LEADER 06778nam 22008055 450 001 996466432603316 005 20200703063332.0 010 $a3-642-14197-8 024 7 $a10.1007/978-3-642-14197-3 035 $a(CKB)2670000000036329 035 $a(SSID)ssj0000446388 035 $a(PQKBManifestationID)11312810 035 $a(PQKBTitleCode)TC0000446388 035 $a(PQKBWorkID)10492150 035 $a(PQKB)11363499 035 $a(DE-He213)978-3-642-14197-3 035 $a(MiAaPQ)EBC3065583 035 $a(PPN)149018452 035 $a(EXLCZ)992670000000036329 100 $a20100729d2010 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aConceptual Structures: From Information to Intelligence$b[electronic resource] $e18th International Conference on Conceptual Structures, ICCS 2010, Kuching, Sarawak, Malaysia, July 26-30, 2010, Proceedings /$fedited by Madalina Croitoru, Sébastien Ferré, Dickson Lukose 205 $a1st ed. 2010. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2010. 215 $a1 online resource (XII, 207 p. 51 illus.) 225 1 $aLecture Notes in Artificial Intelligence ;$v6208 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-642-14196-X 320 $aIncludes bibliographical references and index. 327 $aInvited 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. 330 $ath The 18 International Conference on Conceptual Structures (ICCS 2010) was the latest in a series of annual conferences that have been held in Europe, A- tralia, and North America since 1993. The focus of the conference has been the representation and analysis of conceptual knowledge for research and practical application. ICCS brings together researchers and practitioners in information and computer sciences as well as social science to explore novel ways that c- ceptual structures can be deployed. Arising from the research on knowledge representation and reasoning with conceptual graphs, over the years ICCS has broadened its scope to include in- vations from a wider range of theories and related practices, among them other forms of graph-based reasoning systems like RDF or existential graphs, formal concept analysis, Semantic Web technologies, ontologies, concept mapping and more. Accordingly, ICCS represents a family of approaches related to conc- tualstructuresthatbuild onthesuccesseswithtechniquesderivedfromarti?cial intelligence, knowledge representation and reasoning, applied mathematics and lattice theory, computational linguistics, conceptual modeling and design, d- grammatic reasoning and logic, intelligent systems and knowledge management. The ICCS 2010 theme ?From Information to Intelligence? hints at unve- ing the reasoning capabilities of conceptual structures. Indeed, improvements in storage capacity and performance of computing infrastructure have also - fected the nature of knowledge representation and reasoning (KRR) systems, shifting their focus toward representational power and execution performance. Therefore, KRR research is now faced with a challenge of developing knowledge representation and reasoning structures optimized for such reasonings. 410 0$aLecture Notes in Artificial Intelligence ;$v6208 606 $aArtificial intelligence 606 $aData mining 606 $aProgramming languages (Electronic computers) 606 $aDatabase management 606 $aMathematical logic 606 $aPattern recognition 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aProgramming Languages, Compilers, Interpreters$3https://scigraph.springernature.com/ontologies/product-market-codes/I14037 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 606 $aMathematical Logic and Formal Languages$3https://scigraph.springernature.com/ontologies/product-market-codes/I16048 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 608 $aCongress.$2swd 615 0$aArtificial intelligence. 615 0$aData mining. 615 0$aProgramming languages (Electronic computers). 615 0$aDatabase management. 615 0$aMathematical logic. 615 0$aPattern recognition. 615 14$aArtificial Intelligence. 615 24$aData Mining and Knowledge Discovery. 615 24$aProgramming Languages, Compilers, Interpreters. 615 24$aDatabase Management. 615 24$aMathematical Logic and Formal Languages. 615 24$aPattern Recognition. 676 $a006.3 702 $aCroitoru$b Madalina$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aFerré$b Sébastien$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLukose$b Dickson$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aICCS 2010$d(18th :$f2010 July 26-30 :$eKuching, Sarawak) 906 $aBOOK 912 $a996466432603316 996 $aConceptual Structures: From Information to Intelligence$92831779 997 $aUNISA