Knowledge Modelling: Foundations, Techniques and Applications

1. Knowledge Modelling:Foundations, Techniques and Applications Enrico MottaKnowledge Media InstituteThe Open UniversityUnited Kingdom

2. Basic KBS Architecture Inference Engine User Interface Domain Knowledge Base

3. First Generation KBS Architecture Inference Engine User Interface Rule-based Backward-chaining Domain Knowledge Base Set of Domain rules

4. Inference Engine User Interface Domain Knowledge Base Problems • Focus on implementation-level aspects (backward chaining) rather than knowledge-level functionalities (medical diagnosis) • Poor explanation capabilities • Difficult to assess competence • Low-level reuse support • Rules tend to be application specific

5. Heuristic Classification Model Clancey, AI Journal, 27, 1985 Data Abstractions Solutions Abstractions Heuristic Match Abstraction Refinement Solutions Data

6. HC in Medical Diagnosis Gram-negative Infection Data Abstractions Solutions Abstractions Heuristic Match Immunosuppressed Refinement Abstraction Solutions Data E-coli Infection Low white blood count

7. HC in Book Selection ‘Intelligent Book’ Data Abstractions Solutions Abstractions Heuristic Match Educated Person Stereotype Refinement Abstraction Solutions Data Watches no TV Anna Karenina

8. So What? (Competence vs Performance) • Knowledge-level analysis shows what system actually does, not how it does it • The interesting aspect about Mycin is its classification behaviour, not its depth-first control regime • Separation of competence from performance (or specification from implementation) • Important for both analysis and design of knowledge-intensive systems

9. So What? (Levels of system analysis) • There exist different levels at which a system can be described • knowledge-level (tasks and problem solving methods) • Symbol-level (backward-chaining) • Sub-symbol level (registers) • Shift in the level of analysis: • Wrong question: Can a problem be solved by means of a rule-based system? • Right questions: What type of knowledge-intensive task are we tackling? What are the appropriate problem solving methods?

10. So What? (Reuse) • Knowledge-level analysis uncovers generic reasoning patterns in problem solving agents • E.g., heuristic classification • Shift from rule-based reuse to knowledge-level reuse • Focus on high-level reusable task models and reasoning patterns • Classes of tasks • Design, diagnosis, classification, etc. • Problem solving methods • Design methods, classification methods, etc.

11. So What? (Research & Development) • Model-based knowledge acquisition • From acquiring rules to instantiating task models • Robust KBS development by reuse • KBS as a structured development process • Robustness and economy • Importance of libraries • KBS development not necessarily an ‘art’! • Towards a practical theory of knowledge-based systems • What are the classes of tasks/problem solving methods? • How do we identify/model them? • When are methods appropriate?

12. Knowledge-level Architecturesfor Sharing and Reuse Application of the modelling paradigm to the specification and use of libraries of reusable components for knowledge systems

13. Modelling Frameworks (1) • A modelling framework identifies the generic types of knowledge which occur in knowledge systems, thus providing a generic epistemological organization for knowledge systems • Several exist • KADS/Common KADS - Un.of Amsterdam • Components of Expertise - Steels • Generic Tasks - Chandrasekaran • Role-limiting Methods - McDermott • Protégé - Musen, Stanford • TMDA - Motta • UPML - Fensel & Motta

14. Modelling Frameworks (2) • Much in common • Emphasis on reusable models • Typology of generic tasks • Constructivist paradigm • Some differences • Different degrees of coupling between domain-specific and domain-independent knowledge • Different degrees of flexibility • Different typologies of knowledge categories

15. A Constructive Approach... Let’s define our own framework...

16. Generic Tasks • Informal definition • A generic class of applications - e.g., planning, design, diagnosis, scheduling, etc.. • More precise definition • A knowledge-level, application-independent description of the goal to be attained by a problem solver. • Several typologies exist • e.g., Breuker, 1994 • Viewpoints over applications • No ‘natural categories’ • Different viewpoints can be imposed on a particular application

17. Example: Parametric Design Generic Task Parametric Design Inputs: Parameters, Constraints, Requirements, Cost-Function, Preferences Output: Design-Model Goal: “To produce a complete and consistent design model, which satisfies the given requirements” Preconditions: “At least one requirement and one parameter are provided”

18. Example: Classification Generic Task Classification Inputs: Candidate-classes Observables Output: Best-Matching-Classes Preconditions: “At least one candidate class exists” Goal: “To find the class that best explains the observables”

19. Generic Component 2: Reusable PSMs • A domain-independent, knowledge-level specification of problem solving behaviour, which can be used to solve a class of tasks. • PSM specifications may be partial • PSM can be task-specific • E.g., heuristic classification • PSM can be task-independent • E.g., search methods, such as hill-climbing, A*, etc.....

20. Functional Specification of a PSM Problem solving method search ontology import state-space-terminology competence roles input input: State output output: State preconditions input ≠ 0 postconditions solution_state (output) assumptions $ ?s . solution_state (?s) & successor (input, ?s)

21. Operational Description Begin states:= one x. initialize (input input) repeat state:= one x . select _state (states states) if solution_state (state) then return state else succ_states:= one x. derive_successor_states (state state) states:= one x. update_state_space (input1 states input2 succ_states) end if endrepeat end

22. Task-Method Structures Problem Type Primitive PSM

23. Multi-Functional Domain Models • Domain-specific models, which are not committed to a specific PSM or task. • Examples • A database of cars • The CYC knowledge base, etc..

24. Application Model Picture so far.. Generic Task Problem Solving Method Simple Classifier Classification Multi-Functional Domain Lunar rocks

25. Issue How to link different reusable components? Application Model Classification Simple Classifier Generic Task Problem Solving Method Multi-Functional Domain Lunar rocks

26. Solution: Mappings • Mappings model explicitly the relationship between different components in an application model Application Model Classification Simple Classifier Task-PSMMapping Generic Task Problem Solving Method PSM-DomainMapping Task-DomainMapping Multi-Functional Domain Lunar rocks

27. Example • Scenario: Office Allocation Application • Generic Task: Parametric Design • Domain: KB about employees and offices Task Level Parameter Design Model Domain Level Pairs <Employee, Room> Employee

28. Application-specific knowledge Mappings are an example of application-specific knowledge. Are there others? Yes: Application-specific heuristic problem solving knowledge

29. Elevator Design Example • A configuration designer only considers two positions for the counterweight • Half way between platform and U-bracket • A position such that the distance between the counterweight and the platform is at least 0.75 inches

30. Complete Picture Application Model Generic Task Problem Solving Method Mapping Knowledge Application-specific Problem-Solving Knowledge Application Configuration Multi-Functional Domain

31. Even More Complete Picture Application Model Generic Task Problem Solving Method Task Ontology Method Ontology Mapping Knowledge Application-specific Problem-Solving Knowledge Ontology Mapping Ontology Application Configuration Multi-Functional Domain Domain Ontology