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

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Croitoru, Madalina (Επιμελητής έκδοσης), Ferré, Sébastien (Επιμελητής έκδοσης), Lukose, Dickson (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2010.
• Σειρά: Lecture Notes in Computer Science, 6208
• Θέματα: Computer science.; Programming languages (Electronic computers).; Mathematical logic.; Database management.; Data mining.; Artificial intelligence.; Pattern recognition.; Computer Science.; Artificial Intelligence (incl. Robotics).; Data Mining and Knowledge Discovery.; Programming Languages, Compilers, Interpreters.; Database Management.; Mathematical Logic and Formal Languages.; Pattern Recognition.

 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. 

 Invited Papers -- Entities and Surrogates in Knowledge Representation -- Exploring Conceptual Possibilities -- Graphical Representation of Ordinal Preferences: Languages and Applications -- Combining Description Logics, Description Graphs, and Rules -- Practical Graph Mining -- Accepted Papers -- Use of Domain Knowledge in the Automatic Extraction of Structured Representations from Patient-Related Texts -- Translations between RDF(S) and Conceptual Graphs -- Default Conceptual Graph Rules, Atomic Negation and Tic-Tac-Toe -- On the Stimulation of Patterns -- Ontology-Based Understanding of Natural Language Queries Using Nested Conceptual Graphs -- An Easy Way of Expressing Conceptual Graph Queries from Keywords and Query Patterns -- Natural Intelligence – Commonsense Question Answering with Conceptual Graphs -- Learning to Map the Virtual Evolution of Knowledge -- Branching Time as a Conceptual Structure -- Formal Concept Analysis in Knowledge Discovery: A Survey -- Granular Reduction of Property-Oriented Concept Lattices -- Temporal Relational Semantic Systems -- Accepted Posters -- FcaBedrock, a Formal Context Creator -- From Generalization of Syntactic Parse Trees to Conceptual Graphs -- Conceptual Structures for Reasoning Enterprise Agents -- Conceptual Graphs for Semantic Email Addressing -- Introducing Rigor in Concept Maps -- Conceptual Knowledge Acquisition Using Automatically Generated Large-Scale Semantic Networks.