Graph Structures For Knowledge Representation And Reasoning
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Graph Structures For Knowledge Representation And Reasoning
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Author : Madalina Croitoru
language : en
Publisher: Springer
Release Date : 2014-01-21
Graph Structures For Knowledge Representation And Reasoning written by Madalina Croitoru and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-21 with Computers categories.
This book constitutes the thoroughly refereed post-conference proceedings of the Third International Workshop on Graph Structures for Knowledge Representation and Reasoning, GKR 2013, held in Beijing, China, in August 2013, associated with IJCAI 2013, the 23rd International Joint Conference on Artificial Intelligence. The 12 revised full papers presented were carefully reviewed and selected for inclusion in the book. The papers feature current research involved in the development and application of graph-based knowledge representation formalisms and reasoning techniques. They address the following topics: representations of constraint satisfaction problems; formal concept analysis; conceptual graphs; and argumentation frameworks.
Graph Structures For Knowledge Representation And Reasoning
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Author : Madalina Croitoru
language : en
Publisher: Springer
Release Date : 2018-03-29
Graph Structures For Knowledge Representation And Reasoning written by Madalina Croitoru and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-29 with Computers categories.
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.
Graph Structures For Knowledge Representation And Reasoning
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Author : Michael Cochez
language : en
Publisher: Springer Nature
Release Date : 2021-04-16
Graph Structures For Knowledge Representation And Reasoning written by Michael Cochez and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-16 with Computers categories.
This open access book constitutes the thoroughly refereed post-conference proceedings of the 6th International Workshop on Graph Structures for Knowledge Representation and Reasoning, GKR 2020, held virtually in September 2020, associated with ECAI 2020, the 24th European Conference on Artificial Intelligence. The 7 revised full papers presented together with 2 invited contributions were reviewed and selected from 9 submissions. The contributions address various issues for knowledge representation and reasoning and the common graph-theoretic background, which allows to bridge the gap between the different communities.
Graph Structures For Knowledge Representation And Reasoning
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Author : Madalina Croitoru
language : en
Publisher: Springer
Release Date : 2012-05-27
Graph Structures For Knowledge Representation And Reasoning written by Madalina Croitoru and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-05-27 with Computers categories.
This book constitutes the thoroughly refereed post-conference proceedings of the Second International Workshop on Graph Structures for Knowledge Representation and Reasoning, GKR 2011, held in Barcelona, Spain, in July 2011 as satellite event of IJCAI 2011, the 22nd International Joint Conference on Artificial Intelligence. The 7 revised full papers presented together with 1 invited paper were carefully reviewed and selected from 12 submissions. The papers feature current research involved in the development and application of graph-based knowledge representation formalisms and reasoning techniques and investigate further developments of knowledge representation and reasoning graph based techniques. Topics addressed are such as: bayesian networks, semantic networks, conceptual graphs, formal concept analysis, cp-nets, gai-nets, euler diagrams, existential graphs all of which have been successfully used in a number of applications (semantic Web, recommender systems, bioinformatics etc.).
Graph Structures For Knowledge Representation And Reasoning
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Author : Madalina Croitoru
language : en
Publisher: Springer
Release Date : 2016-01-02
Graph Structures For Knowledge Representation And Reasoning written by Madalina Croitoru and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-02 with Computers categories.
This book constitutes the thoroughly refereed post-conference proceedings of the 4th International Workshop on Graph Structures for Knowledge Representation and Reasoning, GKR 2015, held in Buenos Aires, Argentina, in July 2015, associated with IJCAI 2015, the 24th International Joint Conference on Artificial Intelligence. The 9 revised full papers presented were carefully reviewed and selected from 10 submissions. The papers feature current research involved in the development and application of graph-based knowledge representation formalisms and reasoning techniques. They address the following topics: argumentation; conceptual graphs; RDF; and representations of constraint satisfaction problems.
Graph Based Knowledge Representation
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Author : Michel Chein
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-10-20
Graph Based Knowledge Representation written by Michel Chein and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-10-20 with Mathematics categories.
This book provides a de?nition and study of a knowledge representation and r- soning formalism stemming from conceptual graphs, while focusing on the com- tational properties of this formalism. Knowledge can be symbolically represented in many ways. The knowledge representation and reasoning formalism presented here is a graph formalism – knowledge is represented by labeled graphs, in the graph theory sense, and r- soning mechanisms are based on graph operations, with graph homomorphism at the core. This formalism can thus be considered as related to semantic networks. Since their conception, semantic networks have faded out several times, but have always returned to the limelight. They faded mainly due to a lack of formal semantics and the limited reasoning tools proposed. They have, however, always rebounded - cause labeled graphs, schemas and drawings provide an intuitive and easily und- standable support to represent knowledge. This formalism has the visual qualities of any graphic model, and it is logically founded. This is a key feature because logics has been the foundation for knowledge representation and reasoning for millennia. The authors also focus substantially on computational facets of the presented formalism as they are interested in knowledge representation and reasoning formalisms upon which knowledge-based systems can be built to solve real problems. Since object structures are graphs, naturally graph homomorphism is the key underlying notion and, from a computational viewpoint, this moors calculus to combinatorics and to computer science domains in which the algorithmicqualitiesofgraphshavelongbeenstudied,asindatabasesandconstraint networks.
Graph Structures For Knowledge Representation And Reasoning
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Author : Michael Cochez
language : en
Publisher:
Release Date : 2021
Graph Structures For Knowledge Representation And Reasoning written by Michael Cochez and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
This open access book constitutes the thoroughly refereed post-conference proceedings of the 6th International Workshop on Graph Structures for Knowledge Representation and Reasoning, GKR 2020, held virtually in September 2020, associated with ECAI 2020, the 24th European Conference on Artificial Intelligence. The 7 revised full papers presented together with 2 invited contributions were reviewed and selected from 9 submissions. The contributions address various issues for knowledge representation and reasoning and the common graph-theoretic background, which allows to bridge the gap between the different communities.
Representation Learning For Natural Language Processing
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Author : Zhiyuan Liu
language : en
Publisher: Springer Nature
Release Date : 2020-07-03
Representation Learning For Natural Language Processing written by Zhiyuan Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-03 with Computers categories.
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.
An Introduction To Knowledge Graphs
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Author : Umutcan Serles
language : en
Publisher: Springer Nature
Release Date : 2024-06-08
An Introduction To Knowledge Graphs written by Umutcan Serles and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-08 with Computers categories.
This textbook introduces the theoretical foundations of technologies essential for knowledge graphs. It also covers practical examples, applications and tools. Knowledge graphs are the most recent answer to the challenge of providing explicit knowledge about entities and their relationships by potentially integrating billions of facts from heterogeneous sources. The book is structured in four parts. For a start, Part I lays down the overall context of knowledge graph technology. Part II “Knowledge Representation” then provides a deep understanding of semantics as the technical core of knowledge graph technology. Semantics is covered from different perspectives, such as conceptual, epistemological and logical. Next, Part III “Knowledge Modelling” focuses on the building process of knowledge graphs. The book focuses on the phases of knowledge generation, knowledge hosting, knowledge assessment, knowledge cleaning, knowledge enrichment, and knowledge deployment to cover a complete life cycle for this process. Finally, Part IV (simply called “Applications”) presents various application areas in detail with concrete application examples as well as an outlook on additional trends that will emphasize the need for knowledge graphs even stronger. This textbook is intended for graduate courses covering knowledge graphs. Besides students in knowledge graph, Semantic Web, database, or information retrieval classes, also advanced software developers for Web applications or tools for Web data management will learn about the foundations and appropriate methods.
Graph Representation Learning
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Author : William L. Hamilton
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Graph Representation Learning written by William L. Hamilton and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.