KNOWLEDGE ELICITATION: DATA MINING AND KNOWLEDGE CODIFICATION METHODS

1) what are some of the purposes for doing a data mining or web mining study?

There are two basic purposes:

1) to describe and

2) to predict. Descriptive data mining is done to try to understand patterns in people of things.

For example, purchasing habits of people during super bowl week, the financial dealings of people suspected of money laundering, the incidence of lighting in a region with specific geological features (e.g., a mountain lake). This can serve to explain the behavior of the individuals or phenomena involved. Predictive data mining, on the other hand, is done to attempt to predict the behavior of a person or thing by looking at its history. By building a model of this entity (person, system or thing), one can predict future performance and thereby better react to future occurrences of the pattern sought to identify. One example is to build a model of the stock market in order to predict its future performance.

Knowledge Elicitation Methods

On-Site Observation (Action Protocol)

  • It is a process which involves observing, recording, and interpreting the expert’s problem-solving process while it takes place.
  • The knowledge developer does more listening than talking; avoids giving advice and usually does not pass his/her own judgment on what is being observed, even if it seems incorrect; and most of all, does not argue with the expert while the expert is performing the task.
  • Compared to the process of interviewing, on-site observation brings the knowledge developer closer to the actual steps, techniques, and procedures used by the expert.
  • One disadvantage is that sometimes some experts to not like the idea of being observed.
  • The reaction of other people (in the observation setting) can also be a problem causing distraction.
  • Another disadvantage is the accuracy/completeness of the captured knowledge.

Brainstorming

  • It is an unstructured approach towards generating ideas about creative solution of a problem which involves multiple experts in a session.
  • In this case, questions can be raised for clarification, but no evaluations are done at the spot.
    • Similarities (that emerge through opinions) are usually grouped together logically and evaluated by asking some questions like:
  1. What benefits are to be gained if a particular idea is followed.
  2. What specific problems that idea can possibly solve.
  3. What new problems can arise through this. The general procedure for conducting a brainstorming session:
  4. Introducing the session.
  5. Presenting the problem to the experts.
  6. Prompting the experts to generate ideas.
  7. Looking for signs of possible convergence.
  • If the experts are unable to agree on a specific solution, they knowledge developer may call for a vote/consensus.

Electronic Brainstorming

  • It is a computer-aided approach for dealing with multiple experts.
  • It usually begins with a pre-session plan which identifies objectives and structures the agenda, which is then presented to the experts for approval.
  • During the session, each expert sits on a PC and get themselves engaged in a predefined approach towards resolving an issue, and then generates ideas.

  • This allows experts to present their opinions through their PC’s without having to wait for their turn.
  • Usually the comments/suggestions are displayed electronically on a large screen without identifying the source.
  • This approach protects the introvert experts and prevents tagging comments to individuals.
  • The 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).
  • This eventually leads to convergence of ideas and helps to set final specifications.
  • The result is usually the joint ownership of the solution.

Nominal Group Technique (NGT)

  • This provides an interface between consensus and brainstorming.
  • Here the panel of experts becomes a Nominal Group whose meetings are structured in order to effectively pool individual judgment.
  • 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.
  • NGT is an idea writing technique.

Delphi Method

  • It is a survey of experts where a series of questionnaires are used to pool the experts’ responses for solving a specific problem.
  • Each experts’ contributions are shared with the rest of the experts by using the results from each questionnaire to construct the next questionnaire.

Concept Mapping

  • It is a network of concepts consisting of nodes and links.
  • A node represents a concept, and a link represents the relationship between concepts (refer to Figure 6.5 in page 172 of your textbook).
  • Concept mapping is designed to transform new concepts/propositions into the existing cognitive structures related to knowledge capture.
  • It is a structured conceptualization.
  • It is an effective way for a group to function without losing their individuality.
    • Concept mapping can be done for several reasons:
  1. To design complex structures.
  2. To generate ideas.
  3. To communicate ideas.
  4. To diagnose misunderstanding.
  • Six-step procedure for using a concept map as a tool:
  1. Preparation.
  2. Idea generation.
  3. Statement structuring.
  4. Representation.
  5. Interpretation
  6. Utilization.
  • Similar to concept mapping, a semantic net is a collection of nodes linked together to form a net.
  1. A knowledge developer can graphically represent descriptive/declarative knowledge through a net.
  2. Each idea of interest is usually represented by a node linked by lines (called arcs) which shows relationships between nodes.
  3. Fundamentally it is a network of concepts and relationships (refer to page 173 of your textbook for example).

Black boarding

  • In this case, the experts work together to solve a specific problem using the blackboard as their workspace.
  • Each expert gets equal opportunity to contribute to the solution via the blackboard.
  • It is assumed that all participants are experts, but they might have acquired their individual expertise in situations different from those of the other experts in the group.
  • The process of black boarding continues till the solution has been reached.
    • Characteristics of blackboard system:
  1. Diverse approaches to problem-solving.
  2. Common language for interaction.
  3. Efficient storage of information
  4. Flexible representation of information.
  5. Iterative approach to problem-solving.
  6. Organized participation.
  • Components of blackboard system:
  1. The Knowledge Source (KS): Each KS is an independent expert observing the status of the blackboard and trying to contribute a higher level partial solution based on the knowledge it has and how well such knowledge applies to the current blackboard state.
  2. The Blackboard : It is a global memory structure, a database, or a repository that can store all partial solutions and other necessary data that are presently in various stages of completion.
  3. A Control Mechanism: It coordinates the pattern and flow of the problem solution.
  • The inference engine and the knowledge base are part of the blackboard system.
  • This approach is useful in case of situations involving multiple expertise, diverse knowledge representations, or situations involving uncertain knowledge representation.

Knowledge Capture Systems: Systems that Preserve and Formalize Knowledge

1. What are the methods for eliciting stories?

Stories may be elicited through anthropological observation, which is using a naïve but interested interviewer. The interviewer’s naïveté will facilitate the natural volunteering of stories by the knowledgeable potential storyteller. The interest or curiosity of the interviewer will increase storytellers’ sense of importance and will result in higher levels of story volunteering. Using a group that has a common context such as a community of practice to form storytelling circles is another step towards anecdote elicitation. Other methods useful in storytelling circles are: fish tales since individuals enjoy enhancing previously shared stories, alternative histories, shifting characters or context to gain different perspectives on a story, and indirect stories to foster a feeling of security and privacy. Finally, the use of metaphors to start a story telling process provides a common context or reference for the group.

2. Describe how concept maps represent knowledge.

Concept maps aim to represent knowledge through concepts or main subjects/ideas that are represented as text inside of some type of geometric shape, usually a rectangle or circle. The concepts are patterns or regularities in objects or events. Different concepts are related to each other and this is represented by connecting two of the geometric shapes containing the related concepts via a line, which represents a proposition. The propositions are labeled, usually with a verb phrase or preposition that indicates the nature of the relationship between the two concepts. The more general concepts appear at the top of the map, with specialization progressing towards the bottom of the map. Inter-domain relations between concepts can be represented by a line called a cross-link.

3. What are the organizational situations that context-based reasoning is designed to model and what are the basic tenets of context-based reasoning?

CxBR models tactical situations and the operations needed to be performed during special tactical situations. CxBR is based on the following three tenets:

1.  Tactical situations call for a set of actions and procedures that address the current situation.

2.  Situations are dynamic (subject to change) and a transition to a new situational context or set of actions may be required to address the new situation.

3.  What is likely to happen in a situation is limited by the situation itself.

Knowledge Codification

  • Knowledge codification means converting tacit knowledge to explicit knowledge in a usable form for the organizational members.
  • Tacit knowledge (e.g., human expertise) is identified and leveraged through a form that is able to produce highest return for the business.
  • Explicit knowledge is organized, categorized, indexed and accessed.
  • The organizing often includes decision trees, decision tables etc.
  • Codification must be done in a form/structure which will eventually build the knowledge base.
    • The resulting knowledge base supports training and decision making.
  1. Diagnosis.
  2. Training/Instruction.
  3. Interpretation.
  4. Prediction.
  5. Planning/Scheduling.
  • The knowledge developer should note the following points before initiating knowledge codification:
  1. Recorded knowledge is often difficult to access (because it is either fragmented or poorly organized).
  2. Diffusion of new knowledge is too slow.
  3. Knowledge is nor shared, but hoarded (this can involve political implications).
  4. Often knowledge is not found in the proper form.Often knowledge is not available at the correct time when it is needed.
  5. Often knowledge is not present in the proper location where it should be present.
  6. Often the knowledge is found to be incomplete.

Modes of Knowledge Conversion

  • Conversion from tacit to tacit knowledge produces socialization where knowledge developer looks for experience in case of knowledge capture.
  • Conversion from tacit to explicit knowledge involves externalizing, explaining or clarifying tacit knowledge via analogies, models, or metaphors.
  • Conversion from explicit to tacit knowledge involves internalizing (or fitting explicit knowledge to tacit knowledge.
  • Conversion from explicit to explicit knowledge involves combining, categorizing, reorganizing or sorting different bodies of explicit knowledge to lead to new knowledge.

Codifying Knowledge

  • An organization must focus on the following before codification:
  1. What organizational goals will the codified knowledge serve?
  2. What knowledge exists in the organization that can address these goals?
  3. How useful is the existing knowledge for codification?
  4. 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 over a long period of time.

Codification Tools/Procedures

Knowledge Maps

  • Knowledge maps originated from the belief that people act on things that they understand and accept.
  • It indicates that self-determined change is sustainable.
  • Knowledge map is a visual representation of knowledge.
  • They can represent explicit/tacit, formal/informal, documented/undocumented, internal/external knowledge.
  • It is not a knowledge repository.
  • It is a sort of directory that points towards people, documents, and repositories.
  • It may identify strengths to exploit and missing knowledge gaps to fill.
  • Knowledge Mapping is very useful when it is required to visualize and explore complex systems.
  • Examples of complex systems are ecosystems, the internet, telecommunications systems, and customer-supplier chains in the stock market.
  • Knowledge Mapping is a multi-step process.
  • Key can be extracted from database or literature and placed in tabular form as lists of facts.
    • These tabled relationships can then be connected in networks to form the required knowledge maps.
    • A popular knowledge map used in human resources is a skills planner in which employees are matched to jobs. Steps to build the map:
  • A structure of the knowledge requirements should be developed.
  • Knowledge required of specific jobs must be defined.
  • You should rate employee performance by knowledge competency.
  • You should link the knowledge map to some training program for career development and job advancement.

knowledge management  KNOWLEDGE ELICITATION

Decision Table

  • It is another technique used for knowledge codification.
  • It consists of some conditions, rules, and actions.

A phone card 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:

“If the amount of the order of phone cards 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 than $20, do not apply any discount.”

We shall develop a decision table for their discounting decisions, where the condition alternatives are `Yes’ and `No’.

CONDITIONS AND ACTIONS RULES
1 2 3 4
Paid within 2 weeks Order>$35 $20<= Order<=$35 Order<$20 Y Y N N Y N Y N Y N N Y N —
5% discount 4% discount No discount X X X X

Example: Decision Table

Decision Tree

  • It is also a knowledge codification technique.
  • A decision tree is usually a hierarchically arranged semantic network.
  • A decision tree for the phone card company discounting policy (as discussed above) is shown next.
  • A frame is a codification scheme used for organizing knowledge through previous experience.

knowledge management  KNOWLEDGE ELICITATION

  • It deals with a combination of declarative and operational knowledge.
    • Key elements of frames:
  1. Slot: A specific object being described/an attribute of an entity.
  2. Facet: The value of an object/slot.

Production Rules

  • They are conditional statements specifying an action to be taken in case a certain condition is true.
  • They codify knowledge in the form of premise-action pairs.
  • Syntax: IF (premise) THEN (action)
  • Example: IF income is `standard’ and payment history is `good’, THEN `approve home loan’.
  • In case of knowledge-based systems, rules are based on heuristics or experimental reasoning.
  • Rules can incorporate certain levels of uncertainty.
  • A certainty factor is synonymous with a confidence level , which is a subjective quantification of an expert’s judgment.
  • The premise is a Boolean expression that should evaluate to be true for the rule to be applied.
  • The action part of the rule is separated from the premise by the keyword THEN.
    • The action clause consists of a statement or a series of statements separated by AND’s or comma’s and is executed if the premise is true.
    • In case of knowledge-based systems, planning involves:
  • Breaking the entire system into manageable modules.
  • Considering partial solutions and liking them through rules and procedures to arrive at a final solution.
  • Deciding on the programming language(s).
  • Deciding on the software package(s).
  • Testing and validating the system.
  • Developing the user interface.
  • Promoting clarity, flexibility; making rules clear.
  • Reducing unnecessary risk.

Role of inferencing:

  • Inferencing implies the process of deriving a conclusion based on statements that only imply that conclusion.
  • An inference engine is a program that manages the inferencing strategies.
    • Reasoning is the process of applying knowledge to arrive at the conclusion.
  1. Reasoning depends on premise as well as on general knowledge.
  2. People usually draw informative conclusions.

Case-Based Reasoning

  • It is reasoning from relevant past cases in a way similar to human’s use of past experiences to arrive at conclusions.
  • 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 presented to the expert.
  • The aim is to bring up the most similar historical case that matches the present case.
  • Adding new cases and reclassifying the case library usually expands knowledge.
  • A case library may require considerable database storage as well as an efficient retrieval system.

Knowledge-Based Agents

  • An intelligent agent is a program code which is capable of performing autonomous action in a timely fashion.
  • They can exhibit goal directed behaviour by taking initiative.
  • they can be programmed to interact with other agents or humans by using some agent communication language.

• In terms of knowledge-based systems, an agent can be programmed to learn from the user behaviour and deduce future behaviour for assisting the user.

Knowledge Developer’s Skill Set Knowledge Requirements

  • Computing technology and operating systems.
  • Knowledge repositories and data mining.
  • Domain specific knowledge.
  • Cognitive psychology.

Skills Requirements

  • Interpersonal Communication.
  • Ability to articulate the project’s rationale.
  • Rapid Prototyping skills.
  • Attributes related to personality.
  • Job roles.
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