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Appendixes

Published online by Cambridge University Press:  05 September 2016

Gheorghe Tecuci
Affiliation:
George Mason University, Virginia
Dorin Marcu
Affiliation:
George Mason University, Virginia
Mihai Boicu
Affiliation:
George Mason University, Virginia
David A. Schum
Affiliation:
George Mason University, Virginia
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Summary

SUMMARY: KNOWLEDGE ENGINEERING GUIDELINES

Knowledge Base Guidelines

Guideline 3.1. Work with only one knowledge base loaded in memory (p. 111)

Guideline 3.2. Create a knowledge base and save successive versions (p. 111)

Modeling Guidelines

Guideline 4.1. Structure the modeling process based on the agent's specification (p. 147)

Guideline 4.2. Define reduction trees in natural language using simple questions (p. 148)

Guideline 4.3. Identify the specific instances, the generic instances, and the constants (p. 148)

Guideline 4.4. Guide the reduction by the possible need of future changes (p. 149)

Guideline 4.5. Learn and reuse reduction patterns (p. 149)

Ontology Development Guidelines

Guideline 6.1. Define similar siblings (p. 186)

Guideline 6.2. Group similar siblings under natural concepts (p. 187)

Guideline 6.3. Recognize that a single subconcept may indicate ontology incompleteness or error (p. 187)

Guideline 6.4. Adopt and follow a naming convention (p. 188)

Guideline 6.5. Name subconcepts based on superconcepts (p. 189)

Guideline 6.6. Represent well-established categories fromthe real world as concepts (p. 195)

Guideline 6.7. Define concepts and instances to represent knowledge corresponding to n-ary relations (p. 196)

Guideline 6.8. Define feature names that distinguish them from concept names (p. 196)

Guidelines for Rule and Hypothesis Learning

Guideline 9.1. Properly identify all the entities in the example before starting rule learning (p. 285)

Guideline 9.2. Avoid learning from examples that are too specific (p. 286)

Guideline 9.3. Use modeling-based ontology extension before starting rule learning (p. 286)

Guideline 9.4. Carefully define the domains and the ranges of the features (p. 286)

Guideline 9.5. Provide hints to guide explanation generation (p. 288)

Guideline 9.6. Avoid learning rules without explanations (p. 288)

Guideline 9.7. Recognize concepts in the reasoning tree (p. 288)

Guidelines for Rule Refinement

Guideline 10.1. Assess similar hypotheses to refine the rules (p. 321)

Guideline 10.2. Extend the ontology to define failure explanations (p. 321)

Abstraction Guideline

Guideline 11.1. Define short context-dependent hypothesis names for the abstract tree (p. 334)

Planning Guidelines

Guideline 12.1. Use a plausible task ordering when specifying a task decomposition (p. 398)

Guideline 12.2. Specify the planning tree in a top-down and left-to-right order (p. 399)

Guideline 12.3. Define preconditions when reducing an abstract task to a concrete task (p. 399)

Guideline 12.4. Specify the goal of the current task to enable the specification of the follow-on tasks (p. 400)

Type
Chapter
Information
Knowledge Engineering
Building Cognitive Assistants for Evidence-based Reasoning
, pp. 443 - 446
Publisher: Cambridge University Press
Print publication year: 2016

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  • Appendixes
  • Gheorghe Tecuci, George Mason University, Virginia, Dorin Marcu, George Mason University, Virginia, Mihai Boicu, George Mason University, Virginia, David A. Schum, George Mason University, Virginia
  • Book: Knowledge Engineering
  • Online publication: 05 September 2016
  • Chapter DOI: https://doi.org/10.1017/CBO9781316388464.016
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  • Appendixes
  • Gheorghe Tecuci, George Mason University, Virginia, Dorin Marcu, George Mason University, Virginia, Mihai Boicu, George Mason University, Virginia, David A. Schum, George Mason University, Virginia
  • Book: Knowledge Engineering
  • Online publication: 05 September 2016
  • Chapter DOI: https://doi.org/10.1017/CBO9781316388464.016
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Appendixes
  • Gheorghe Tecuci, George Mason University, Virginia, Dorin Marcu, George Mason University, Virginia, Mihai Boicu, George Mason University, Virginia, David A. Schum, George Mason University, Virginia
  • Book: Knowledge Engineering
  • Online publication: 05 September 2016
  • Chapter DOI: https://doi.org/10.1017/CBO9781316388464.016
Available formats
×