Conceptual Structures: From Information to Intelligence: 18th International Conference on Conceptual Structures, ICCS 2010, Kuching, Sarawak, Malaysia, July 26-30, 2010. Proceedings

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th The 18 International Conference on Conceptual Structures (ICCS 2010) was the latest in a series of annual conferences that have been held in Europe, A- tralia, and North America since 1993. The focus of the conference has been the representation and analysis of conceptual knowledge for research and practical application. ICCS brings together researchers and practitioners in information and computer sciences as well as social science to explore novel ways that c- ceptual structures can be deployed. Arising from the research on knowledge representation and reasoning with conceptual graphs, over the years ICCS has broadened its scope to include in- vations from a wider range of theories and related practices, among them other forms of graph-based reasoning systems like RDF or existential graphs, formal concept analysis, Semantic Web technologies, ontologies, concept mapping and more. Accordingly, ICCS represents a family of approaches related to conc- tualstructuresthatbuild onthesuccesseswithtechniquesderivedfromarti?cial intelligence, knowledge representation and reasoning, applied mathematics and lattice theory, computational linguistics, conceptual modeling and design, d- grammatic reasoning and logic, intelligent systems and knowledge management. The ICCS 2010 theme “From Information to Intelligence” hints at unve- ing the reasoning capabilities of conceptual structures. Indeed, improvements in storage capacity and performance of computing infrastructure have also - fected the nature of knowledge representation and reasoning (KRR) systems, shifting their focus toward representational power and execution performance. Therefore, KRR research is now faced with a challenge of developing knowledge representation and reasoning structures optimized for such reasonings.

Author(s): Michel Chein (auth.), Madalina Croitoru, Sébastien Ferré, Dickson Lukose (eds.)
Series: Lecture Notes in Computer Science 6208 : Lecture Notes in Artificial Intelligence
Edition: 1
Publisher: Springer-Verlag Berlin Heidelberg
Year: 2010

Language: English
Pages: 207
Tags: Artificial Intelligence (incl. Robotics); Database Management; Data Mining and Knowledge Discovery; Mathematical Logic and Formal Languages; Pattern Recognition; Data Storage Representation

Front Matter....Pages -
Entities and Surrogates in Knowledge Representation....Pages 1-1
Exploring Conceptual Possibilities....Pages 2-2
Graphical Representation of Ordinal Preferences: Languages and Applications....Pages 3-9
Combining Description Logics, Description Graphs, and Rules....Pages 10-12
Practical Graph Mining....Pages 13-13
Use of Domain Knowledge in the Automatic Extraction of Structured Representations from Patient-Related Texts....Pages 14-27
Translations between RDF(S) and Conceptual Graphs....Pages 28-41
Default Conceptual Graph Rules, Atomic Negation and Tic-Tac-Toe....Pages 42-55
On the Stimulation of Patterns....Pages 56-69
Ontology-Based Understanding of Natural Language Queries Using Nested Conceptual Graphs....Pages 70-83
An Easy Way of Expressing Conceptual Graph Queries from Keywords and Query Patterns....Pages 84-96
Natural Intelligence – Commonsense Question Answering with Conceptual Graphs....Pages 97-107
Learning to Map the Virtual Evolution of Knowledge....Pages 108-124
Branching Time as a Conceptual Structure....Pages 125-138
Formal Concept Analysis in Knowledge Discovery: A Survey....Pages 139-153
Granular Reduction of Property-Oriented Concept Lattices....Pages 154-164
Temporal Relational Semantic Systems....Pages 165-180
FcaBedrock, a Formal Context Creator....Pages 181-184
From Generalization of Syntactic Parse Trees to Conceptual Graphs....Pages 185-190
Conceptual Structures for Reasoning Enterprise Agents....Pages 191-194
Conceptual Graphs for Semantic Email Addressing....Pages 195-198
Introducing Rigor in Concept Maps....Pages 199-202
Conceptual Knowledge Acquisition Using Automatically Generated Large-Scale Semantic Networks....Pages 203-206
Back Matter....Pages -