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A introduction to CommonKADS: structured knowledge engineering

A introduction to CommonKADS: structured knowledge engineering. Guus Schreiber www.commonkads.uva.nl. Activities in knowledge-system development. Business context modelling. Knowledge modelling. Communication modelling. System design. Why context modeling?.

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A introduction to CommonKADS: structured knowledge engineering

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  1. A introduction to CommonKADS: structured knowledge engineering Guus Schreiber www.commonkads.uva.nl

  2. Activities in knowledge-system development Business context modelling Knowledge modelling Communication modelling System design

  3. Why context modeling? • Often difficult to identify profitable use of (knowledge) technology • Laboratory is different from the ''real'' world • Acceptability to users very important • Fielding into ongoing process not self evident • Often not clear what additional measures to take

  4. How to analyze a knowledge-intensive organization? • describe organization aspects: • opportunity/problems portfolio • business context, goals, strategy • internal organization: • structure • processes • people (staff: functional roles) • power and culture • resources (knowledge, support systems, equipment,…) • do this for both current and future organization • comparison, and first decisions on where to go

  5. Worksheets Organization Model

  6. Process “Housing”

  7. Example OM-3 for “Housing”

  8. Knowledge modelling • Specific type of conceptual modelling • Only gradual differences with “general” conceptual modelling • Knowledge modelling from scratch is time-consuming and difficult • Knowledge reuse is important theme • Patterns exist for types of problem-solving tasks • Base on typology of problem-solving tasks

  9. Analytic versus synthetic tasks • analytic tasks • system pre-exists • it is typically not completely "known" • input: some data about the system, • output: some characterization of the system • synthetic tasks • system does not yet exist • input: requirements about system to be constructed • output: constructed system description

  10. Task hierarchy

  11. Knowledge categories • Task knowledge • goal-oriented • functional decomposition • Domain knowledge • relevant domain knowledge and information • static • Inference knowledge • basic reasoning steps that can be made in the domain knowledge and are applied by tasks

  12. Knowledge model overview

  13. Domain knowledge • domain schema • schematic description of knowledge and information types • comparable to data model • defined through domain constructs • knowledge base • set of knowledge instances • comparable to database content • but; static nature

  14. Constructs for domain schema • Concept • cf. object class (without operations) • Relation • cf. association • Attribute • primitive value • Rule type • introduces expressions => no SE equivalent

  15. Example rule type

  16. Inference • fully described through a declarative specification of properties of its I/O • internal process of the inference is a black box • not of interest for knowledge modeling. • I/O described using “role names” • functional names, not part of the domain knowledge schema / data model • guideline to stop decomposition: explanation

  17. Example inference: cover

  18. Inference structure

  19. Task knowledge • describes goals • assess a mortgage application in order to minimize the risk of losing money • find the cause of a malfunction of a photocopier in order to restore service. • design an elevator for a new building. • describes strategies (methods, PSMs) that can be employed for realizing goals. • typically described in a hierarchical fashion:

  20. UML activity diagram for method control

  21. Assessment • find decision category for a case based on domain-specific norms. • typical domains: financial applications (loan application), community service • terminology: case, decision, norms • some similarities with monitoring • differences: • timing: assessment is more static • different output: decision versus discrepancy

  22. Example assessment task:mortgage assessment

  23. Mortgage domain information

  24. Assessment: abstract & match method • Abstract the case data • Specify the norms applicable to the case • e.g. “rent-fits-income”, “correct-household-size” • Select a single norm • Compute a truth value for the norm with respect to the case • See whether this leads to a decision • Repeat norm selection and evaluation until a decision is reached

  25. Mortgage domain knowledge

  26. Template (pattern) for assessment task case abstract abstracted specify norms select case evaluate norm norm decision match value

  27. Assessment control

  28. Claim handling for unemployment benefits

  29. Normen: Verzekerd Werkloos Wekeneis Jareneis Beslisregels ALS niet verzekerd of niet werkloos of niet voldoet aan wekeneis DAN geen WW ALS wel wekeneis en niet jareneis DAN korte basisuitkering ALS wel jareneis DAN loongerelateerde uitkering Normen en beslisregels voor WW beoordeling

  30. In applications: typical task combinations • monitoring + diagnosis • Production process • monitoring + assessment • Nursing task • diagnosis + planning • Troubleshooting devices • classification + planning • Military applications

  31. Example: apple-pest management

  32. Summary • Knowledge engineering is a specialized form of software engineering • CommonKADS: model-based approach to knowledge engineering • Reuse of task-specific knowledge models is important theme • Knowledge model often outlives application

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