Graph Based Representation And Reasoning
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Graph Based Representation And Reasoning
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Author : Tanya Braun
language : en
Publisher: Springer Nature
Release Date : 2022-09-19
Graph Based Representation And Reasoning written by Tanya Braun and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-19 with Computers categories.
This book constitutes the proceedings of the 27th International Conference on Conceptual Structures, ICCS 2022, held virtually in September 2022. The 7 full papers and 1 short paper presented were carefully reviewed and selected from 25 submissions. The papers focus on the representation of and reasoning with conceptual structures in a variety of contexts.
Graph Based Representation And Reasoning
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Author : Manuel Ojeda-Aciego
language : en
Publisher: Springer Nature
Release Date : 2023-08-15
Graph Based Representation And Reasoning written by Manuel Ojeda-Aciego and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-15 with Computers categories.
This book constitutes the refereed deadline proceedings of the 28th International Conference on Graph-Based Representation and Reasoning, ICCS 2023, held in Berlin, Germany, during September 11–13, 2023. The 9 full papers, 5 short papers and 4 Posters are included in this book were carefully reviewed and selected from 32 submissions. They were organized in topical sections as follows: Complexity and Database Theory, Formal Concept Analysis: Theoretical Advances, Formal Concept Analysis: Applications, Modelling and Explanation, Semantic Web and Graphs, Posters.
Graph Based Representation And Reasoning
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Author : Nathalie Hernandez
language : en
Publisher: Springer
Release Date : 2014-07-17
Graph Based Representation And Reasoning written by Nathalie Hernandez and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-17 with Computers categories.
This book constitutes the proceedings of the 21st International Conference on Conceptual Structures, ICCS 2014, held in Iaşi, Romania, in July 2014. The 17 regular papers and 6 short papers presented in this volume were carefully reviewed and selected from 40 and 10 submissions, respectively. The topics covered are: conceptual structures, knowledge representation, reasoning, conceptual graphs, formal concept analysis, semantic Web, information integration, machine learning, data mining and information retrieval.
Graph Based Representation And Reasoning
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Author : Peter Chapman
language : en
Publisher: Springer
Release Date : 2018-06-07
Graph Based Representation And Reasoning written by Peter Chapman and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-07 with Computers categories.
This book constitutes the proceedings of the 23rd International Conference on Conceptual Structures, ICCS 2018, held in Edinburgh, UK, in June 2018. The 10 full papers, 2 short papers and 2 posters presented were carefully reviewed and selected from 21 submissions. They are organized in the following topical sections: graph- and concept-based inference; computer- human interaction and human cognition; and graph visualization.
Graph Based Representation And Reasoning
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Author : Ollivier Haemmerlé
language : en
Publisher: Springer
Release Date : 2016-06-10
Graph Based Representation And Reasoning written by Ollivier Haemmerlé and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-10 with Computers categories.
This book constitutes the proceedings of the 22th International Conference on Conceptual Structures, ICCS 2016, held in Annecy, France, in July 2016. The 14 full papers and 5 short papers presented in this volume were carefully reviewed and selected from 40 submissions. They are organized around the following topical sections: time representation; graphs and networks; formal concept analysis; ontologies and linked data.
Graph Based Representation And Reasoning
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Author : Dominik Endres
language : en
Publisher: Springer
Release Date : 2019-06-24
Graph Based Representation And Reasoning written by Dominik Endres and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-24 with Computers categories.
This book constitutes the proceedings of the 24th International Conference on Conceptual Structures, ICCS 2019, held in Marburg, Germany, in July 2019. The 14 full papers and 6 short papers presented were carefully reviewed and selected from 29 submissions. The proceedings also include one of the two invited talks. The papers focus on the representation of and reasoning with conceptual structures in a variety of contexts. ICCS 2019's theme was entitled "Graphs in Human and Machine Cognition."
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 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.
Knowledge Graphs For Explainable Artificial Intelligence Foundations Applications And Challenges
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Author : Ilaria Tiddi
language : en
Publisher:
Release Date : 2020
Knowledge Graphs For Explainable Artificial Intelligence Foundations Applications And Challenges written by Ilaria Tiddi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Artificial intelligence categories.
The latest advances in Artificial Intelligence and (deep) Machine Learning in particular revealed a major drawback of modern intelligent systems, namely the inability to explain their decisions in a way that humans can easily understand. While eXplainable AI rapidly became an active area of research in response to this need for improved understandability and trustworthiness, the field of Knowledge Representation and Reasoning (KRR) has on the other hand a long-standing tradition in managing information in a symbolic, human-understandable form. This book provides the first comprehensive collection of research contributions on the role of knowledge graphs for eXplainable AI (KG4XAI), and the papers included here present academic and industrial research focused on the theory, methods and implementations of AI systems that use structured knowledge to generate reliable explanations. Introductory material on knowledge graphs is included for those readers with only a minimal background in the field, as well as specific chapters devoted to advanced methods, applications and case-studies that use knowledge graphs as a part of knowledge-based, explainable systems (KBX-systems). The final chapters explore current challenges and future research directions in the area of knowledge graphs for eXplainable AI. The book not only provides a scholarly, state-of-the-art overview of research in this subject area, but also fosters the hybrid combination of symbolic and subsymbolic AI methods, and will be of interest to all those working in the field.
Graph Based Representations In Pattern Recognition
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Author : Francisco Escolano
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-05-31
Graph Based Representations In Pattern Recognition written by Francisco Escolano 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 2007-05-31 with Computers categories.
This book constitutes the refereed proceedings of the 6th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2007, held in Alicante, Spain in June 2007. The 23 revised full papers and 14 revised poster papers presented were carefully reviewed and selected from 54 submissions. The papers are organized in topical sections on matching, distances and measures, graph-based segmentation and image processing, graph-based clustering, graph representations, pyramids, combinatorial maps and homologies, as well as graph clustering, embedding and learning.