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Title
Graph structures for knowledge representation and reasoning [electronic resource] : 4th International Workshop, GKR 2015, Buenos Aires, Argentina, July 25, 2015, revised selected papers / Madalina Croitoru, Pierre Marquis, Sebastian Rudolph, Gem Stapleton (eds.).
ISBN
9783319287027 electronic book
3319287028 electronic book
9783319287010
Published
Cham : Springer, 2015.
Language
English
Description
1 online resource : illustrations.
Call Number
Q387
Dewey Decimal Classification
006.3/32
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed January 7, 2016).
Series
Lecture notes in computer science. Lecture notes in artificial intelligence ; 9501.
LNCS sublibrary. SL 7, Artificial intelligence.
Designing a Knowledge Representation Tool for Subject Matter Structuring
Aligning Experientially Grounded Ontologies using Language Games
An overview of argumentation frameworks for decision support
Learning Optimal Bayesian Networks with DAG Graphs
Combinatorial results on directed hypergraphs for the SAT problem
Conceptual Graphs for Formally Managing and Discovering Complementary Competences
Subjective Networks: Perspectives and Challenges
RDF-SQ: Mixing Parallel and Sequential Computation For Top-down OWL RL Inference
Bring User Interest to Related Entity Recommendation.
