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Natural language understanding systems require a knowledge base provided with formal representations reflecting the structure of human beings' cognitive system. Although surface semantics can be sufficient in some other systems, the construction of a robust knowledge base guarantees its use in most natural language processing applications, thus consolidating the concept of resource reuse. This conference deals with meaning and knowledge representation in the context of natural language understanding from the perspective of theoretical linguistics, computational linguistics, cognitive science, knowledge engineering, artificial intelligence, natural language processing, text analytics or linked data and semantic web technologies.
Embeddings have undoubtedly been one of the most influential research areas in Natural Language Processing (NLP). Encoding information into a low-dimensional vector representation, which is easily integrable in modern machine learning models, has played a central role in the development of NLP. Embedding techniques initially focused on words, but the attention soon started to shift to other forms: from graph structures, such as knowledge bases, to other types of textual content, such as sentences and documents. This book provides a high-level synthesis of the main embedding techniques in NLP, in the broad sense. The book starts by explaining conventional word vector space models and word embeddings (e.g., Word2Vec and GloVe) and then moves to other types of embeddings, such as word sense, sentence and document, and graph embeddings. The book also provides an overview of recent developments in contextualized representations (e.g., ELMo and BERT) and explains their potential in NLP. Throughout the book, the reader can find both essential information for understanding a certain topic from scratch and a broad overview of the most successful techniques developed in the literature.
This book offers a coherent theoretical and multimodal perspective on research, teaching and learning in different non-formal, semi-formal, and formal learning environments. Drawing on examples across a range of different settings, the book provides a conceptual framework for research on learning in different environments. It provides conceptual models around learning design which act as a framework for how to think about contemporary learning, a guideline for how to do research on learning in different sites, and a tool for innovative, collaborative design with other professionals. The book highlights concepts like multimodal knowledge representations; framing and setting; transformation, transduction, and re-design; signs of learning and cultures of recognition in different social contexts. The book supports innovative thinking on how we understand learning, and will appeal to academics, scholars and post graduate students in the field of education research and theory, learning sciences, and multimodal and social semiotics. It will also be of interest to school leaders, university provosts and professionals working in education.
This volume contains a selection of the best papers presented at the 8th International Conference on Industrial Engineering and Industrial Management, XX International Conference on Industrial Engineering and Operations Management, and International IIE Conference 2014, hosted by ADINGOR, ABEPRO and the IIE, whose mission is to promote links between researchers and practitioners from different branches, to enhance an interdisciplinary perspective of industrial engineering and management. The conference topics covered: operations research, modelling and simulation, computer and information systems, operations research, scheduling and sequencing, logistics, production and information systems, supply chain and logistics, transportation, lean management, production planning and control, production system design, reliability and maintenance, quality management, sustainability and eco-efficiency, marketing and consumer behavior, business administration and strategic management, economic and financial management, technological and organizational innovation, strategy and entrepreneurship, economics engineering, enterprise engineering, global operations and cultural factors, operations strategy and performance, management social responsibility, environment and sustainability. This book will be of interest to researchers and practitioners working in any of the fields mentioned above.
This book studies learning as a social enterprise, contextually situated, organized and assessed. It gives a broad theoretic grounding for an understanding of learning which goes beyond a common reductionist approach. The book discusses four related approaches to learning which share a social perspective: social semiotics and multimodality; a design-theoretic approach to learning; a socio-cultural perspective; and a perspective of mimetic learning. Contributing authors consider the theoretical question of how to understand educational systems, learning and social communication as historically situated practices. The chapters in this book analyze key working practices including: analyzing what learning, remembering and cognitive work is like in a practice involving different kinds of expertise; problem-solving and engaging through collaboration; learning and teaching in different formal, semi- and non-formal environments; a design-theoretic approach to learning; social semiotic perspectives on learning; the mimetic and ritual dimension of learning; how social learning can be organized to support students; how learning has been conceptualized in psychology and neighbouring research areas. This book will be of great interest to students and researchers interested in learning and meaning-making, multimodal representations and communication, designs for learning, education and teaching, and social achievement in different formal and non-formal contexts.
Reasoning about knowledge—particularly the knowledge of agents who reason about the world and each other's knowledge—was once the exclusive province of philosophers and puzzle solvers. More recently, this type of reasoning has been shown to play a key role in a surprising number of contexts, from understanding conversations to the analysis of distributed computer algorithms. Reasoning About Knowledge is the first book to provide a general discussion of approaches to reasoning about knowledge and its applications to distributed systems, artificial intelligence, and game theory. It brings eight years of work by the authors into a cohesive framework for understanding and analyzing reasoning about knowledge that is intuitive, mathematically well founded, useful in practice, and widely applicable. The book is almost completely self-contained and should be accessible to readers in a variety of disciplines, including computer science, artificial intelligence, linguistics, philosophy, cognitive science, and game theory. Each chapter includes exercises and bibliographic notes.
This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale. The book defines knowledge graphs and provides a high-level overview of how they are used. It presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried before describing how the resulting data graph can be enhanced with notions of schema, identity, and context. The book discusses how ontologies and rules can be used to encode knowledge as well as how inductive techniques—based on statistics, graph analytics, machine learning, etc.—can be used to encode and extract knowledge. It covers techniques for the creation, enrichment, assessment, and refinement of knowledge graphs and surveys recent open and enterprise knowledge graphs and the industries or applications within which they have been most widely adopted. The book closes by discussing the current limitations and future directions along which knowledge graphs are likely to evolve. This book is aimed at students, researchers, and practitioners who wish to learn more about knowledge graphs and how they facilitate extracting value from diverse data at large scale. To make the book accessible for newcomers, running examples and graphical notation are used throughout. Formal definitions and extensive references are also provided for those who opt to delve more deeply into specific topics.
Formal ways of representing uncertainty and various logics for reasoning about it; updated with new material on weighted probability measures, complexity-theoretic considerations, and other topics. In order to deal with uncertainty intelligently, we need to be able to represent it and reason about it. In this book, Joseph Halpern examines formal ways of representing uncertainty and considers various logics for reasoning about it. While the ideas presented are formalized in terms of definitions and theorems, the emphasis is on the philosophy of representing and reasoning about uncertainty. Halpern surveys possible formal systems for representing uncertainty, including probability measures, possibility measures, and plausibility measures; considers the updating of beliefs based on changing information and the relation to Bayes' theorem; and discusses qualitative, quantitative, and plausibilistic Bayesian networks. This second edition has been updated to reflect Halpern's recent research. New material includes a consideration of weighted probability measures and how they can be used in decision making; analyses of the Doomsday argument and the Sleeping Beauty problem; modeling games with imperfect recall using the runs-and-systems approach; a discussion of complexity-theoretic considerations; the application of first-order conditional logic to security. Reasoning about Uncertainty is accessible and relevant to researchers and students in many fields, including computer science, artificial intelligence, economics (particularly game theory), mathematics, philosophy, and statistics.
History of Analytical Chemistry is a systematic account of the historical development of analytical chemistry spanning about 4,000 years. Many scientists who have helped to develop the methods of analytical chemistry are mentioned. Various methods of analysis are discussed, including electrogravimetry, optical methods, electrometric analysis, radiochemical analysis, and chromatography. This volume is comprised of 14 chapters and begins with an overview of analytical chemistry in ancient Greece, the origin of chemistry, and the earliest knowledge of analysis. The next chapter focuses on analytical chemistry during the Middle Ages, with emphasis on alchemy. Analytical knowledge during the period of iatrochemistry and the development of analytical chemistry during the phlogiston period are then examined. Subsequent chapters deal with the development of the fundamental laws of chemistry, including the principle of the indestructibility of matter; analytical chemistry during the period of Berzelius; and developments in qualitative and gravimetric analysis. Elementary organic analysis is also considered, along with the development of the theory of analytical chemistry. This book will be helpful to chemists as well as students and researchers in the field of analytical chemistry.