Knowledge Acquisition and Modelling

Knowledge Acquisition and Elicitation

Ref: Knowledge Acquisition in Practice: A step by step guide, Milton, Springer-Verlag

Knowledge Engineering Transfer View

Human knowledge transferred to knowledge base =>knowledge exists and is accessible Typically interviews and task execution and

observation used for KA End result set of rules that exercise knowledge

made explicit Modelling View

Need to build models Incremental, evolutionary process Model is an approximation of reality Models are subjective

KA Typology

KA techniques

natural techniques

contrived techniques

modelling techniques

interviews

observation techniques

group meetings

questionnaires

unstructured interview

semi-structured interview

structured interview

card sorting

three card trick

rep grid technique

constrained tasks

20-questions

commentating

teach back

limited time

limited information

laddering

process mapping

concept mapping

state diagram mapping

Natural Techniques

Interview Techniques Knowledge engineer asks questions of the expert or end

user. Essential method for acquiring explicit conceptualisations

and knowledge, but poor for tacit knowledge. Variations:

Unstructured interview Free flowing, used in early stages of elicitation, can produce basics of

knowledge domain, basically broad chat Semi-structured interview

Main technique for elicitation Pre-defined questions sent to expert prior to interview,

supplementary questions asked at interview. Can be used as part of validation.

Structured interview Pre-defined set of questions, can simply be filling in a questionnaire

at the interview.

Interview Techniques Dependent on

The questions asked Ability of the expert to articulate the knowledge

Model built on knowledge elicited during interview

Model reviewed by the expert

Modelling Techniques

Modelling Techniques Use of knowledge models with experts Used as validation and refinement Can show a basic model to an expert and

prompt them to modify. Can show a complete model of knowledge

provided by one expert to a second expert to cross-validate.

Can create one from scratch with an expert – start with a blank page

Model Based Knowledge Acquisition Each model emphasizes certain aspects of the

system to be built and abstracts from others. Each model is indicative of one view of the

world Models provide a decomposition of

knowledge-engineering tasks: while building one model, the knowledge engineer

can temporarily neglect certain other aspects.

Knowledge Modelling Process

Knowledge Modelling Use skeletal models Or generic tasks

Generic tasks are templates of problem-solving activities that can be configured together to describe any intelligent activity.

Modelling Frameworks

Knowledge Modelling At least five different types of knowledge are

distinguished: Tasks-goals

correspond to the goals that must be achieved during problem solving. Problem-solving methods

ways to achieve the goals described in tasks. In some knowledge modelling frameworks, problem-solving methods define subtasks to which other problem solving methods can be applied. We will call such a decomposition a task instance.

Inferences describe the primitive reasoning steps in the problem solving process.

Ontologies describe the structure and vocabulary of the static domain knowledge.

Domain knowledge refers to a collection of statements about the domain.

Principles Divide and conquer. Configuration of an adequate model set for a

specific application. Models evolve through well defined states. The model set supports project management. Model development is driven by project

objectives and risk. Models can be developed in parallel.

Recommended ReadingKnowledge Engineering: Principles and MethodsRudi Studer, V. Richard Benjamins and Dieter

Fense Data & Knowledge Engineering (1998)Volume: 25, Issue: 1-2, Publisher: Elsevier http://www.hubscher.org/roland/courses/hf760

/readings/studer98knowledge.pdf

Contrived Techniques

Knowledge Capture – Specialised Techniques Contrived Techniques Primarily for deep, tacit knowledge Involve the expert performing tasks they

would not normally do as part of their job. Most of these techniques come from

psychology

Knowledge Capture – Specialised Techniques Important types:

Concept (card) Sorting Three Card Trick (Triadic) Repertory Grid Technique Constrained Tasks 20-questions Commentary Teach Back

Usually involve expert doing two types of task: Tasks they normally perform

Commentary is useful here Tasks designed to probe the expert

Concept sorting or Triadic

Concept (Card) Sorting Way of finding out how an expert compares and orders

concepts Can reveal knowledge about classes, properties and relations

Works best in small groups Simplest form is card sorting

Collection of concepts (or other knowledge objects) are written on separate cards

Cards sorted into piles by an expert in to piles - each card in a pile must have something in common

Each time the cards are sorted it will be based on an attribute and each pile will represent a value

Enables significant elicitation of properties and dimensions Used to capture concept knowledge and tacit knowledge Use in conjunction with triadic method Can also sort objects or pictures instead of cards

Concept Sorting – How To ? Decide what classes of concepts you want to

explore (in particular their properties – attributes and values)

Write the name of each concept on a separate card

At the session explain to the expert what is going to happen

Ask the expert to name the piles Write down (or record) the results of the sort Collect the cards and ask the expert to sort

again Repeat until the expert can’t sort anymore

Triadic Elicitation Method (3 card trick) Used to capture the way in which an expert views the

concepts in a domain. Present three random concepts and ask in what way two

of them are similar but different from the other one. Answer will give an attribute. A good way of acquiring tacit knowledge. How does it work ?

Select 3 cards at random Identify which 2 cards are the most similar?

– Why? – What makes them different from the third card?

Helps to determine the characteristics of our classes Picking 3 cards forces us into identifying differences between

them There will always be two that are “closer” together Although which two cards that is may differ depending on your

perspective

Triadic Elicitation – How To? Explain to the expert that you are trying a technique to

draw out deeper knowledge Collect all cards previously used Shuffle cards and randomly select 3 Place them on the table, two close together one further

away Ask how the two close together are similar and the other

different Write down (or record) what the expert says using an

attribute Use the results to find another card sort to find the values

of all concepts for this attribute If the expert can’t identify an attribute, just pick another

3 cards Repeat until the expert can think of no more differences

20-Questions Expert asks questions of the engineer The Knowledge Engineer thinks of an object/concept in the

domain Expert asks yes/no questions to the knowledge engineer in

order to deduce an answer. Knowledge Engineer

notes the questions and the order in which they are asked need not know much about the domain, or have an answer in mind,

just answer “yes” or “no” randomly The questions asked provide a good way of quickly acquiring

attributes in a prioritised order. Can provide an insight into the key aspects, properties or

categories and their relative priorities. Note that the main purpose of this exercise is not really to try

and find out what the Engineer is thinking of, but to determine the important properties!

20-Questions – How To? Decide on set of concepts you need to explore in more detail Explain to the expert what is going on Ask the expert to imagine that you the engineer have the same level of

knowledge they do about the set of concepts Instruct the expert that they should ask the least number of questions

to deduce the answer Engineer can only answer yes and no Explain that the best way is to ask questions which split the concepts

in half so that each question eliminates half the possible solutions Start As each question is asked write it down (or record it) When a number of questions have been asked take the expert back to

an earlier question and change the answer you gave to prompt the expert to ask further questions

After the session extract the attributes and values (or new concepts) from the questions asked and these will be added to the knowledge base

Laddering Involves the construction, modification and validation of

trees. Accessing personal construct system Take a group of things and ask what they have in

common Then what other ‘siblings’ (brothers/sisters) there might

be A valuable method for acquiring concept knowledge and,

to a lesser extent, process knowledge. Can make use of various trees:

concept tree composition tree attribute tree process tree decision tree cause tree

Example

Source: Bourne and Jenkins , Eliciting Managers' Personal Values: An Adaptation of the Laddering Interview Method, Organizational Research Methods, SAGE 2005

Concept Tree Hierarchical diagram of concepts showing classes and

members Activities to create

Move nodes (concepts) around the tree Add new node Deleting nodes Renaming nodes

Difficulty is avoiding the problems which requires knowing: All links on the tree represent an ‘is-a’ relationship Terminology to describe the tree What classes to use in the tree Naming conventions to use How to deal with complex cases (e.g. multiple parents,

synonyms)

Concept Tree – ‘is-a’ relationship

Is-a = is a type of Different to ERDs

vehicle

traffic

ship

traffic issues

lorry

car

steam ship

sailing ship

shipping lanes

pollution

congestion

Road safety

What are the

mistakes in this tree?

Concept Tree - Terminology Root node Leaf node Branch Parent Children Descendants

Concept Tree – What classes to use? Class is a concept which has children on a tree Other concepts are related to it by an is-a

relationship To develop classes use either a top-down or

bottom-up approach Top-down start with a set of general classes and

refine Bottom-up start to develop classes by grouping

those concepts that are similar

Repertory Grid technique Used to elicit attributes for a set of concepts Used to rate concepts against attributes using a

numerical scale Uses statistical analysis to arrange and group

similar concepts and attributes Allows the expert to provide a rating of each

concept for an attribute in concept sorting A useful way of capturing concept knowledge and

tacit knowledge When many ratings are provided using many

attributes statistics can be applied to find clusters and correlations

Requires special software

Repertory Grid – How To? 1st stage

Concepts are selected (between 6 & 15) Set of approx. same no. of attributes is also required

Should be such that values can be rated on a continuous scale (e.g. small to large)

Chosen from knowledge previously elicited

2nd stage Expert provides a rating for each concept against each attribute Numerical scale is used

3rd stage Ratings are applied to cluster analysis to create a visual representation of

the ratings called a focus grid Concepts with similar scores will be grouped together, attributes with

similar scores will be grouped 4th stage

Engineer walks expert through the results to gain feedback and prompt for further knowledge about the groupings

If needed more concepts and attributes are rated and included in the grid

Repertory Grid Example

Domain elements are certain types of crime: petty theft, burglary, drug-dealing, murder, mugging and rape.

This is one expert’s view on the issue. Consider carefully whether any pair of

attributes are very similar, by comparing horizontal lines in this grid. The closest is probably the personal/impersonal

one and the major/petty one. Beware, when making this comparison, that

the expert may have inadvertently ‘inverted’ the scale for just one of two similar constructs. For example, in the example the major/petty

construct has a value of 5 for ‘major’. If the expert had chosen 1 instead, and 5 for ‘petty’, then this construct and the personal/impersonal one would look very different.

Further analysis may lead you to omit one pairing of constructs.

Following that you would draw up a table showing how similar or dissimilar each domain element is from the others.

For example, when the absolute-value metric is used, the (numeric) difference.

Constrained Tasks Expert performs a task they would normally

do, but with constraints. Variations:

limited time limited data

Useful for focusing the expert on essential knowledge and priorities

Commentary and protocol generation Expert provides a running commentary of

their own or another’s task performance. A valuable method for acquiring process

knowledge and tacit knowledge. Variations:

self-reporting imaginary self-reporting self-retrospective shadowing retrospective shadowing

Knowledge Analysis and Modelling

Knowledge Analysis Identifying the elements needed to build the

knowledge base Concepts

Things that constitute a domain Main elements of the k-base

Attributes Qualities or features belonging to a class of concepts

Values Specific qualities or features of a concept that

differentiate it from other concepts Relations

Way in which concepts are associated with one another

Concepts Physical concepts

Products, components, machines

Pieces of information Plans, goals, requirements

Sources of information Documents, databases,

websites People and roles

Experts, roles of experts Organisations and groups

Producers, suppliers, consumers, departments

Areas of knowledge Marketing, physics,

chemistry

Functions Purpose of components or

roles Tasks

Activities performed by experts

Issues Problems, solutions,

advantages, disadvantages Physical phenomena

Mechanisms and forces Other issues

Constraints, behaviours, states

Attributes Of physical objects

Shape, age Of information

Source, format, importance Of people

Gender, age, personality Of organisation

Size, turnover, product range

Values Come in different varieties Dependent on type

Adjective, number, sentence, paragraphs, hyperlinks, images, pictures

Categorical For values that are adjectives

Numerical For values that are numbers

Text For values that are one or two sentences

Hypertext For values that are chunks of hypertext

Relations Has part Performs Followed by Requires Causes Produces Can have an inverse relation Short exercise

Think of something that illustrates each one of these

Knowledge modelling K-model = way of viewing the knowledge in

the k-base Each model provides a different perspective

on the knowledge Helps clarify the ‘mess’ that is the knowledge Can be used in elicitation

Trees Diagram showing hierarchical arrangement of

nodes Node = concept Link = relationship Concept tree Composition tree Cause tree Mixed tree

Concept tree Each link is an is-a

relation Taxonomy Read from right to

left

Taken from www.pcpack.co.uk

Other types of tree Composition tree

All links are has-part Used to show components and sub-components of a

concept Process tree

Special form of composition tree All nodes are tasks

Attribute tree Shows attributes and values to describe a concept

Mixed tree Contains more than one type of relation

Matrices

Attribute matrix Presents set of

properties of a concept (attributes and values)

Concepts on vertical axis

Attributes and values on horizontal axis

Relationship matrix Shows two sets of

concepts related to one another using a specified relationship

Cells show which pairs of concepts have the relationship

Maps Shows an arrangement of nodes linked by

arrows Each node represents concept Link represents relationship Concept maps Process maps

Concept map

Many different types

Knowledge Analysis – How to? How do you identify concepts from interview

transcripts and documents? Need some codification Highlighters – different colours for different

things

Typical project 47 steps proposed by Milton Knowledge Acquisition in Practice: A step by

step guide, Milton, Springer-Verlag Phase I

Start, scope and plan the project Phase II

Initial capture and modelling Phase III

Detailed capture and modelling Phase IV

Share and store knowledge

Phase I – Start, Scope and Plan Identify a project

How it can benefit, what it involves Gather opinions from relevant people Document ideas as project proposal Seek agreement on proposal from key people Start knowledge capture With domain experts break the domain into different topics

and rank against key criteria Identify a proposed scope and finalise Identify sources of knowledge Define and understand the type of project to be able to

create a schedule Collate the proposal, scope and schedule into a project

plan and disseminate with other materials to team

Phase II – Initial Capture and Modelling Learn the basics of the domain from documents and

informal conversation with experts Prepare for semi-structured interviews then execute and

transcribe Analyse results to identify concepts, create a concept

tree to develop a taxonomy and validate with experts Create a k-page for each concept

K-page = 2 column table showing all knowledge associated with a concept

Create a glossary Build a meta-model showing the relationships between

concepts and relationships Build appropriate k-models Continue with validation models

Phase III – Detailed Capture and Modelling Use further interviews and modelling to

capture more detailed knowledge Finalise k-model Prepare prototype end product used to carry

out assessment exercise with sample of end-users

Produce a completion plan defining what needs to be done to complete the project

Use specialised techniques to do detailed knowledge capture

If needed cross-validate between experts and resolve conflicts

Phase IV – Share and store knowledge Define and create format of end-product Create provisional end-product using k-base Give to experts for full validation Create final end-product and release for use After use for some time assess impact on

organisation and document it Conduct complete product review to learn

lessons and make suggestions to change methodology

Ensure end-product is useful, usable and used End-users must find

Find product useful Find product easy to use Actually use it