An Overview of Knowledge Acquisition

1. An Overview of Knowledge Acquisition Mark A. Musen

2. Overview • Problems in Knowledge Acquisition (KA) • Strategies for KA • Knowledge Elicitation Techniques • Computational models • Conclusion

3. Introduction • Knowledge acquisition: central in the development of intelligent computer programs. • It is a well known barrier in the development of expert systems. • The challenges when encoding human problem-solving knowledge include: • Elicitation – the act of getting the information. • Modeling and representation – storing the knowledge. • Knowledge acquisition is related to these fields of research: • Computer science • Psychology • Philosophy • Social sciences

4. The Problem of Tacit Knowledge • Humans learn through three phases: • Cognitive – (conceptually) identifying the actions • Associative – performing the actions, becoming efficient through repetition and feedback • Autonomous – the action is performed correctly and effortlessly. • At this final stage, the knowledge is tacit – the person isn’t consciously aware of this knowledge.

5. What exactly is Tacit Knowledge? • Knowledge that is implied by actions or statements. • Not explicitly stated knowledge • The person isn’t aware of having the knowledge • This presents a challenge to knowledge acquisition because experts donot introspect reliably.

6. The Problem of Miscommunication • The knowledge engineer must: • Speak the same “language” as the expert. • Become familiar with the domain. • Learn a new vocabulary, and possibly a new perspective to look atproblems. • Be an efficient intermediary between the expert and the knowledgebase. • Understand the application area in detail. In many cases, the knowledge engineer and the expert must cooperate to clarify the relevant terms. This is done by identifying the concepts, and agreeing on a single label to use for them.

7. The Problem of Using Knowledge Representations • The knowledge representation scheme must have sufficient expressive power. This is called “epistemological adequacy” – the ability to express the facts that a person knows about some aspect of the world. • The primitives in the knowledge representation scheme necessarily affect the way developers think about a problem. • The expressiveness enabled by a knowledge representation scheme restricts the knowledge bases of expert systems, and the knowledge engineer’s cleverness in using the language of the system restricts the knowledge base further.

8. The Problem of Creating Models • Knowledge bases are models, and are thus approximate and selective. • With too many simplifications and assumptions, the expert system may fail. • The mental model that the knowledge engineer has of the problem area should match the model that the expert has. • Both the expert and the system builder revise their mental model. • The system should be constructed in accordance with this common mental model of the domain. • The knowledge base is not transferred from the expert to the system. It is created.

9. Overview • Problems in Knowledge Acquisition (KA) • Strategies for KA • Knowledge Elicitation Techniques • Computational models • Conclusion

10. Direct Questioning • Ask the domain expert direct questions to elicit information • Disadvantages: • the answers can depend on how the questions is asked • the answers can implicit background information • a lot of the knowledge is tacit and hence it wont be included

11. Protocol Analysis • Goal: elicit authentic knowledge • The subjects (i.e. domain experts) have to be studied while they are in the problem solving process • They have to think aloud and report what they do and think (but not to rationalize or justify their actions) • This leads to a verbal protocol which the knowledge engineer can use to build a model of problem solving. • Disadvantages: • many people have argued that to let expert speak out load during their problem solving process will lead to distortion of their behavior, and hence it wont be authentic knowledge

12. Psychometric Methods • Concerned with how people classify elements in the world and solve problems • George Kelly’s personal construct theory (Kelly: a clinical psychologist who developed special interviewing techniques) • This interviewing technique is also being used in KA • The knowledge engineer can learn how the domain expert make and act on distinctions.

13. Kelly’s personal construct theory • Begin with selecting a set of entities from the patient personal experiences (parents, friends etc.) • The interviewer then identifies the patient personal constructs by asking the patient to distinguishing features of the entities • At last the interviewer asks how each constructs applied to each entity • This results in a matrix consisting of personal constructs and entities

14. Ethnographic Methods • Adapted the field methods of anthropologists and perform ethnographic observations of human experts directly in the workplace • this (hopefully) leads to an identification of the authentic methods of how the experts solve actual problems • Work in this field become more and more important, but this is both expensive and inconvenient

15. Overview • Problems in Knowledge Acquisition (KA) • Strategies for KA • Knowledge Elicitation Techniques • Computational models • Conclusion

16. Model-specification techniques • Knowledge Acquisition and Design Structuring system (KADS) • Manual method for Structured and systematic development • Transforms knowledge from initial, informal to operational • Knowledge described in 4 layers: domain, inference, task and strategy layer

17. Domain layer Inference layer • Knowledge about the domain • Fundamental ontologies • Concepts, structures, relations • The inferences of application area • Meta-classes (roles that domain components may play in solving the problem) Task layer • Sequence in which the system should invoke knowledge on the inference layer • Control knowledge Strategy layer • How the problem solver can choose dynamically among alternative task layer sequences

18. Advantage • The 4 layers help to describe problem solving behavior and domain ontologies consistently Disadvantage • No natural semantic (subject of research) • A clear semantic could make this more useful

19. Use of predefined models • KADS: a broad methodology to describe abstract models of expertise • Some American researchers: Develop knowledge-engineering techniques that would guarantee an executable system. • Computer-based interviewing tools • Explicit model: The user knows the conceptual model. Relates the knowledge acquisition to the problem solving • Implicit model: The problem solving model is hidden from the user

20. Explicit models of problem solving ROGET • Assists the knowledge engineers and domain experts in developing EMYCIN-based expert systems. • Dialog with the user • Makes a computer-readable knowledge base SALT • Models strategies for constructing solutions • Assumes task can be mapped into a propose-and-revise method for problem solving • Requires a structured language for input

21. Implicit models of problem solving • Problem solving model hidden from the user ETS • Automated structured interview • System responsible for fitting the answers to the internal model • Impossible for the program itself to generate a robust model Explicit and implicit • Have to know that the task can be solved via a form of classification.

22. Task based conceptual models • Conceptual model must be appropriate for the domain • Conceptual models that mirror the way developers seem to think about the knowledge • More concrete models – made for the task at hand • OPAL – cancer treatment

23. Meta level knowledge acquisition tools • Knowledge acquisition programs that contains several models that the user can modify to fit the needs PROTEGE • Generate task-oriented tools like OPAL • Has its own problem solving model DOTS • Do not impose a particular problem solving method • Symbol-oriented – not method-oriented like PROTEGE

24. Conclusion • Early research: Eliminate knowledge engineer as middleman (ETS) • Special skills required to create robust models • Develop refined methods to assist KA