Understanding Understanding Knowledge
y
Knowledge can be defined as the ``understanding obtained through the process of experience or appropriate study.''
y
Knowledge
y
can also be an accumulation of facts, pr ocedural rules, or heuristics.
o
A fact is
o
A procedural rule
generally a statement representing truth about a subject matter or domain.
o
A heuristic is
is a rule that describes a sequence of actions.
a rule of thumb bas ed on years of experience.
Intelligence implies the capability to acquire a nd apply appropriate knowledge. o
Memory indicates the ability to store and retri eve relevant experience according to will.
o
Learning represents the skill of acquiring knowledge using the method of instruction/study.
y
Experience Experience relates to the understanding that we develop through our past actions.
y
Knowledge
y
Common sense
can develop over time through successful experience, and experience can lead to expertise. refers to the natural and mostly unreflective opinions of humans.
Cognitive Psych Psychology
y
sychology tries to identify the cognitive structures and processes that closel y relates to skilled performance within an area of Cognitive psych operation.
y
It
y
In
y
The
provides a strong background for understanding knowledge and expertise. general, it is the interdisciplinary study of human intelligence. two major components of cognitive psychology are: o
Experimental Experimental Psych Psychology: This studies the cognitive processes that constitutes human intelligence.
o
Artif rtificial Intelligence(AI): I) : This
studies the cognition of Computer-based intelligent systems.
y
The
process of eliciting and r epresenting experts knowledge usually involves a knowledge developer and developer and some human experts (domain experts).
y
In
y
It
y
The
y
Hence,
order to gather the knowledge fr om human experts, the developer usually interviews the experts and asks for information regarding a specific area of expertise. is almost impossible for humans to provide the completely accurate reports of their m ental processes.
research in the ar ea of cognitive psychology helps to a better understanding of w hat constitutes knowledge, how knowledge is elicited, and how it should be represented in a corporate knowledge base. cognitive psychology contributes a great deal to the ar ea of knowledge management.
Data, Information and Knowledge
y
y
y
Data represents
unorganized and unprocessed facts.
o
Usually
o
It
data is static in nature.
can represent a set of dis crete facts about events.
o
Data
o
An
is a prerequisite to information.
organization sometimes has to decide on the nature and v olume of data that is requir ed for creating the necessary information.
Information o
Information
can be considered as an a ggregation of data (processed data) which makes decision making easier.
o
Information
has usually got some meaning and purpose.
Knowledge o
By knowledge we mean human understanding of a subject matter t hat has been acquired through proper st udy and experience. experience.
o
Knowledge
is usually based on learning, thinki ng, and proper understanding of the probl em area.
o
Knowledge
is not information and information is not data.
o
Knowledge
is derived from information in the same way information i s derived from data.
o
We
o
It
can view it as an understanding of information based on i ts perceived importance or relevance to a problem area.
can be considered as the integration of human perceptive processes that helps them to draw meaningful conclusions.
Figure 1.1: Data, Information, Knowledge and Wisdom
Kinds of of Knowledge
y
Deep Knowledge: Knowledge
y
Shallow Knowledge:
acquired through years of proper experience.
Minimal understanding of the problem area.
y
Knowledge as Know-How: Accumulated
y
Reasoning
lessons of practical experience.
and Heuristics: Some of the ways in which humans reason are as follows:
o
Reasoning
o
Formal Reasoning: This
by analogy: This indicates relating one concept to another. indicates reasoning by using deductive (exact) or inductive or inductive reasoning.
Deduction
In
Inductive
reasoning implies reasoning from a set of f acts to a general conclusion.
Inductive
reasoning is the basis of scientific discovery.
A
uses major and minor premises.
case of deductive reasoning, new knowledge is generated by using previously specified knowledge.
case is knowledge associated with an operational level.
y
Common Sense: This implies a type of knowledge that almost every human being possess i n varying forms/amounts.
y
We
y
can also classify knowledge on the basis of whether it is procedural, declarative, semantic, or episodic or episodic.. o
P rocedural knowledge
o
Declarative knowledge is
routine k nowledge about which the expert is conscious. It is shallow knowledge that can be readily recalled since it consists of sim ple and uncomplicated information. This type of knowledge often resides in s hort-term memory.
o
Semantic knowledge is
o
Episodic knowledge represents
Another
represents the understanding of how to carry out a specific procedure.
highly organized, ``chunked'' knowledge that resides mainly in long-term memor y. Semantic knowledge can include major concepts, vocabulary, facts, and relationships. the knowledge based on episodes (experimental information). Each episode is usually ``chunked'' in long-term memory.
way of classifying knowledge is to find whether it is tacit or explicit or explicit
o
T acit acit knowledge usually
o
E xplicit knowledge is
gets embedded in human mind through experience.
that which is codified and digitized in documents, documents, books, reports, spreadsheets, memos etc.
Expert Expert Knowledge
It
is the i nformation woven inside the mind of an expert for accurately and quickly solving complex problems.
y
y
Knowledge Chunking o
Knowledge
is usually stored in experts long-range memory as chunk s. s.
o
Knowledge
chunking helps experts to optimize their memory capacity and enables them to process the information quickly.
o
Chunks are groups of ideas that are stored and recalled together as an unit.
Knowledge
as an Attribute of Expertise
o
In
most areas of s pecialization, insight and knowledge accumulate quickly, and the criteria for expert performance usually undergo continuous change.
o
In
o
The
o
The
true experts (knowledgeable) are usuall y found to be more selective about the information they acquire, and also they are better able i n acquiring information in a less s tructured situation.
o
They
o
Hence,
o
If
o
Nonexperts
o
Nonexperts
o
Specific
order to become an expert in a particular area, one i s expected to master the necessary k nowledge and make significant contributions to the concerned field. unique performance of a true expert can be easily noticed in the quali ty of decision making.
can quantify soft information, and can categori ze problems on the basis of solution procedures that are embedded in the experts long range memory and r eadily available on recall. they tend to use knowl edge-based decision strategies starting with known quantities to deduce unknowns.
a first-cut solution path fails, then the expert can trace back a few steps and then proceed again. use means-end decision strategies to approach the the pr oblem scenario.
usually focus on goals r ather than focusing on essential features of the task which makes the task m ore time consuming and sometimes unreliable. individuals are found to consistently perform at hi gher levels than others and they are labeled as experts.
Thinking and Learning in Humans
y
Research
y
Humans
y
Humans
y
On
in the area of ar tificial intelligence has introduced more structure into human thinking about thinking.
do not necessarily r eceive and process information in exactly the same way as the machines do.
can receive information via seeing, sm elling, touching, hearing (sensing) etc., which promotes a way of thinking and learning that is unique to humans. macro level, humans and computers can receive inputs fr om a multitude of sources.
y
Computers can receive inputs from keyboards, touch screens etc.
y
On
y
The
micro level, both human brain and CPU of a computer receive information as electrical impulses.
point to note here is that the computers must be programmed to do specific tasks. Performing one task does not necessarily transcend onto other tasks as it may do with humans.
y
Human
learning: Humans learn new facts, integrate them in some way which they think is r elevant and organize the result to produce necessary solution, advice and decision. Human learning can occur in the following ways: o
Learning
through Experience.
o
Learning
by Example.
o
Learning
by Discovery.
Knowledge Management Systems Life Cycle
Challenges in KM K M Systems Develop evelopment
y
Changing Organizational Culture:
y
Knowledge Evaluation:
o
o y
y
Knowledge
Involves
Involves
changing people's attitudes and behaviors. assessing the worth of inform ation.
Processing:
o
Involves
o
Sometimes
the identification of techniques to acquire, store, pr ocess and distribute information. it is necessary to document how certain decisions were reached.
Knowledge Implementation: o
An
o
It
organization should commit to change, learn, and i nnovate.
o
Lessons
is important to extract meaning from information that may have an impact on s pecific missions. learned from feedback can be stored for future to help others facing the similar problem(s).
Conventional Vs K M Systems Life Cycle (KM (K MSLC)
Key Differences
o
The
o
The
systems analyst gathers data and infor mation from the users and the users depend on analysts for the solution.
knowledge developer gathers knowledge from people with known knowledge and the developer depends on them for the solution.
o
The
o
The
main interface for the systems anal yst is associated with novice users who know the problem but not the solution.
main interface for the knowledge developer is ass ociated with the knowledgeable person who knows the probl em and the solution.
y
Conventional systems development is primarily sequential, whereas KM SLC is incremental and interactive.
y
In case of conventional systems, testing is usually done towards the end of the cycle (after KM SLC, the evolving system is verified and validated from the beginning of the cycle.
y
Systems
y
The
the system has been built), whereas i n
development and systems management is much more extensive f or conventional information systems than it is for KM SLC.
conventional systems life cycle is usually process-driven and documentation-oriented whereas KM SLC is result-oriented.
o
The conventional systems development does not support tools such as rapid prototyping since it follows a predefined sequence of steps
o
KM SLC
can use rapid prototyping incorporating changes on the spot.
Figure 2.1: Rapid Prototyping
Key Similarities
y
Both
y
The
cycles starts with a problem and end with a solution.
y
V erification and validation of a KM
y
Both
early phase in case of conventional systems development life cycle starts with information gathering. In KM SLC the early phase needs knowledge capture. system is often very similar to conventional systems testing.
the systems analyst and the know ledge developer needs to choose the appropriate tools for designing their intended systems.
Figure 2.2: Users
and Experts: A Comparison
KMSLC Approac pproach hes
y
Primarily due to lack of s tandardization, a number of approaches have been proposed for KM SLC.
y
Refer
y
The
to Table 3.2 in page 65 of your textbook for a list of representative approaches, and refer toFigure 3.3 in page 66 of your textbook for a proposed hybrid life cycle. conventional systems development approach can still be used for developing KM systems, but it is usually being replaced by iterative design, prototyping etc.
Evaluating th t he Existing Existing Infrastructure
KM
systems ar e developed in order to satisfy the need for improving pr oductivity and potential of employees and the company as a whol e.The existing knowledge infrastructure is evaluated so that it can give the perception that the present w ays of doing things ar e not just abandoned in preference for a new system.
System Justif Justification: It involves answers to the following questions:
Is
existing knowledge going to be lost through retirement, , transfer, or departure to other organizations?
y
Is
the proposed KM system needed in multiple locations?
y
Are
y
experts available and willing to s upport the building of the proposed KM system?
y
Does
the concerned problem needs years of proper experience and cognitive reasoning to solve?
y
While
undergoing knowledge capture, would it be poss ible for the expert to arti culate how the problem will be s olved?
y
How
y
Are
critical is the knowledge that is to be captured?
y
Would
the involved tasks nonalgorithmic in nature? it possible to find a champion within the organization?
Scop Scoping: According to the textbook, the term scoping term scoping means limiting the breadth and depth of the pr oject within the financial, human resource, and operational constraints. constraints.
Feasibility: Feasibility
The
study involves addressing the following questions:
y
Is
it possible to complete the project wi thin the expected timeframe?
y
Is
the project affordable?
y
Is
the project appropriate?
y
How
frequently the system would be consulted at w hat will be associated cost?
traditional approach used to conduct a feasibili ty study can be used for buil ding a KM system. This involves the following tasks:
y
Forming
a knowledge management team.
y
Preparing a master plan.
y
Performing cost/benefit analysis of the proposed system.
y
Quantifying
system criteria and costs.
User Supp Support ort
y
Is
the proposed user awa re of the fact that the new KM system is being developed? How it is perceived?
y
How
y
What
much involvement can be expected from the user w hile the building process continues? type of users training will needed when the proposed system is up and running?
y
What
kind of operational support should be provided?
of Strategic Planning Role of
y
As
a consequence of evaluating the exis ting infrastructure, the concerned organization should develop a strategic plan which s hould aim at advancing the objectives of the organi zation with the proposed KM system in mind.
y
Areas
to be considered: o
Vision
o
Resources
o
Culture
Figure 2.3:
Matching business strategies with KM strategies
K M team Forming a KM
Forming
a KM team usually means
y
Identifying
y
Strategically,
Factors
the key units, branches, and divisions etc. as the k ey stakeholders in the prospective KM system. technically, and organizationally balancing the team si ze and competency.
impacting team success:
y
Quality
y
Size
and capability of team members (in terms of personality, experience, and communication skill).
of the team.
y
Complexity of the project.
y
Team
y
Promising only what that can be actual ly delivered.
motivation and leadership
Capturing Knowledge
y
Capturing Knowledge involves extracting, analyzing and interpreting the concerned knowledge that a human expert uses to solve a specific problem.
y
Explicit knowledge is usually captured in r epositories from appropriate documentation, files etc.
y
Tacit
y
Interviewing
knowledge is usually captured from experts, and from organization's stored database(s).
y
Data
y
In KM
y
Knowledge
y
Knowledge
is one of the most popular methods used to capture knowledge.
mining is also useful in terms of using intelligent agents that may analyze the data warehouse and come up wi th new findings.
systems development, the knowledge developer acquires the necessary heuristic knowledge from the experts for building the appropriate knowledge base. capture and knowledge transfer are often carried out through teams (refer to Figure 2.4).
capture includes determining feasibility, choosing the appropriate expert, tapping the experts knowledge, retapping knowledge to plug the gaps in the system, and verify/validate the knowledge base (refer to Table 3.4 in page 76 of your textbook).
Figure 2.4:
Matching business strategies with KM strategies
of Rapid Prototyp Prototy ping The Role of
y
In
y
Foe
most of the cases, knowledge developers use iterative approach for capturing knowledge.
y
The
example, the knowledge developer may start wi th a prototype (based on the somehow limited knowledge captured from the expert during the first few sessions). following can turn the approach into rapid prototyping: o
y
The
Knowledge
developer explains the preliminary/fundamental procedure based on rudimentary knowledge extracted from the expert during the few past sessions.
o
The
o
While
expert reacts by saying certain remarks.
o
The
the expert watches, the know ledge developer enters the additional knowledge into the computer-based system (that represents the prototype). knowledge developer again runs the modified prototype and continues adding additional knowledge as s uggested by the expert till the expert is satisfied.
spontaneous, and iterative process of building a knowledge base is referr ed to as rapid prototyping. prototyping.
Expert Expert Selection
The
expert must have excellent communication skill to be abl e to communicate information understandably and in suffi cient detail.
Some
common questions that may ari se in case of expert sel ection:
y
How
y
Will
to know that the so-called expert is in fact an expert?
y
What
y
How
he/she stay with the project till its completion? backup would be available in case the expert loses interest or quits?
is the knowl edge developer developer going to k now what does and what does not lie within the expert's ar ea of expertise?
The Role of of the Knowledge Develop eveloper
y
The
knowledge developer can be considered as the architect of the system.
y
He/she identifies the problem domain, captures knowledge, writes/tests the heuristics that represent knowledge, and co-ordinates the entire project.
y
Some
necessary attributes of knowledge developer: o
Communication skills.
o
Knowledge of knowledge capture tools/ technology.
o
Ability
o
Tolerance
o
To
o
Ability
to work in a team with professional/experts. for ambiguity.
be able ti think conceptually. to frequently interact with the champion, knowl edge workers and knowers in the organization.
Figure 2.5: Knowledge Developer's Role
KM Bluep Bluep rint Designing t he KM
This
phase indicates the beginning of designing the IT infrastructure/ Knowledge Management infrastructure. The KM Blueprint (KM system design) addresses a number of issues.
y
Aiming
y
Finalizing
for system interoperability/scalability with existing IT infrastructure of the organization. the scope of the proposed KM system.
y
Deciding
about the necessary system components.
y
Developing
the key layers of the KM architecture to meet organization's requirements. These layers are:
o
User
o
Authentication/security layer
interface
o
Collaborative agents and filtering
o
Application
o
Transport
o
Physical layer
o
Repositories
layer
internet layer
Testing th t he KM KM System
This
phase involves the following two steps:
y
Verif Verification Procedure: Ensures that the system is right, i .e., the programs do the task that they are designed to do.
y
Validation Procedure: Ensures that the system is the ri ght system - it meets the user's expectations, and will be usable on demand.
Implementing th t he KM KM System
y
After
capturing the appropriate knowledge, encoding in the knowledge base, verifying and validating; the next task of the know ledge developer is to implement the proposed system on a s erver.
y
Implementation means converting the new KM
y
Conversion
y
Some
system into actual operation.
is a major step i n case of implementation.
other steps are post implementation review and system and system maintenance. maintenance.
Quality Assurance
It
indicates the development of controls to ensure a qual ity KM system. The types of errors to look for:
y
Reasoning
errors
y
Ambiguity
y
Incompleteness
y
False
representation
Training Users
y
The
y
Users can range from novices (casual users with very limited knowledge) to experts (users with prior IT experience and knowledge of latest technology).
level/duration of training depends on the user's k nowledge level and the system's attributes.
y
Users can also be classified as tutors (who acquires a working knowl edge in order to keep the system current), pupils (unskilled worker who tries to gain some understanding of the captured knowledge), or customers or customers (who is interested to know how to use the KM system). system).
y
Training
y
Training can be supported by user manuals, explanatory facilities, and job aids.
should be geared to the specifi c user based on capabilities, experience and system complexity.
Managing Change
Implementation
means change, and organizational members usually r esist change. The resistors may include:
y
Experts
y
Regular
y
Troublemakers
y
Narrow
Resistance
employees (users)
minded people
can be seen in the form of following personal reactions:
y
Projection, i.e., hostility towards peers.
y
Avoidance,
y
Aggression.
i.e., withdrawal from the scene.
Post system Evaluation
Key
questions to be asked in the post implementation stage:
y
How
y
Has
the new system caused organiz ational changes? If so, how constructive are the changes?
y
Has
the new system affected the attitudes of the end us ers? If so, in what way?
y
How
y
In
y
Do
the new system improved the accuracy/timeliness of concerned decision making tasks?
the new system changed the cost of busi ness operation? How significant has it been?
what ways the new s ystem affected the relationships between end users in the organization? the benefit obtained from the new s ystem justify the cost of investment?
Implications for KM KM
The
managerial factors to be considered:
Some
y
The
organization must make a commitment to user training/education prior to building the system.
y
Top
Management should be informed with cost/benefit analysis of the proposed system.
y
The
knowledge developers and the people with potential to do knowledge engineering should be properly trained.
y
Domain
y
The
experts must be recognized and rewarded.
organization needs to do long-range strategic pl anning.
questions to be addressed by the management r egarding systems maintenance:
y
Who
will be the i n charge of maintenance?
y
What
skills the maintenance specialist needs to have?
y
What
would be the best way to train the maintenance specialist?
y
What
incentives should be pr ovided to ensure quality maintenance?
y
What
types of support/funding will be required?
y
What
relationship s hould be established between the maintenance of the KM system and the IT staff of the organization?
Knowledge Creation & Knowledge Arch rchitecture
Knowledge Creation
y
Knowledge
update can mean creating new knowledge based on ongoing experience in a specific domain and then using the new knowledge in combination with the existing knowledge to come up wi th updated knowledge for knowledge sharing.
y
Knowledge
y
A
team can commit to perform a job ov er a specific period of time.
y
A
job can be regarded as a series of specific tasks carried out in a specific order.
y
When
can be created through teamwork (refer to Figure 3.1)
the job is completed, then the team compares the experience it had i nitially (while starting the job) to the outcome (successful/disappointing).
y
This
y
While
comparison translates experience into knowledge.
y
Over time, experience usually leads to expertis e where one team (or individual) can be known for handling a complex problem very well.
y
This
performing the same job in future, the team can take corr ective steps and/or modify the actions based on the new knowledge they have acquired.
knowledge can be transferred to others in a reusable format.
Figure 3.1: Knowledge Creation/Knowledge Sharing
y
There
exists factors that encourage (or retard) knowledge transfer.
y
Personality is one factor in case of knowledge sharing.
via Teams
y
For
example, extrovert people usually posses self-confidence, feel secure, and tend to share experi ences more readily than the introvert, self-centered, and security-conscious people.
y
People with positive attitudes, who usuall y trust others and who work i n environments conductive to knowledge sharing tends to be better in sharing knowledge.
y
V ocational
y
People whose vocational needs are sufficiently met by job r e enforcers are usually found to be mor e likely to favor knowl edge sharing than the people who are deprived of one or more re enforcers.
re enforcers are the key to knowl edge sharing.
Figure 3.2: Impediments to Knowledge Sharing
Model el of Know Knowle ledg e Creati on & Transf o rmati on Nonaka's Nonaka's Mod dge
In
1995, Nonaka coined the terms tacit knowledge and explicit knowledge as the two main types of human knowledge. The key to knowledge creation lies in the way i t is mobilized and converted through technology.
y
Tacit to ta cit communication (Socialization): Takes
place between people in meetings or i n team discussions.
(Externalization): Articulation among people through dialog (e.g., brainstorming).
y
Tacit to exp explicit licit communication communication
y
Explicit Explicit to exp e xplicit licit communication (Communication): This transformation phase can be best supported by technology. Expli cit knowledge can be easily captured and then distributed/transmitted to worldwide audience.
y
Explicit to tacit communication (Internalization): This implies taking explicit knowledge (e.g., a report) and deducing new ideas or taking constructive action. One significant goal of knowledge management is to create technology to help the users to derive tacit knowledge from explicit knowledge.
Knowledge Arch rchitecture
y
Knowledge
y
The
y
These
architecture can be regarded as a pr erequisite to knowledge sharing.
infrastructure can be vi ewed as a combination of people, content, and technology. components are inseparable and interdependent (refer to Figure 3.3).
Figure 3.3: Knowledge Management, a conceptual view
People Core The Peop
y
By
people, people, here we mean knowledge workers, managers, customers, and suppliers.
y
As
y
All
y
The
y
In
the first step i n knowledge architecture, our goal is to evaluate the existi ng information/ documents which are used by people, the applications needed by them, the people they usuall y contact for solutions, the associates they collaborate wi th, the official emails they send/receive, and the database(s) they usually access. the above stated resources help to create an employee profile, profile, which can later be used as the basis for designing a knowledge management system. idea behind assessing the people core is to do a proper job in case of assigning job content to the right person and to make sure that the flow of information that once w as obstructed by departments now flows to ri ght people at right time. order to expedite knowledge sharing, a knowledge network has to be designed in such a w ay as to assign people authori ty and responsibility for specific kinds of knowledge content, which means: Identifying knowledge centers:
o
After
Here,
These
determining the knowledge that people need, the next step i s to find out where the requir ed knowledge resides, and the way to capture it s uccessfully. the term knowledge center means center means areas in the organization where knowledge is available for capturing.
centers supports to identify expert(s) or expert teams in each center w ho can collaborate in the necessary knowledge capture process.
o
Activating knowledge content satellites
o
Assigning
This
step breaks down each knowledge center into some more manageable levels, satellites, or areas .
experts for each knowledge center:
After
Ownership is a crucial factor in case of knowledge capture, knowledge transfer, and k nowledge implementation.
In
Often, fight can occur over the budget or over the control of sensitive processes (this includes the kind of knowledge a department owns).
These reasons justify the process of assigning department ownership to knowledge content and knowledge process.
adjacent/interdependent departments should be cooperative and ready to share knowledge.
the final framework has been decided, one manager should be assigned for each knowledge satellite who will ensure integrity of information content, access, and update.
a typical organization, departments usually tend to be territorial.
echnical Core The Tech
y
The
y
Technology
objective of the technical core is to enhance communication as well as ensure effective knowledge sharing. provides a lot of opportunities for managing tacit knowledge in the area of communication.
y
Communication networks create links between necessary databases.
y
Here
the term technical core is meant to refer to the t otality of the required hardw are, software, and the specialized human resources.
y
Expected attributes of technology under the technical core: Accuracy, speed, reliability, security, and integrity.
y
Since
y
A
an organization can be thought of as a k nowledge network, the goal of knowledge economy is to push employees towards greater efficiency/ productivity by making best possible us e of the knowledge they posses. knowledge core usually becomes a network of technologies designed to work on top of the organization's existi ng network.
User Interf nterface Layer
y
Usually
a web br owser represents the interface between the user and the KM system.
y
It
y
The
way the text, graphics, tables etc are displayed on the screen tends to si mplify the technology for the user.
y
The
user interface layer should provide a way for the proper flow of tacit and explicit knowledge.
y
The
y
Features
is the top layer in the KM system architecture.
necessary knowledge transfer between people and technology involves capturing tacit knowledge from experts, storing it in knowledge base, and making it available to people for solving complex problems.
o
to be considered in case of user interface design: Consistency
o
Relevancy
o
Visual
o
Usability
o
Ease of Navigation
clarity
Figure 3.4: The Transfer
of Knowledge
Auth uthorized Access Layer
y
This layer maintains s ecurity as well as ensures authorized access to the knowl edge captured and stored in the organization's repositories.
y
The
knowledge is usually c
y y
aptured by using internet, intranet of extrane t.
y
An
y
Extranet is a type of intranet w ith extensions allowing specified people (customers, s uppliers, etc.) to access some organizational information.
y
Issues
organization's intranet represents the internal network of communication systems.
related to the access layer: access privileges, backups.
y
The
y
Firewalls
access layer is mostly focused on security, use of protocols (like passwords), and software tools like firewalls. can protect against:
o
E-mails that can cause problems.
o
Unauthorized
o
Undesirable
Unauthorized
o y
Firewalls
access from the outside world.
material (movies, images, music etc). sensitive information leaving the organization.
cannot protect against:
o
Attacks
o
Viruses
o
Weak
not going through the firewall.
on floppy disks.
security policies.
Collaborative Intelligence and Filtering Layer
y
This
y
Authorized
y
Intelligent
y
In
case of client/server computing, there happens to be frequent and direct interaction between the client and the serv er.
y
In
case of mobile agent computing, the interaction happens betw een the agent and the serv er.
y
A mobile agent roams around the internet across mul tiple servers looking for the correct inform ation.Some benefits can be found in the areas of:
y
y
layer provides customized views based on stored knowledge. users can find information (through a search m echanism) tailored to their needs.
agents (active objects which can perceive, reason, and act i n a situation to help pr oblem solving) are found to be extremely useful in some situations.
Key
In
o
Fault
o
Reduced
tolerance.
o
Heterogeneous operation.
overall network load.
components of this layer: o
The
o
Membership in specific services, such as sales promotion, news service etc.
registration directory that develops tailored information based on user profile.
o
The
search facility such as a search engine.
terms of the prerequisi tes for this layer, the following criteria can be considered: o
Security.
o
Portability.
o
Flexibility
o
Scalability
o
Ease of use.
o
Integration.
Knowledge- Enabling Application pp lication Layer (Value-Added Layer) ayer)
y
This
y
Most of the applications help users to do their jobs in better ways.
creates a competitive edge.
y
They
include knowledge bases, discussion databases, decision support etc.
Transp ransport Layer
y
This
y
It
y
This
y
In
is the most technical layer.
ensures to make the organizati on a network of relationships w here electronic transfer of knowledge can be considered as r outine. layer associates with LAN (Local Area Network), WAN (Wide Area Network), intranets, extranets, and internet.
this layer we consider multimedia, URL's, connectivity speeds/bandwidths, search tools, and consider managing of network traffic.
Middleware Layer
y
This
y
It
layer makes it possible to connect between old and new data formats.
contains a range of programs to do this job.
Repositories Layer
y
It
y
These may include, legacy applications, intelligent data warehouses, operati onal databases etc.
is the bottom layer of the KM architecture which r epresents the physical layer in which repositories are install ed.
y
After
establishing the repositories, they are li nked to form an integrated repository.
Figure 3.5: Integrated Knowledge
t he KM KM System Acquiring th
Building In-h n-house from Scratch Scratch
y
Requires
y
Development
ready professionals.
y
Risk is high.
y
Main benefit is the customization.
cost is high.
Buying
y
Quick
installation.
Management System
y
Low
cost.
y
Customization may not be quite right.
Outsourcing
It
is a tr end that allows organizations to concentrate on their s trengths while technological design and other specialized areas ar e releases to outsiders.
y
Present trend exists towards ready-to-use, generalized software packa ges. Advantages of a reliable KM software package:
y
Implementation
time is shorter.
y
Development
y
There
exists reduced need of resources.
y
Offer
greater flexibility.
y
Shorter
cost is comparatively low.
track record.
y
Lack
y
Application incompatibility.
of competition.
Crucial components in case of knowl edge management systems development: development:
y
Reliability
y
Market pressure.
y
Usability.
y
Modularity.
y
Performance.
y
Serviceability.
y
Portability.
Buying
or outsourcing a KM system raises the important question of ownership. ownership. The issues to consider in case of ownership:
y
What
is the user paying for?
y
What
restrictions exist in case of copying the software for organizational subdivisions?
y
Who
can modify the software and w hat are the associated costs?
y
How
the modifications can be made if at some stage the vendor happens to be out of business?
Capturing th the Tacit Knowledge
y
Knowledge Capture can be defined as the process using which the expert's thoughts and experiences can be captured.
y
In this case, the knowledge developer collaborates with the expert in order to convert the experti se into the necessary program code(s).
y
Important
steps:
o
Using
o
Interpreting
appropriate tools for eliciting information.
o
Finally,
the elicited information and consequently inferring the experts underlyi ng knowledge/reasoning process.
using the interpretation to construct the the necessary rules which can represent the experts reasoning process.
Expert Expert Evaluation
y
y
Indicators
of expertise:
o
The
expert commands genuine respect.
o
The
expert is found to be consul ted by people in the organization, w hen some problem arises.
o
The
expert possess self confidence and he/she has a realistic view of the limitations.
o
The
expert avoids irrelevant information, uses facts and figures.
o
The
expert is able to expl ain properly and he/she can customize his/ her presentation according to the level of the audience.
o
The
expert exhibits his/her depth of the detailed knowledge and his/her quality of explanation is exceptional.
o
The
expert is not arrogant regarding his/her personal information.
Experts qualifications: o
The
expert should know when to follow hunches, and when to make exceptions.
o
The
expert should be able to see the bi g picture.
y
y
o
The
o
The
expert should posses good communication skills. expert should be able to tolerate stress.
o
The
expert should be able to think creatively.
o
The
expert should be able to exhibi t self-confidence in his/her thought and actions.
o
The
expert should maintain credibility.
o
The
expert should operate within a schema-driven/structured orientation.
o
The
expert should use chunked knowledge.
o
The
expert should be able to generate enthusias m as well as motivation.
o
The
expert should share his /her expertise willingly and without hesitation.
o
The
expert should emulate an ideal teacher's habi ts.
Experts levels of expertise: o
Highly
o
Moderately expert problem solvers.
expert persons.
o
New
experts.
Capturing single vs multiple experts' tacit knowledge: o
o
o
o
Advantages
of working with a singl e expert:
Ideal
for building a simple KM system with only few rules.
Ideal
when the problem lies within a restricted domain.
The
single expert can facilitate the logis tics aspects of coordination arrangements for knowledge capture.
Problem related/personal conflicts are easier to resolve.
The
Disadvantages
single expert tends to share more confidentiality.
of working with a single expert:
Often,
The
They
The
Advantages
the experts knowledge is found to be not easy to capture.
single expert usually provides a si ngle line of reasoning. are more likely to change meeting schedules.
knowledge is often found to be dispers ed.
of working with multiple (team) exper ts:
Complex problem domains are usually benefited.
Stimulates
Listening
Formal
Disadvantages
interaction.
to a multitude of views allows the developer to consider alternative ways of representing knowledge.
meetings are sometimes better environment for generating thoughtful contributions.
of working with multiple (team) experts:
Disagreements
Coordinating meeting schedules are more complicated.
Harder
Overlapping
Often
can frequently occur.
to retain confidentiality. mental processes of multiple experts can result in a process loss. loss.
requires more than one knowl edge developer.
Develop eveloping Relationsh elationship with with Experts Experts
y
Creating the right impression: The knowledge developer must learn to use psychology, common sense, technical as well as marketing skills to attract the experts r espect and attention.
y
Understanding
of the expert's style of expression:
Experts are usually found to use one of the following styles of expression:
y
o
Procedure type: These type of experts are found to be logical, verbal and always procedural.
o
Storyteller
o
Godfather type: These
o
Salesperson
type: These type of experts are found to be f ocused on the content of the domain at the expense of the solution. type of experts are found to be compulsive to take over.
type: These type of experts are found to spend most of the tim e dancing around the topic, explaining why his/her solution is the best.
Preparation for the session: o
Before making the first appoi ntment, the knowledge developer must acquire some knowledge about the problem and the expert.
o
Initial
o
The
knowledge developer must build the trust.
o
The
knowledge developer must be familiar with project terminology d he/she must r eview the existing documents.
o
The
knowledge developer should be able to make a quick rapport with the expert.
Deciding
sessions can be most challenging/critical.
the location for the session:
o
Protocol calls for the expert to decide the location.
o
The
expert is usually more comfortable in having his/her necessary tools and information available close to him/her.
The
o y
Approaching
meeting place should be quiet and free of interruptions.
multiple experts:
o
Individual approach: The
o
Approach
Small
o
knowledge developer holds sessions with one expert at a time.
using primary and secondary experts:
The
For
knowledge developer hold sessions with the senior expert ear ly in the knowledge capture program for the clarification of the plan. a detailed probing, he/she may ask for other experts' knowledge.
groups approach:
Experts gather together in one place, di scuss the problem domain, and usually pr ovide a pool of information.
Experts' responses are monitored, and the functionali ty of each expert is tested against the exper tise of the others.
This
approach requires experience in assessing tapped knowledge, as well as cognition skills.
The
knowledge developer must deal with the issue of p ower and its effect on expert's opinion.
Fuzzy Reasoning & Quality of of Knowledge Capture
y
Sometimes,
y
The
y
Analogies/Uncertainties:
y
the information gathered from the experts vi a interviewing is not precise and it i nvolves fuzziness and uncertainty.
fuzziness may increase the difficulty of transl ating the expert's notions into applicable r ules.
o
In
the course of explaining events, experts can use analogies (comparing a problem with a similar problem which has been encountered in possibly different settings, months or years ago).
o
An
o
Belief ,
o
People may use different kinds of words in order to express belief.
o
These
expert's knowledge or expertise r epresents the ability to gather uncertain information as input and to use a plausi ble line of reasoning to clarify the fuzzy details. an aspect of uncertainty, tends to describe the l evel of credibility. words are often paired with qualifiers such as highly, extremely. extremely.
Understanding experience: o
Knowledge developers can benefit fr om their understanding/knowledge of cognitive psychology.
o
When
o
The
resulting answer is often found to be t he culmination of the processing of s tored information.
o
The
right question usually evokes the memory of experiences that produced good and appropriate solutions i n the past.
a question is asked, then an expert operates on certain stored information through deductive, inductive, or other kinds of problem-solving methods.
Sometimes,
how quickly an expert responds to a question depends on the clari ty of content, whether the content has been recently used , and how w ell the expert has understood the question.
y
Problem with the language: How well the expert can repr esent internal processes can vary wi th their command of the language they are using and the knowl edge developer's interviewing skills.
The
language may be unclear i n the following number of ways:
o
Comparative words (e.g., better , faster ) are sometimes left hanging.
o
Specific
o
Absolute
o
Some
words or components may be left out of an explanation. words and phrases may be us ed loosely.
words always seem to have a built-in ambiguity.
Interviewing as a Tacit Knowledge Capture Tool
y
y
Advantages
of using interviewing as a tacit knowledge capture tool:
o
It
is a flexible tool.
o
It
is excellent for evaluating the validity of information.
o
It
is very effective in case of eli citing information regarding complex matters.
o
Often
Interviews
people enjoy being interviewed.
can range from the highly unstructured type to highl y structured type.
o
The unstructured types are difficult to conduct, and they are used i n the case when the knowl edge developer really needs to explore an issue.
o
The structured types are found to be goal-ori ented, and they are used in the case when the knowledge developer needs specific information.
o
Structured
o
In
questions can be of the foll owing types:
Multiple-choice questions.
Dichotomous
Ranking
questions.
scale questions.
semi structured types, the knowledge developer asks predefined questions, but he/she allows the expert some freedom i n expressing his/her answer.
y
Guidelines
Setting
o
Phrasing questions.
o
Listening closely/avoiding arguments.
Reliability
Some
y
y
y
y
y
the stage and establishing rapport.
Evaluating the session outcomes.
o y
for successful interviewing:
o
of the information gathered from experts:
uncontrolled sources of err or that can reduce the information's reliability:
o
Expert's perceptual slant.
o
The
o
Fear
o
Problems with communication.
o
Role
failure in expert's par t to exactly remember what has happened. of unknown in the part of expert. bias.
Errors in par t of the knowledge developer: validity problems are often caused by the interviewer effect (something about the knowledge developer colours the response of the expert). Some of the effects can be as follows: o
Gender effect
o
Age effect
o
Race effect
Problems encountered during interviewing o
Response
o
Inconsistency.
bias.
o
Problem with communication.
o
Hostile
o
Standardizing
o
Setting
attitude. the questions.
the length of the interview.
Process of ending the interview: o
The
o
One
end of the session should be carefully planned.
o
This
o
Many verbal/nonverbal cues can be us ed for ending the interview. (refer to Table 5.2, in page 148 of your textbook).
procedure calls for the knowl edge developer to halt the questioning a few minutes before the scheduled ending time, and to summarize the key points of the session. allows the expert to comment a schedule a f uture session.
Issues:
Many issues may arise during the interview, and to be prepared for the most important ones, the knowl edge developer can consider the following questions:
Rapid
o
How would it be possi ble to elicit knowledge from the experts who cannot say w hat they mean or cannot mean what they say.
o
How
to set up the problem domain.
o
How
to deal with uncertain reasoning processes.
o
How
to deal with the situation of difficult relationships with expert(s).
o
How
to deal with the si tuation when the expert does not lik e the knowledge developer for some reason.
Prototyping in interviews: o
Rapid prototyping is an approach to building KM systems, in which k nowledge is added with each knowledge capture session.
o
This
is an iterative appr oach which allows the expert to verify the rul es as they are built during the session.
o
This
approach can open up communication through i ts demonstration of the KM system.
o
Due
o
It
o
This
approach is highly interactive.
o
The
prototype can create user expectations w hich in turn can become obstacles to f urther development effort.
to the process of ins tant feedback and modification, it reduces the risk of failure.
allows the knowledge developer to learn each tim e a change is incorporated in the prototype.
Some Knowledge Capturing Tech echniques
On-Site Observation ( Action Protocol) Protocol )
y
It
is a process w hich involves observing, recording, and interpreting the expert's problem-solving process while it takes place.
y
The
y
Compared to the process of intervi ewing, on-site observation brings the knowledge developer closer to the actual steps, techniques, and procedures used by the expert.
y
One
knowledge developer does more listening than talking; avoi ds giving advice and usually does not pass his/her own judgment on what is being observed, even i f it seems incorrect; and most of all, does not argue with the expert while the expert is performing the task.
disadvantage is that sometimes some experts to not like the idea of being observed.
y
The
y
Another
reaction of other people (in the observation setting) can also be a problem causing distraction. disadvantage is the accuracy/completeness of the captured knowledge.
On-Site Observation ( Action Protocol) Protocol )
y
It
y
The
is a process w hich involves observing, recording, and interpreting the expert's problem-solving process while it takes place.
y
Compared to the process of intervi ewing, on-site observation brings the knowledge developer closer to the actual steps, techniques, and procedures used by the expert.
y
One
y
The
y
Another
knowledge developer does more listening than talking; avoi ds giving advice and usually does not pass his/her own judgment on what is being observed, even i f it seems incorrect; and most of all, does not argue with the expert while the expert is performing the task.
disadvantage is that sometimes some experts to not like the idea of being observed.
reaction of other people (in the observation setting) can also be a problem causing distraction. disadvantage is the accuracy/completeness of the captured knowledge.
Brainstorming
y
It is an unstructured approach towar ds generating ideas about creative solution of a problem which involves multiple experts in a session.
y
In
y
Similarities
this case, questions can be raised for clarification, but no eval uations are done at the spot.
The
y
If
(that emerge through opinions) are usually grouped together logically and evaluated by asking some questions like:
o
What
benefits are to be gained if a particular idea is followed.
o
What
specific problems that idea can possibly solve.
o
What
new problems can arise through this.
general procedure for conducting a brainstorming session:
o
Introducing
o
Presenting the problem to the experts.
the session.
o
Prompting the experts to generate ideas.
o
Looking
for signs of possi ble convergence.
the experts are unable to agr ee on a specific s olution, they knowledge developer may call for a vote/consensus.
Electronic Brainstorming
y
Is
is a computer-aided approach for dealing with multiple experts.
y
It usually begins with a pre-session plan which identifies objectives and structures the agenda, which is then presented to the experts for approval.
y
During
y
This
y
Usually
y
This
y
The
y
This
eventually leads to convergence of ideas and helps to set final specifications.
y
The
result is usually the joint ownership of the solution.
the session, each expert si ts on a PC and get themselves engaged in a predefined approach towards resolving an issue, and then generates ideas. allows experts to present their opinions through their PC's without having to wait for their turn. the comments/suggestions are displayed electronically on a l arge screen without identifying the source.
approach protects the introv ert experts and prevents tagging comments to individuals.
benefit includes improved communication, effective discussion regarding sensitive issues, and closes the meeting with concise recommendations for necessary action (refer to Figure 5.1 for the sequence of steps).
Figure 5.1: The
process of brainstorming
Protocol Analysis ( Think-Aloud Meth ethod) od)
y
In
this case, protocols (s cenarios) are collected by asking experts to solve the specific problem and verbalize their decision process by stating directly what they think.
y
Knowledge
y
The
developers do not interrupt in the interim.
elicited information is structured later when the knowl edge developer analyzes the protocol.
y
Here
y
A
scenario can involve i ndividuals and objects.
the term scenario term scenario refers to a detailed and somehow complex s equence of events or more precisely, an episode.
A
scenario provides a concrete vision of how some specific human activity can be supported by information technology.
Consensus Decision Making
y
Consensus decision making usually follows br ainstorming.
y
It
y
In
order to arrive at a consensus, the knowledge developer conducting the exercise tries to r ally the experts towards one or tw o alternatives.
y
The
y
This method is democratic in nature.
y
This
is effective if and only if each expert has been provided with equal and adequate opportunity to pr esent their views.
knowledge developer follows a procedure designed to ensure fairness and standardization.
method can be sometimes tedious and can take hours.
Repertory Grid
y
This
is a tool used for knowledge capture.
y
The
domain expert classifies and categorizes a problem domain using his/her own model.
y
The
grid is used for capturing and evaluating the expert's model.
y
Two
experts (in the same problem domain) may produce distinct sets of personal and subjective results.
y
The
grid is a scale (or a bipolar construct) on which elements can be placed within gradations.
y
The
knowledge developer usually elicits the constructs and then asks the domain expert to provide a set of examples calledelements calledelements..
y
Each element is rated according to the constructs w hich have been provided.
(Refer to page 167 of your textbook for examples).
Nominal Group Grou p Tech echnique ( NGT)
y
This
provides an interface between consensus and brainstorming.
y
Here
y
Idea
writing is a structured group approach used for developing ideas as well as exploring their meaning and the net result is usually a written report.
the panel of experts becomes a Nominal Group whose meetings are structured in order to effectively pool individual judgment.
y
NGT is
an idea writing technique.
elphii Meth ethod Delph
y
It
is a survey of experts where a series of questionnaires are used to pool the experts' responses for solving a specific problem.
y
Each experts' contributions are s hared with the rest of the experts by usi ng the results from each questionnair e to construct the next questionnaire.
once pt Mapping pping Concep
y
It
is a network of concepts consisting of nodes and links.
y
A
node represents a concept, and a li nk represents the relationship between concepts (refer toFigure 6.5 in page 172 of your textbook).
y
Concept mapping is designed to transform new concepts/propositions into the existing cognitive structures r elated to knowledge capture.
y
It
is a structured conceptualization.
y
It
is an effective way for a group to function without losing their individuality.
y
Concept mapping can be done for several reasons:
y
o
To
design complex structures.
o
To
generate ideas.
o
To
communicate ideas.
o
To diagnose misunderstanding.
Six-step
procedure for using a concept map as a tool:
o
y
Preparation.
o
Idea generation.
o
Statement
o
Representation.
structuring.
o
Interpretation
o
Utilization.
Similar
to concept mapping, a semantic a semantic net is a collection of nodes li nked together to form a net. knowledge developer can graphically represent descriptive/declarative knowledge through a net.
o
A
o
Each idea of interest is usually represented by a node linked by lines (called arcs) arcs) which shows r elationships between nodes.
o
Fundamentally
it is a network of concepts and relationships (refer to page 173 of your textbook for example).
Black boarding
y
In
this case, the experts work together to solve a specific problem using the blackboard as their workspace.
y
Each expert gets equal opportunity to contribute to the solution via the blackboard.
y
It
y
The
y
Characteristics of blackboard system:
y
is assumed that all par ticipants are experts, but they might hav e acquired their individual expertise in situations different from those of the other experts in the group. process of black boarding continues till the solution has been reached.
o
Diverse
o
Common language for interaction.
approaches to problem-solving.
o
Efficient storage of information
o
Flexible
o
Iterative
o
Organized
representation of information. approach to problem-solving. participation.
Components of blackboard system:
y
The
y
This
o
The Knowledge Source (KS):
Each KS is an independent expert observing the status of the blackboard and trying to contribute a higher level par tial solution based on the knowledge it has and how well such knowledge applies to the current blackboard state.
o
The Blackboard
o
A
: It is a global memory structure, a database, or a reposi tory that can store all par tial solutions and other necessary data that are presently in various stages of completion. Control Mechanism: It coordinates the pattern and flow of the problem solution.
inference engine and the knowledge base a re part of the blackboard system.
approach is useful in case of situations involving multiple expertise, diverse knowledge representations, or s ituations involving uncertain knowledge representation.
Knowledge Codif odification
y
Knowledge
y
Tacit knowledge (e.g., human expertise) is identified and leveraged through a form that is able to produce highest return for the business.
y
Explicit knowledge is organized, categorized, indexed and accessed.
y
The
y
Codification must be done in a form/ structure which will eventually build the knowledge base.
y
The
organizing often includes decision trees, decision tables etc.
resulting knowledge base supports tr aining and decision making. o
y
The
codification means converting tacit knowledge to explicit knowledge in a usable form for the organizational members.
Diagnosis.
o
Training/Instruction.
o
Interpretation.
o
Prediction.
o
Planning/Scheduling.
knowledge developer should note the following points before i nitiating knowledge codification: o
Recorded
o
Diffusion
knowledge is often difficult to access (because it is either fragmented or poorly organized).
of new knowledge is too slow.
o
Knowledge
o
Often
knowledge is not found in the proper for m.
is nor shared, but hoarded (this can involve political implications).
o
Often
knowledge is not available at the corr ect time when it is needed.
o
Often
knowledge is not present in the proper location where it should be present.
o
Often
the knowledge is found to be incomplete.
Modes of of Knowledge Conversion
y
Conversion from tacit to tacit k nowledge produces socialization where knowledge developer looks for experience in case of knowledge capture.
y
Conversion from tacit to explicit k nowledge involves externalizing, explaining or clarifying tacit knowledge via analogies, models, or metaphors.
y
Conversion from explicit to tacit knowledge involves internalizing (or fitting explicit knowledge to tacit knowledge.
y
Conversion from explicit to explicit k nowledge involves combining, categorizing, reorganizing or sorting different bodies of explicit knowledge to lead to new knowledge.
Codif odifying Knowledge
y
y
An
organization must focus on the foll owing before codification: o
What
organizational goals will the codified knowledge serve?
o
What
knowledge exists in the organization that can address these goals?
o
How
useful is the exi sting knowledge for codification?
o
How
would someone codify knowledge?
Codifying tacit knowledge (in its entirety) in a knowledge base or repository is often difficult because it is usually developed and internalized in the minds of the human experts ov er a long period of time.
Codif odification Tools/Procedures
Know Knowle ledg e Maps dge
y
Knowledge
y
It
maps originated from the belief that people act on thi ngs that they understand and accept.
y
Knowledge
y
They
y
It
is not a k nowledge repository.
y
It
is a sort of directory that points towards people, documents, and repositories.
y
It may identify strengths to exploit and missing knowledge gaps to fill.
y
Knowledge
y
Examples of complex systems are ecosystems, the internet, tel ecommunications systems, and customer-supplier chains in the stock market.
y
Knowledge
y
Key
y
These
indicates that self-determined change is sustainable. map is a visual repr esentation of knowledge.
can represent explicit/tacit, formal/informal, documented/undocumented, internal/external knowledge.
Mapping is very useful when it is r equired to visualize and explore complex systems.
Mapping is a multi-step process.
can be extracted from database or l iterature and placed in tabular form as lists of facts. tabled relationships can then be connected in network s to form the required knowledge maps.
Figure 6.1:
Example: Knowledge Map
A
popular knowledge map used in human resources is a skills planner in which employees are matched to jobs. Steps to build the map:
structure of the knowledge requirements should be developed.
y
A
y
Knowledge
y
Y ou should rate employee perfor mance by knowledge competency.
y
Y ou
required of specific jobs must be defined.
should link the knowledge map to some trai ning program for career development and job advancement.
Decisi on Table
y
It
is another technique used for knowl edge codification.
y
It
consists of some conditions, rules, and actions.
phonecard company sends out monthly invoices to permanent customers and gives them discount if payments are made within two weeks. Their discounting policy is as follows:
A
``If the amount of the order of phonecards is greater than $35, subtract 5% of the order; if the amount is greater than or equal to $20 and less than or equal to $35, subtract a 4% discount; if the amount is less t han $20, do not apply any discount.''
We
shall develop a decision table for their discounting decisions, where the condition alternatives are `Yes' and `No'.
Figure 6.2:
Example: Decision Table
Decisi on Tree
A
y
It
is also a k nowledge codification technique. technique.
y
A
decision tree is usually a hierarchically arranged semantic network.
decision tree for the phone card company discounting policy (as discussed above) is shown next.
Figure 6.3:
Example: Decision Tree
F rames
y
A
frame is a codification scheme used for organizing k nowledge through previous experience.
y
It
deals with a combination of declarative and operational knowledge.
y
Key
P r oducti oducti on
o
Slot: A
o
Facet: The
specific object being described/an attribute of an entity. value of an object/slot.
Ru Rules
They
are conditional statements specifying an action to be taken in case a certain condition is true.
y
They
codify knowledge in the form of premise-action pairs .
y
Syntax: IF
y
Example: IF income is `standard' and payment history is `good', THEN `approve home loan'.
y
In
y
Rules
y
A
y
The
premise is a Boolean expression that should evaluate to be true for the rule to be appli ed.
y
The
action part of the rule is separated from the premise by the keyword THEN.
y
The
action clause consists of a statement or a series of statements separated byAND's or comma's and is executed if the premise is true.
y
In
elements of frames:
(premise) THEN (action)
case of knowledge-based systems, rules ar e based on heuristics or experimental reasoning. can incorporate certain levels of uncertainty.
certainty factor is synonymous with a confidence level , w hich is a subjective quantification of an expert's judgment.
case of knowledge-based systems, planning involves:
Breaking
y
Role
the entire system into manageabl e modules.
y
Considering partial solutions and liking them through r ules and procedures to arrive at a fi nal solution.
y
Deciding
on the programming language(s).
y
Deciding
on the software package(s).
y
Testing
y
Developing
y
Promoting clarity, flexibility; making rules clear.
y
Reducing
and validating the system. the user interface.
unnecessary risk.
of inferencing:
y
Inferencing implies
y
An
y
the process of deriving a conclusion based on statements that only imply that conclusion.
inference engine is a program that manages the inferencing strategies.
easoning is the process of appl ying knowledge to arrive at the conclusion.
o
Reasoning
o
People usually draw informative conclusions.
depends on premise as w ell as on general knowledge.
Case-Based Case-Based Reaso Reasoning ning
y
It
is reasoning from relevant past cases in a way similar to human's use of past experiences to arrive at conclusions.
y
Case-based reasoning is a technique that records and documents cases and then searches the appropriate cases to determine their usefulness in solving new cases pr esented to the expert.
y
The
y
Adding
y
A
aim is to bring up the most similar historical case that matches the present case. new cases and reclassifying the case library usually expands knowledge.
case library may require considerable database storage as well as an effi cient retrieval system.
Know Knowle ledg e-Based dge-Base d Agents
y
An
y
They
intelligent agent is a program code which is capable of performing autonomous action in a timely fashion.
y
they can be programmed to interact wi th other agents or humans by using some agent communication language.
can exhibit goal directed behaviour by taki ng initiative.
y
In terms of knowledge-based systems, an agent can be progr ammed to learn from the user behaviour and deduce future behaviour for assisting the user.
Knowledge Develop eveloper's Skill Set
Know Knowle ledg e Requ Requirements dge
y
Computing technology and operating systems.
y
Knowledge
y
Domain specific knowledge.
repositories and data mining.
y
Cognitive psychology.
Skills Requ Req uirements
y
Interpersonal Communication.
y
Ability
to articulate the project's rationale.
y
Rapid
Prototyping skills.
y
Attributes
y
Job
roles.
related to personality.
