The Knowledge Acquisition and Representation Language, KARL - Fensel, Dieter

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Résumé :
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Sommaire:
1 Introduction.- 1.1 Model-based and Incremental Knowledge Engineering.- 1.1.1 Model-based Knowledge Engineering.- 1.1.2 Incremental Knowledge Engineering.- 1.1.3 MIKE.- 1.2 The Knowledge Acquisition and Representation Language KARL.- 1.2.1 A Model of Expertise in KARL.- 1.2.2 Why not using VDM or Z.- 1.2.3 The Formal Semantics.- 1.2.4 The Implementation.- 1.2.5 Case Studies.- 1.3 Some Arguments about Formal and Operational Specification Languages.- 1.3.1 The Knowledge of Experts Cannot or not Adequately be Described Formally.- 1.3.2 Formal Languages are Difficult to Learn.- 1.3.3 Formal Specifications are too Complex and too Difficult to Understand.- 1.3.4 Formal Specifications are too Expensive.- 1.3.5 Should a Specification Language be Executable or Not.- 2 Logical-Karl.- 2.1 Significant Ideas of Other Approaches Used for L-KARL.- 2.1.1 Object-orientation and Equality.- 2.1.2 Reasoning About Classes.- 2.1.3 Integration of Well-typing Into a Model-theoretical Semantics.- 2.1.4 Set-valued Attributes.- 2.1.5 Minimal and Perfect Models as Semantics.- 2.2 Syntax of L-KARL.- 2.3 Informal Semantics of L-KARL.- 2.3.1 ID Terms.- 2.3.2 Class and Predicate Definitions.- 2.3.3 Literals.- 2.3.4 The Difference Between Objects and Values.- 2.3.5 The Well-typing Conditions.- 2.3.6 Formulae.- 2.3.7 Constraints.- 2.4 A Comparison with F-logic and O-logic.- 3 Procedural-Karl.- 3.1 Significant Ideas of Other Approaches Used for P-KARL.- 3.2 Syntax of P-KARL.- 3.3 Informal Semantics of P-KARL.- 4 The Karl Model of Expertise.- 4.1 The Sisyphus Example.- 4.2 The Domain Layer.- 4.2.1 Terminological Knowledge: The Domain Schema.- 4.2.2 Intensional Descriptions.- 4.2.3 Factual Knowledge.- 4.2.4 Necessary Descriptions.- 4.2.5 Data.- 4.2.6 Graphical Representation.- 4.2.7 The Domain Layer of the Sisyphus Example.- 4.3 The Inference Layer.- 4.3.1 An Alphabet of an Inference Layer.- 4.3.2 Roles.- 4.3.3 Elementary Inference Actions.- 4.3.4 Inference Structure.- 4.3.5 Graphical Representation.- 4.3.6 Inference Structures versus Dataflow Diagrams.- 4.3.7 The Inference Layer of the Sisyphus Example.- 4.4 The Task Layer.- 4.4.1 Language Primitives at the Task Layer.- 4.4.2 Graphical Representation.- 4.4.3 The Task Layer of the Sisyphus Example.- 4.5 The Model of Cooperation.- 4.5.1 Data.- 4.5.2 Control Information.- 5 The Formal Semantics of Karl.- 5.1 The Formal Semantics of L-KARL.- 5.1.1 Model Theory of L-KARL.- 5.1.2 Herbrand Models.- 5.1.3 Minimal Model Semantics.- 5.1.4 Perfect Model Semantics.- 5.1.5 Constraints.- 5.1.6 Built-in Predicates.- 5.2 The Formal Semantics of P-KARL.- 5.3 The Formal Semantics of a Domain Layer.- 5.4 The Formal Semantics of an Inference Layer.- 5.5 The Formal Semantics of a Task Layer.- 6 Conclusion.- 6.1 Highlights of KARL.- 6.2 Related Work.- 6.2.1 A Comparison with (ML)2.- 6.2.2 KARL and Structured Analysis.- 6.3 Shortcomings of KARL.- 6.3.1 Current Limitations of KARL.- 6.3.2 Actual Limitations of KARL.- 6.4 Future Work.- 6.4.1 Validation of Conceptual Models.- 6.4.2 Formal Specifications of Reusable Problem-Solving Methods.- References.