Overcoming Knowledge Acquisition Bottlenecks
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The document discusses the problem of knowledge acquisition in artificial intelligence. It describes knowledge acquisition as the critical bottleneck problem that has hindered the development of successful applied AI. The document outlines several historical problems with knowledge engineering, including a lack of hardware and trained knowledge engineers. It also discusses various methods that were developed to help with the process of eliciting and acquiring knowledge from experts, such as repertory grids, think aloud methods, and card sorting. Modern approaches and methodologies for building ontologies are also covered, such as CommonKADS and METHONTOLOGY.
![Knowledge Engineering
“Critical scientific problem [...] successful applied AI requires that
knowledge move from the heads of experts into programs”
FEIGENBAUM, E. A. (1984), Knowledge Engineering. Annals of the New York Academy of Sciences, 426: 91–107. doi: 10.1111/j.1749-6632.1984.tb16513.x
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Overcoming Knowledge Acquisition Bottlenecks
- 1. The Knowledge Reengineering Bottleneck Rinke Hoekstra rinke.hoekstra@vu.nl VU University Amsterdam/University of Amsterdam vrijdag 24 februari 12
- 2. Knowledge Engineering “Critical scientific problem [...] successful applied AI requires that knowledge move from the heads of experts into programs” FEIGENBAUM, E. A. (1984), Knowledge Engineering. Annals of the New York Academy of Sciences, 426: 91–107. doi: 10.1111/j.1749-6632.1984.tb16513.x vrijdag 24 februari 12
- 3. ‣ The lack of adequate and appropriate hardware ‣ Lack of cumulation of AI methods and techniques ‣ Shortage of trained knowledge engineers ‣ The problem of knowledge acquisition ‣ The development gap Problems of Knowledge Engineering vrijdag 24 februari 12
- 4. Knowledge Acquisition Bottleneck “The problem of knowledge acquisition is the critical bottleneck problem in artificial intelligence” FEIGENBAUM, E. A. (1984), Knowledge Engineering. Annals of the New York Academy of Sciences, 426: 91–107. doi: 10.1111/j.1749-6632.1984.tb16513.x vrijdag 24 februari 12
- 5. The Dark Ages vrijdag 24 februari 12
- 6. Knowledge Elicitation Repertory Grids Think Aloud Method Cardsorting ... vrijdag 24 februari 12
- 7. Knowledge Elicitation Repertory Grids Think Aloud Method Cardsorting MYCIN and GUIDON ... Knowledge Types vrijdag 24 februari 12
- 8. Knowledge Elicitation Repertory Grids Think Aloud Method Cardsorting MYCIN and GUIDON ... Knowledge Types CommonKADS Engineering Methodology Problem Solving Methods Domain Models vrijdag 24 februari 12
- 9. Knowledge Elicitation Repertory Grids Think Aloud Method Cardsorting MYCIN and GUIDON ... Knowledge Types CommonKADS Engineering Methodology Problem Solving Methods Domain Models Ontolingua “Explicit specification of a shared conceptualization” Sharing ontologies vrijdag 24 februari 12
- 10. How to build the right ontology? vrijdag 24 februari 12
- 11. How to build the right Methodologies Middle Out Approach Documentation Ontology ontology? Identify Capture Purpose and Uschold & Gruninger Specify Scope Ontology METHONTOLOGY Guidelines Motivating Coding Evaluation KACTUS Scenarios Ontology Competency SENSUS Questions Integration (KA)2 vrijdag 24 februari 12
- 12. How to build the right Methodologies Middle Out Approach Documentation Ontology ontology? Identify Capture Purpose and Uschold & Gruninger Specify Scope Ontology METHONTOLOGY Guidelines Motivating Coding Evaluation KACTUS Scenarios Ontology Competency SENSUS Questions Integration (KA)2 Top Ontology Ontology Types Representation Ontology Generic Ontology Top Foundation Core Ontology Generic Domain Domain Ontology Application Core vrijdag 24 februari 12
- 13. How to build the right Methodologies Middle Out Approach Documentation Ontology ontology? Identify Capture Purpose and Uschold & Gruninger Specify Scope Ontology METHONTOLOGY Guidelines Motivating Coding Evaluation KACTUS Scenarios Ontology Competency SENSUS Questions Integration (KA)2 Top Ontology Ontology Types Representation Ontology Generic Ontology Top Foundation Core Ontology Generic Domain Domain Ontology Application Core Principles OntoClean Ontology vs. Epistemology vrijdag 24 februari 12
- 14. How to build the right Methodologies Middle Out Approach Documentation Ontology ontology? Identify Capture Purpose and Uschold & Gruninger Specify Scope Ontology METHONTOLOGY Guidelines Motivating Coding Evaluation KACTUS Scenarios Ontology Competency SENSUS Questions Integration (KA)2 Top Ontology Ontology Types Representation Ontology Generic Ontology Top Foundation Core Ontology Generic Domain Domain Ontology Application Core Principles OntoClean Ontology vs. Epistemology Ontology Reuse Merging & Alignment Modularization Ontology Design Patterns vrijdag 24 februari 12
- 15. Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/ vrijdag 24 februari 12
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