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Graph Structures for Knowledge Representation and Reasoning

papers feature current research involved in the development and application of graph-based knowledge representation formalisms and reasoning;

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Graph Structures for Knowledge Representation and Reasoning

papers feature current research involved in the development and application of graph-based knowledge representation formalisms and reasoning;

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Graph-based Knowledge Representation

representation and reasoning formalisms upon which knowledge-based systems can be built to solve real problems. Since object structures are graphs;

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Graph Structures for Knowledge Representation and Reasoning

submissions. The contributions address various issues for knowledge representation and reasoning and the common graph-theoretic background, which;

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Graph Structures for Knowledge Representation and Reasoning

contributions address various issues for knowledge representation and reasoning and the common graph-theoretic background allows to bridge the gap;

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Graph-Based Representation and Reasoning

, respectively. The topics covered are: conceptual structures, knowledge representation, reasoning, conceptual graphs, formal concept analysis, semantic;

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Graph-based Representation and Reasoning

are organized around the following topical sections: time representation; graphs and networks; formal concept analysis; ontologies and linked;

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Conceptual Structures: Leveraging Semantic Technologies

knowledge representation and reasoning with conceptual graphs, over the years ICCS has broadened its scope to include in- vations from a wider range;

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Knowledge Graph and Semantic Computing: Knowledge Graph Empowers New Infrastructure Construction

submissions. The papers are organized in topical sections on knowledge extraction: knowledge graph representation and reasoning; knowledge;

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Knowledge Graph and Semantic Computing: Knowledge Computing and Language Understanding

wide research fields including the knowledge graph, the semantic Web, linked data, NLP, information extraction, knowledge representation and;

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Graph-Based Representations in Pattern Recognition

submissions. The accepted papers cover diverse issues of graph-based methods and applications, with 7 in graph representation, 15 in graph matching;

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Graph Representation Learning

, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation;

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Conceptual Structures: From Information to Intelligence

held in Europe, A- tralia, and North America since 1993. The focus of the conference has been the representation and analysis of conceptual;

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Graph-Based Representations in Pattern Recognition

This book constitutes the refereed proceedings of the 12th IAPR-TC-15 International Workshop on Graph-Based Representation in Pattern;

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Reasoning with Probabilistic and Deterministic Graphical Models

for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform;

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Reasoning with Probabilistic and Deterministic Graphical Models

for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform;

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Reasoning with Probabilistic and Deterministic Graphical Models

for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform;

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Knowledge Representation

This collection of extended versions of 12 papers from the First International Conference on Principles of Knowledge Representation and;

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Graph Classification And Clustering Based On Vector Space Embedding

volume utilizes the dissimilarity space representation originally proposed by Duin and Pekalska to embed graphs in real vector spaces. Such an;

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Graph-Based Representations in Pattern Recognition

This book constitutes the refereed proceedings of the 11th IAPR-TC-15 International Workshop on Graph-Based Representation in Pattern;

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AAAI-90

in artificial intelligence.AI and Education. Automated Reasoning: automatic programming, planning and scheduling, rule-based reasoning, search, theorem;

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Handbook of Knowledge Representation

; conceptual graphs; nonmonotonic reasoning; model-based problem solving; and Bayesian networks. Part II focuses on classes of knowledge and;

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Knowledge Representation Reasoning & The

Knowledge representation and reasoning is the foundation of artificial intelligence, declarative programming, and the design of knowledge;

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A Tour through Graph Theory

questions. The book inspires readers to learn by working through examples, drawing graphs and exploring concepts. This book distinguishes itself;

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A Tour through Graph Theory

questions. The book inspires readers to learn by working through examples, drawing graphs and exploring concepts. This book distinguishes itself;

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Heterogeneous Graph Representation Learning and Applications

to summarize the latest developments and present cutting-edge research on heterogeneous graph representation learning. To gain the most from;

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Constraint-Based Reasoning

understanding, qualitative and diagnostic reasoning, and expert systems. Constraint-Based Reasoning presents current work in the field at several levels;

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