Conceptual Structures: Logical, Linguistic, and Computational Issues

1. Front Matter

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2. Concepts and Language

   1. The Role of Conceptual Structure in Human Evolution

      • Keith Devlin

      Pages 1-12

   2. Concepts in Linguistics – Concepts in Natural Language

      • Gisela Harras

      Pages 13-26

   3. Patterns, Schemata, and Types — Author Support through Formalized Experience

      • Felix H. Gatzemeier

      Pages 27-40

   4. Conventions and Notations for Knowledge Representation and Retrieval

      • Philippe Martin

      Pages 41-54

3. Conceptual Ontology

   1. Ontology, Metadata, and Semiotics

      • John F. Sowa

      Pages 55-81

   2. Pragmatically Yours,

      • Mary Keeler

      Pages 82-99

   3. Conceptual Modeling for Distributed Ontology Environments

      • Deborah L. McGuinness

      Pages 100-112

   4. Discovery of Class Relations in Exception Structured Knowledge Bases

      • Hendra Suryanto, Paul Compton

      Pages 113-126

4. Conceptual Graphs: Perspectives

   1. CGs Applications : Where Are We 7 Years after the First ICCS?

      • Michel Chein, David Genest

      Pages 127-139

   2. The Engineering of a CG-Based System: Fundamental Issues

      • Guy W. Mineau

      Pages 140-156

   3. Conceptual Graphs, Metamodeling, and Notation of Concepts

      • Olivier Gerbé, Guy W. Mineau, Rudolf K. Keller

      Pages 157-171

   4. Knowledge Representation and Reasonings Based on Graph Homomorphism

      • Marie-Laure Mugnier

      Pages 172-192

   5. User Modelling Using Conceptual Graphs for Intelligent Agents

      • James F. Baldwin, Trevor P. Martin, Aimilia Tzanavari

      Pages 193-206

   6. Towards a Unified Querying System of Both Structured and Semi-structured Imprecise Data Using Fuzzy View

      • Patrice Buche, Ollivier Haemmerlé

      Pages 207-220

5. Formal Semantics of Conceptual Structures

   1. The Extensional Semantics of the Conceptual Graph Formalism

      • Guy W. Mineau

      Pages 221-234

   2. Semantics of Attribute Relations in Conceptual Graphs

      • Pavel Kocura

      Pages 235-248

   3. Nested Concept Graphs and Triadic Power Context Families

      • Susanne Prediger

      Pages 249-262

   4. Negations in Simple Concept Graphs

      • Frithjof Dau

      Pages 263-276

   5. Extending the CG Model by Simulations

      • Jean-François Baget

      Pages 277-291

Computerscientistscreatemodelsofaperceivedreality.ThroughAItechniques, these models aim at providing the basic support for emulating cognitive - havior such as reasoning and learning, which is one of the main goals of the AI research e?ort. Such computer models are formed through the interaction of various acquisition and inference mechanisms: perception, concept learning, conceptual clustering, hypothesis testing, probabilistic inference, etc., and are represented using di?erent paradigms tightly linked to the processes that use them. Among these paradigms let us cite: biological models (neural nets, genetic programming), logic-based models (?rst-order logic, modal logic, rule-based s- tems), virtual reality models (object systems, agent systems), probabilistic m- els(Bayesiannets,fuzzylogic),linguisticmodels(conceptualdependencygraphs, language-based representations), etc. OneofthestrengthsoftheConceptualGraph(CG)theoryisitsversatilityin terms of the representation paradigms under which it falls. It can be viewed and therefore used, under di?erent representation paradigms, which makes it a p- ular choice for a wealth of applications. Its full coupling with di?erent cognitive processes lead to the opening of the ?eld toward related research communities such as the Description Logic, Formal Concept Analysis, and Computational Linguistic communities. We now see more and more research results from one community enrich the other, laying the foundations of common philosophical grounds from which a successful synergy can emerge.

• Cognition
• Computational Linguistics
• Concept Analysis
• Conceptual Structures
• Evolution
• Logics
• Natural
• Symbolic Computation
• calculus
• knowledge base
• knowledge representation
• logic programming
• modeling
• ontology
• programming language
• algorithm analysis and problem complexity

• Institute of Algebra, University of Technology, Dresden, Germany

  Bernhard Ganter

• Département d’informatique et de génie logiciel, Université Laval, Pavillon Adrien-Pouliot, Quebec City, Canada

  Guy W. Mineau

Policies and ethics