The role of the knowledge engineer. Knowledge acquisition. - PowerPoint PPT Presentation

Loading...

PPT – The role of the knowledge engineer. Knowledge acquisition. PowerPoint presentation | free to download - id: 56add5-ZTliY



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

The role of the knowledge engineer. Knowledge acquisition.

Description:

Knowledge acquisition. Software development: conventional systems and KBS You are probably familiar with a standard model of the software development life cycle. – PowerPoint PPT presentation

Number of Views:205
Avg rating:3.0/5.0
Slides: 46
Provided by: johnpl155
Learn more at: http://www.cwa.mdx.ac.uk
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: The role of the knowledge engineer. Knowledge acquisition.


1
The role of the knowledge engineer. Knowledge
acquisition.

2
Software development conventional systems and KBS
  • You are probably familiar with a standard model
    of the software development life cycle. It is
    likely to be something like this
  • Feasibility study
  • Analysis
  • Requirements definition
  • Design
  • Implementation
  • Testing
  • Maintenance review

3
Software development conventional systems and KBS
  • Knowledge-based systems require special
    approaches to systems analysis, especially to the
    collection of the data (or rather knowledge) on
    which the system is based.
  • We will discuss the ways in which this model
    needs to be modified to take account of these
    special features in lecture 8.

4
Knowledge Engineering
  • The term "knowledge engineering" is often used to
    mean the process of
  • designing
  • building
  • installing
  • an expert system or other knowledge- based
    system. In other words, the whole process of
    making a KBS, from beginning to end.

5
Knowledge Engineering
  • Some authors use the term to mean just the phase
    in which the knowledge base is built.

6
Building the knowledge base
  • Five processes can be identified
  • 1. Knowledge acquisition
  • 2. Knowledge analysis representation
  • 3. Knowledge validation
  • 4. Inference design
  • 5. Explanation and justification
  • These are not stages that have to follow each
    other - some of them will run concurrently.

7
Knowledge Acquisition
  • Knowledge acquisition is
  • The process of gathering the knowledge to stock
    the expert system's knowledge base.

8
Knowledge Acquisition
  • This has proved to be the most difficult
    component of the knowledge engineering process.
    It's become known as the 'knowledge acquisition
    bottleneck', and expert system projects are more
    likely to fail at this stage than any other.
  • This is the principle reason why expert systems
    have not become more widespread.

9
Knowledge Acquisition
  • Sources of knowledge
  • Documents textbooks, journal articles, technical
    reports, records containing case histories, etc.
  • This will almost never be sufficient to provide
    the knowledge base for a real-world expert
    system.
  • The range of problems which a textbook examines
    and solves is always smaller than the range of
    problems that a human expert is master of.

10
Knowledge Acquisition
  • Sources of knowledge
  • Human experts

11
Knowledge Elicitation
  • The most important part of knowledge acquisition
    is knowledge elicitation - obtaining knowledge
    from a human expert (or human experts) for use in
    an expert system.
  • Knowledge elicitation is difficult. Hence the
    knowledge acquisition bottleneck mentioned above.
  • It is necessary to find out what the expert(s)
    know, and how they use their knowledge.

12
Knowledge Elicitation
  • Expert knowledge includes
  • domain-related facts principles
  • problem-solving strategies
  • meta-knowledge - for instance, knowledge about
    when to use a particular piece of knowledge
  • explanations and justifications.

13
Knowledge Elicitation
  • The knowledge elicitation/analysis task involves
  • finding at least one expert in the domain who
  • is willing to provide his/her knowledge
  • has the time to provide his/her knowledge
  • is able to provide his/her knowledge.
  • - any or all of these are liable to prove
    difficult.

14
Knowledge Elicitation
  • The knowledge elicitation/analysis task involves
  • repeated interviews with the expert(s), probably
    combined with other, non-interview, techniques.

15
Knowledge Elicitation - the compiled knowledge
problem
  • One major obstacle to knowledge elicitation
    experts cannot easily describe all they know
    about their subject.
  • They do not necessarily have much insight into
    the methods they use to solve problems.
  • Their knowledge is "compiled" (like a compiled
    computer program - fast efficient, but
    unreadable).

16
Knowledge Elicitation - interview techniques
  • Some of the interview techniques used in
    knowledge elicitation
  • Unstructured interview. A general discussion of
    the domain, designed to provide a list of topics
    and concepts.
  • Structured interview. Concerned with a particular
    concept within the domain - a particular
    problem-solving skill or small group of skills.

17
Knowledge Elicitation - interview techniques
  • interview techniques
  • Problem-solving interview. The DE is provided
    with a real-life problem, of a kind that they
    deal with during their working life, and asked to
    solve it. As they do so, they are required to
    describe each step, and their reasons for doing
    what they do. The transcript of their verbal
    account is called a protocol.

18
Knowledge Elicitation - interview techniques
  • interview techniques
  • Think-aloud interview. As above, but the DE
    merely imagines that they are solving the problem
    presented to them, rather than actually doing it.
    Once again, they describe the steps involved in
    solving the problem.

19
Knowledge Elicitation - interview techniques
  • interview techniques
  • Critical incident analysis. The DE is asked to
    provide details of cases which were particularly
    difficult, or of special interest for some other
    reason. He/she describes how they were solved,
    and the lessons that were learnt.

20
Knowledge Elicitation - interview techniques
  • interview techniques
  • Dialogue. The DE interacts with a client, in the
    way that they would normally do during their
    normal work routine.

21
Knowledge Elicitation - interview techniques
  • interview techniques
  • Review. The KE and DE examine the record of an
    interview session together.

22
Knowledge Elicitation - non-interview techniques
  • Some of the non-interview techniques used in
    knowledge elicitation
  • Sample lecture preparation. The DE prepares a
    lecture, and the KE analyses its content.

23
Knowledge Elicitation - non-interview techniques
  • non-interview techniques
  • Concept sorting ("card sort"). The DE is
    presented with a series of cards, with the names
    of domain concepts written on them, spread out on
    a table top, and asked to arrange them into
    clusters, in such a way that the cards in each
    cluster have something important in common. Then
    the DE is asked to name the principles that
    he/she has used to form these clusters. This
    process can be repeated to produce a hierarchy of
    concepts.

24
Knowledge Elicitation - non-interview techniques
  • non-interview techniques
  • Repertory grid (particularly the "laddered grid"
    technique).
  • Questionnaires. Especially useful when the
    knowledge is to be elicited from several
    different experts.

25
Knowledge Elicitation - interview techniques
  • It is standard practice to tape-record KE
    sessions.
  • For something like a problem-solving interview,
    one would wish to videotape it as well.
  • However, KEs should be aware of the costs this
    involves, in time and money - it can take as much
    as 15 hours of secretarial time to transcribe and
    edit a one-hour interview.

26
Knowledge analysis representation
  • Simultaneously with the knowledge acquisition
    process, a knowledge analysis process takes
    place. The KE uses the data - the transcripts and
    protocols, etc - from the knowledge acquisition
    sessions to build a good model of the expertise
    that the DE is using to solve problems in the
    domain.

27
Knowledge analysis representation
  • The raw data (taken from the DE) is converted
    into intermediate representations. These are
    structured representations of the knowledge, but
    not yet the sort of coded knowledge that can be
    put into the knowledge base.
  • This will improve the knowledge engineer's
    understanding of the subject

28
Knowledge analysis representation
  • This will probably provide knowledge in a form
    that can be shown to the DE, for criticism and
    correction
  • This provides easily-accessible knowledge for
    future KEs to work from (knowledge archiving).
  • The intermediate representation is then converted
    into the knowledge representation formalism which
    is to be used in the KBS software.

29
Knowledge validation
  • It is necessary to verify the knowledge against
    the knowledge source (the expert or document).
  • It is also necessary to validate the knowledge
    against known outcomes.
  • The objective is to produce knowledge of high
    integrity.

30
Inference design
  • It may be necessary to design the software which
    will comprise the inference engine or a
    particular shell may already have been specified.

31
Explanation and justification
  • An explanation facility, capable of
    explaining/justifying any of the reasoning and
    conclusions that the system produces, needs to be
    designed and programmed.

32
Computer-assisted knowledge elicitation
  • Since knowledge engineering skills, and hence
    knowledge engineers, are rare (see appendix), it
    would be desirable to automate the job.
  • i.e. to write an expert system to do knowledge
    engineering.

33
Computer-assisted knowledge elicitation
  • The state of the art in AI (especially in natural
    language processing) is not sufficiently advanced
    to permit fully-automated knowledge elicitation.

34
Computer-assisted knowledge elicitation
  • However, 'knowledge elicitation workbenches', or
    'knowledge engineering environments', are
    commercially available
  • e.g. KEE, KnAcqTools, ETS, KRITON, AQUINAS
  • their principle use is to simplify the task of
    converting a protocol into frames, rules, etc.,
    and inserting these structures into an expert
    system shell as soon as they are formulated.

35
Fully computerised knowledge acquisition
  • It might be thought that one could avoid using a
    domain expert altogether, by building a system
    that could extract knowledge, given facts about
    the domain.
  • This is the approach taken by machine learning
    systems
  • "classic" machine learning systems such as ID3
    (Quinlan, 1979) AQ11 (Michalski Chilauski,
    1980)

36
Fully computerised knowledge acquisition
  • systems designed to provide knowledge for a
    particular system's knowledge base, e.g.
    META-DENDRAL, designed to discover rules for the
    rule-base in DENDRAL
  • data mining systems these do a similar job to
    classic machine learning systems, but work on a
    very large database of information.
  • sub-symbolic systems, i.e. neural nets and
    genetic algorithms. More about these in the last
    lecture in this course.

37
Fully computerised knowledge acquisition
  • There are plenty of examples of machine learning
    systems producing formerly-unknown knowledge, and
    knowledge that was better than that of a domain
    expert

38
Knowledge discovery
  • e.g.(1) META-DENDRAL
  • produced rules about the behaviour of molecules
    in a mass spectroscope that were published in a
    chemistry journal as original contributions to
    the field

39
Knowledge discovery
  • e.g.(2) AQ11
  • produced rules about how to diagnose diseases in
    Soya bean plants.

40
(No Transcript)
41
(No Transcript)
42
(No Transcript)
43
  • AQ11s rules were correct 97 of the time. The
    domain expert's rules were correct 83 of the
    time he abandoned his rules, and adopted AQ11's
    rules instead.

44
Knowledge discovery
  • e.g.(1) META-DENDRAL produced rules about the
    behaviour of molecules in a mass spectroscope
    that were published in a chemistry journal as
    original contributions to the field
  • e.g.(2) AQ11 produced rules about how to
    diagnose diseases in Soya bean plants. They were
    correct 97 of the time. The domain expert's
    rules were correct 83 of the time he abandoned
    his rules, and adopted AQ11's rules instead.

45
Fully computerised knowledge acquisition
  • This approach is particularly fruitful in
    'knowledge-poor' domains, i.e. domains where not
    much expert knowledge is available.
  • However, it is a mistake to believe that one can
    do machine learning without a domain expert - at
    the very least, you need an expert to select the
    training examples, and to explain the domain
    terminology. Probably also to identify the
    features of the examples which are likely to be
    relevant.
About PowerShow.com