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Author: Nathalie Hernandez
Publisher: Springer
ISBN: 3319083899
Category : Computers
Languages : en
Pages : 323
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Book Description
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.
Author: Nathalie Hernandez
Publisher: Springer
ISBN: 3319083899
Category : Computers
Languages : en
Pages : 323
Get Book Here
Book Description
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.
Author: Tanya Braun
Publisher: Springer Nature
ISBN: 3030869822
Category : Computers
Languages : en
Pages : 231
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Book Description
This book constitutes the proceedings of the 26th International Conference on Conceptual Structures, ICCS 2021, held virtually in September 2021. The 12 full papers and 4 short papers 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. The papers are organized in the following topical sections: applications of conceptual structures; theory on conceptual structures, and mining conceptual structures.
Author: Ollivier Haemmerlé
Publisher: Springer
ISBN: 3319409859
Category : Computers
Languages : en
Pages : 266
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Book Description
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.
Author: Peter Chapman
Publisher: Springer
ISBN: 3319913794
Category : Computers
Languages : en
Pages : 207
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Book Description
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.
Author: Manuel Ojeda-Aciego
Publisher: Springer Nature
ISBN: 3031409604
Category : Computers
Languages : en
Pages : 213
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Book Description
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.
Author: Dominik Endres
Publisher: Springer
ISBN: 3030231828
Category : Computers
Languages : en
Pages : 288
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Book Description
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."
Author: Michel Chein
Publisher: Springer Science & Business Media
ISBN: 1848002866
Category : Mathematics
Languages : en
Pages : 428
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Book Description
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.
Author: William L. William L. Hamilton
Publisher: Springer Nature
ISBN: 3031015886
Category : Computers
Languages : en
Pages : 141
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Book Description
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.
Author: Michael Cochez
Publisher: Springer Nature
ISBN: 3030723089
Category : Computers
Languages : en
Pages : 158
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Book Description
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.
Author: I. Tiddi
Publisher: IOS Press
ISBN: 1643680811
Category : Computers
Languages : en
Pages : 314
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Book Description
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.
