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Graph Structures for Knowledge Representation and Reasoning: 4th International Workshop, GKR 2015, Buenos Aires, Argentina, July 25, 2015, Revised Selected Papers de Madalina Croitoru

Graph Structures for Knowledge Representation and Reasoning: 4th International Workshop, GKR 2015, Buenos Aires, Argentina, July 25, 2015, Revised Selected Papers: Lecture Notes in Computer Science, cartea 9501

Editat de Madalina Croitoru, Pierre Marquis, Sebastian Rudolph, Gem Stapleton
en Limba Engleză Paperback – 3 feb 2016
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.



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Specificații

ISBN-13: 9783319287010
ISBN-10: 331928701X
Pagini: 155
Ilustrații: VII, 155 p. 48 illus. in color.
Dimensiuni: 155 x 235 x 9 mm
Greutate: 0.24 kg
Ediția:1st ed. 2015
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence

Locul publicării:Cham, Switzerland

Public țintă

Research

Cuprins

Designing a Knowledge Representation Tool for SubjectMatter Structuring.- Aligning Experientially Grounded Ontologies using LanguageGames.- An overview of argumentation frameworks for decision support.- LearningOptimal Bayesian Networks with DAG Graphs.- Combinatorial results on directedhypergraphs for the SAT problem.- Conceptual Graphs for Formally Managing andDiscovering Complementary Competences.- Subjective Networks: Perspectives andChallenges.- RDF-SQ: Mixing Parallel and Sequential Computation For Top-downOWL RL Inference.- Bring User Interest to Related Entity Recommendation.

Caracteristici

Includes supplementary material: sn.pub/extras