics, politics, narratology, visual culture, cognitive theory, performance theory and theories of embodied subjectivity. Covers the new ideas and practices that have emerged since the work of Barthes, Eco and Foucault - especially communication and meaning-making in the digital environment, and the new paradigms of understanding associated with cognitive theories of identity and language. Teaches readers how to interpret and interrogate the world of signs in which they live. Understanding Representation provides students across the social sciences and humanities with an invaluable introduction to what is meant by ′representation′.

Download or read book Handbook of Knowledge Representation written by Frank van Harmelen and published by Elsevier. This book was released on 2008-01-08 with total page 1035 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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