Knowledge Acquisition. Concepts of Knowledge Engineering Knowledge engineering The engineering discipline in which knowledge is integrated into computer

Knowledge Acquisition

Concepts of Knowledge Engineering• Knowledge engineering

The engineering discipline in which knowledge is integrated into computer systems to solve complex problems that normally require a high level of human expertise

Concepts of Knowledge Engineering• The knowledge-engineering process

1. Knowledge acquisition

2. Knowledge representation

3. Knowledge validation

4. Inferencing

5. Explanation and justification

Concepts of Knowledge Engineering• Knowledge representation

A formalism for representing facts and rules in a computer about a subject or specialty

• Knowledge validation (verification)

The process of testing to determine whether the knowledge in an artificial intelligence system is correct and whether the system performs with an acceptable level of accuracy

Concepts of Knowledge Engineering

The Scope and Types of Knowledge• Documented knowledge

For ES, stored knowledge sources not based directly on human expertise

• Undocumented knowledge

Knowledge that comes from sources that are not documented, such as human experts

The Scope and Types of Knowledge• Knowledge acquisition from databases

– Many ES are constructed from knowledge extracted in whole or in part from databases

• Knowledge acquisition via the Internet– The acquisition, availability, and

management of knowledge via the Internet are becoming critical success issues for the construction and maintenance of knowledge-based systems

The Scope and Types of Knowledge• Levels of knowledge

– Shallow knowledge – A representation of only surface level

information that can be used to deal with very specific situations

– Deep knowledge – A representation of information about the

internal and causal structure of a system that considers the interactions among the system’s components

The Scope and Types of Knowledge

The Scope and Types of Knowledge• Major categories of knowledge

– Declarative knowledge

A representation of facts and assertions– Procedural knowledge

Information about courses of action. Procedural knowledge contrasts with declarative knowledge

– Metaknowledge

In an expert system, knowledge about how the system operates or reasons. More generally, knowledge about knowledge

Methods of Acquiring Knowledge from Experts• Elicitation of knowledge

The act of extracting knowledge, generally automatically, from nonhuman sources; machine learning

Methods of Acquiring Knowledge from Experts• Knowledge modeling methods

– Manual method A human-intensive method for knowledge acquisition, such as interviews and observations, used to elicit knowledge from experts

– Semiautomatic method A knowledge acquisition method that uses computer-based tools to support knowledge engineers in order to facilitate the process

Methods of Acquiring Knowledge from Experts• Knowledge modeling methods

– Automatic method

An automatic knowledge acquisition method that involves using computer software to automatically discover knowledge from a set of data

Methods of Acquiring Knowledge from Experts• Manual knowledge modeling methods

– Interviews• Interview analysis

An explicit, face-to-face knowledge acquisition technique that involves a direct dialog between the expert and the knowledge engineer

• Walk-through • In knowledge engineering, a process whereby the expert

walks (or talks) the knowledge engineer through the solution to a problem

• Unstructured (informal) interview An informal interview that acquaints a knowledge engineer with an expert’s problem-solving domain


– Structured Interviews • A structured interview is a systematic, goal-

oriented process• It forces organized communication between the

knowledge engineer and the expert


– Process tracking The process of an expert system’s tracing the reasoning process in order to reach a conclusion

– Protocol analysis A set of instructions governing the format and control of data in moving from one medium to another

– Observations

Methods of Acquiring Knowledge from Experts• Automatic knowledge modeling methods

– The process of using computers to extract knowledge from data is called knowledge discovery

– Two reasons for the use of automated knowledge acquisition:• Good knowledge engineers are highly paid and

difficult to find• Domain experts are usually busy and sometimes

uncooperative

Methods of Acquiring Knowledge from Experts• Automatic knowledge modeling methods

– Typical methods for knowledge discovery• Inductive learning• Neural computing• Genetic algorithms

Acquiring Knowledge from Multiple Experts• Major purposes of using multiple experts:

– To better understand the knowledge domain– To improve knowledge-base validity, consistency,

completeness, accuracy, and relevancy– To provide better productivity– To identify incorrect results more easily– To address broader domains– To be able to handle more complex problems and

combine the strengths of different reasoning approaches

Acquiring Knowledge from Multiple Experts• Multiple-expert scenarios

– Individual experts

• Primary and secondary experts– Small groups– Panels

Acquiring Knowledge from Multiple Experts• Methods of handling multiple experts

– Blend several lines of reasoning through consensus methods such as Delphi, nominal group technique (NGT), and group support systems (GSS)

Automated Knowledge Acquisition from Data and Documents

• The objectives of using automated knowledge acquisition:– To increase the productivity of knowledge

engineering (reduce the cost)– To reduce the skill level required from the knowledge

engineer– To eliminate (or drastically reduce) the need for an

expert– To eliminate (or drastically reduce) the need for a

knowledge engineer– To increase the quality of the acquired knowledge

Knowledge Verification and Validation• Knowledge acquired from experts needs

to be evaluated for quality, including:– The main objective of evaluation is to assess

an ES’s overall value– Validation is the part of evaluation that deals

with the performance of the system– Verification is building the system right or

substantiating that the system is correctly implemented to its specifications

Representation of Knowledge

• Production rule

A knowledge representation method in which knowledge is formalized into rules that have IF parts and THEN parts (also called conditions and actions, respectively)


• Major advantages of rules– Rules are easy to understand– Inferences and explanations are easily

derived– Modifications and maintenance are relatively

easy– Uncertainty is easily combined with rules– Each rule is often independent of all others


• Major limitations of rule representation:– Complex knowledge requires thousands of rules,

which may create difficulties in using and maintaining the system

– Builders like rules, so they try to force all knowledge into rules rather than look for more appropriate representations

– Systems with many rules may have a search limitation in the control program

– Some programs have difficulty evaluating rule-based systems and making inferences


• Semantic network

A knowledge representation method that consists of a network of nodes, representing concepts or objects, connected by arcs describing the relations between the nodes



• Frame A knowledge representation scheme that associates one or more features with an object in terms of slots and particular slot values– Slot

A sub-element of a frame of an object. A slot is a particular characteristic, specification, or definition used in forming a knowledge base

– Facet An attribute or a feature that describes the content of a slot in a frame



• Inheritance The process by which one object takes on or is assigned the characteristics of another object higher up in a hierarchy

• Instantiate To assign (or substitute) a specific value or name to a variable in a frame (or in a logic expression), making it a particular “instance” of that variable




• Decision table

A table used to represent knowledge and prepare it for analysis



Reasoning in Intelligent Systems• Commonsense reasoning

The branch of artificial intelligence that is concerned with replicating human thinking

• Reasoning in rule-based systems– Inference engine

The part of an expert system that actually performs the reasoning function

– Rule interpreterThe inference mechanism in a rule-based system

– Chunking A process of dividing and conquering, or dividing complex problems into subproblems

Reasoning in Intelligent Systems• Backward chaining

A search technique that uses IF THEN rules and is used in production systems that begin with the action clause of a rule and works backward through a chain of rules in an attempt to find a verifiable set of condition clauses

Reasoning in Intelligent Systems• Forward chaining

A data-driven search in a rule-based system

Explanation and Metaknowledge• Explanation

An attempt by an ES to clarify its reasoning, recommendations, or other actions (e.g., asking a question)

• Explanation facility (justifier)

The component of an expert system that can explain the system’s reasoning and justify its conclusions

Explanation and Metaknowledge• Why explanations• How explanations• Other explanations

– Who – What– Where– When– Why – How

Explanation and Metaknowledge• Metaknowledge

– Static explanation

In an ES, an association of fixed explanation text with a rule to explain the rule’s meaning.

– Dynamic explanation

In ES, an explanation facility that reconstructs the reasons for its actions as it evaluates rules

Inferencing with Uncertainty


• The importance of uncertainty– Uncertainty is a serious problem– Avoiding it may not be the best strategy.

Instead, we need to improve the methods for dealing with uncertainty


• Representing uncertainty– Numeric representation– Graphic representation– Symbolic representation


• Probabilities and related approaches– Probability ratio– Bayesian approach

• Subjective probability A probability estimated by a manager without the benefit of a formal model

– Dempster–Shafer theory of evidence• Belief function

The representation of uncertainty without the need to specify exact probabilities


• Theory of certainty factors– Certainty theory

A framework for representing and working with degrees of belief of true and false in knowledge-based systems

– Certainty factor (CF) A percentage supplied by an expert system that indicates the probability that the conclusion reached by the system is correct. Also, the degree of belief an expert has that a certain conclusion will occur if a certain premise is true

– Disbelief The degree of belief that something is not going to happen