Knowledge engineering
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Knowledge Engineering. Second Term Fourth Year. Data, Information and Knowledge. Data: Data is unprocessed facts and figures without any added interpretation or analysis. Information: Data that has been interpreted so that it has meaning for the user. Knowledge:

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Knowledge Engineering

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Knowledge Engineering

Second Term Fourth Year

Data, Information and Knowledge

  • Data:

    Data is unprocessed facts and figures without any added interpretation or analysis.

  • Information:

    Data that has been interpreted so that it has meaning for the user.

  • Knowledge:

    Knowledge is combination of information, experience and insight that may benefit the individual or the organization.





for a purpose

build and


Knowledge Engineering

Definition: The art of bringing the principles and tools of AI research to solve complex problems using expert’s knowledge.

Process of Knowledge Engineering

Sources of Knowledge







Explanation, Justification


Levels of Knowledge

  • Shallow Knowledge:

    • Shallow knowledge refers to representation of only surface level information that can be used to deal with very specific situations.

    • The shallow knowledge may be presented in terms of IF-THEN rules.

      Example: If gasoline tank is empty  car will not start

    • The Shallow version represents input-output relationship of a system.

    • It can be presented in terms of IF-THEN rules.

    • Shallow knowledge may be insufficient in describing complex situations.

Levels of Knowledge

  • Deep Knowledge:

    • Deep knowledge refers to the internal and causal structure of a system and considers the interactions among the system’s components.

    • Human problem solving is based on deep knowledge of a situation.

    • Deep knowledge can be applied to different tasks and different situations.

    • This knowledge is difficult to computerize.

Categories of Knowledge

  • Declarative Knowledge:

    • Declarative knowledge is a descriptive representation of knowledge.

  • Procedural Knowledge:

    • Procedural knowledge considers the manner in which things work under different set of circumstances.

  • Semantic Knowledge:

    • Semantic knowledge reflects cognitive structure that involves the use of the long-term memory.

      • words and other symbols.

      • word symbol meanings and usage rules.

      • word symbol referents and interrelationships.

      • algorithms for manipulating symbols, concepts and relations.

Categories of Knowledge

  • Episodic Knowledge:

    • Episodic knowledge is autobiographical, experimental information organized as a case or an episode. It is thought to reside in long term memory, usually classified by time and place.

  • Metaknowledge:

    • Metaknowledge means knowledge about knowledge. In AI, meta-knowledge refers to knowledge about the operation of knowledge based systems, that is, about its reasoning capabilities.

Knowledge Representation (KR)

Assumption of (traditional) AI work is that:

  • Knowledge may be represented as “symbol structures” (essentially, complex data structures) representing bits of knowledge (objects, concepts, facts, rules, strategies ...)

    • E.g. “red”represents colour red.

    • “Car1” represents my car.

    • red(car1) represents fact that my car is red.

  • Intelligent behaviour can be achieved through manipulation of symbol structures

Knowledge Representation Languages

  • Knowledge representation languages have been designed to facilitate this.

  • Rather than use general C++/Java data structures, use special purpose formalisms.

  • A KR language should allow you to:

    • represent adequately the knowledge you need for your problem (representational adequacy).

    • do it in a clear, precise and natural ways.

    • allow you to reason on that knowledge, drawing new conclusions.

Properties of a good knowledge based system

  • Representational Adequacy: the ability to represent all kinds of knowledge that are needed in that domain.

  • Inferential Adequacy: the ability to manipulate the representational structures in such a way as to derive new structures corresponding to new knowledge inferred from old.

  • Inferential Efficiency: the ability to incorporate into the knowledge structure additional information that can be used to focus the attention of the inference mechanisms in the most promising directions.

  • Acquisitional Efficiency: the ability to acquire new information easily.

Main KR Approaches

  • Logic

  • Frames/Semantic Networks/Objects

  • Rule based systems

Logic as a Knowledge Representation Language

  • A Logic is a formal language, with precisely defined syntax and semantics, which supports sound inference. Independent of domain of application.

  • Different logics exist, which allow you to represent different kinds of things, and which allow more or less efficient inference.

    • propositional logic, predicate logic, temporal logic, modal logic, description logic..

  • But representing some thing in logic may not be very natural, and inferences may not be efficient. More specialized languages may be better..

Propositional Logic

  • In general a logic is defined by

    • syntax: what expressions are allowed in the language.

    • Semantics: what they mean, in terms of a mapping to real world

    • proof theory: how we can draw new conclusions from existing statements in the logic.

  • Propositional logic is the simplest..

Propositional Logic

  • The Propositional Logic allows facts about the world to be represented as sentences formed from:

  • Propositional symbols: P, Q, R, S...

  • And: 

  • Or: 

  • Not: 

  • Implies: 

  • If and only if: 

  • Wrapping parentheses: (...)

  • logical constants: true, false, unknown


  • `It is humid': Q

  • `It is humid and hot': QP

  • `If it is hot and humid, then it is raining':

    (PQ)  R

  • ‘it is humid but not hot’: PQ

Truth Tables

Some Identities

For Propositional Expressions P, Q and R:

  • (P)  P

  • (PQ)  (PQ)

  • The Contrapositive Law: (PQ)  (QP)

  • de Morgan’s Laws: (a)(PQ)  (PQ) and

    (b)(PQ)  (PQ)

  • The Commutative Laws: (PQ)  (QR) and

    (PQ) (QR)

  • The Associative Laws: ((PQ)R)  (P(QR))

    ((PQ)R)  (P(QR))

  • The Distributive Laws: P(QR)) (PQ) (PR)

    P(QR)) (PQ) (PR)


(i) Show that (PQ)  (PQ)



(ii) Show that (PQ)  (PQ)



  • An expression that is always true (under any interpretation is called a tautology.

  • If A is tautology, we write ╞ A

  • A logical expression that is a tautology is often described as being VALID.

    Examples: Ali lives in Karachi Ali lives in Pakistan, A man is mortal, a lion is an animal.

  • If an expression is false in any interpretation, it is described as being CONTRADICTORY:

    A¬ A

    Example: I study in MUET  I am not student of MUET.


  • A sentence is SATISFIABLE, if and only if there is some interpretation in some world for which it is true.

  • Some expressions are SATISFIABLE, but not valid. They are true under some interpretation but not under all interpretations.

    • teacher  not a student

    • intelligent student  position holder

    • I live in Sindh  I live in Karachi

      A contradictory expression is clearly not satisfiable so is described as being UNSATISFIABLE.


Show that the following formula is valid:

((PH)H)  P


Since formula is true for all possible combinations of truth values, the formula is valid.


For each of the following sentences, decide whether it is valid, satisfiable, unsatisfiable or neither.

  • Smoke  Smoke

    Answer: Valid

    (b)Smoke  Fire

    Answer: Satisfiable

    (c)(Smoke Fire)(Smoke Fire)

    Answer: Satisfiable

    (d)Smoke  Fire  Fire

    Answer: Valid

    (e)((Smoke heat)Fire)  ((SmokeFire)(HeatFire)

    Answer: Valid

    (f)(Smoke  Fire)((Smoke heat)Fire)

    Answer: Valid

    H.W.: show the validity of (d), (e) and (f) using truth tables.


  • Inference is the process of deriving new sentences from old ones.

  • Inference Rules are patterns of sound inference that can be used to find proofs.

Rules of Inference for Propositional Logic

  • Let B can be derived from A by inference. This is written as

  • Some Popular Inference Rules

    (i) Modus Ponens or Implication Elimination:

    If A is true and A=> B is true, then conclude B is true.

    (ii) And Elimination: (From a conjuction, you can infer any of the conjuctants.

Rules of Inference for Propositional Logic

(iii) And Induction:(From a list of sentences, you can infer their conjuction)

(iv) Or-Induction:(From a sentence, you can infer its disjuction with anything else at all).

(v) Double-Negation Elimination: (From a doubly negated sentence, you can infer a positive sentence.)

Rules of Inference for Propositional Logic

(vi) Unit Resolution: (From a disjunction, if one of the disjuncts is false, then you can infer the other one is true.

(vii) Resolution:

or equivalently,

Truth table demonstrating the soundness of the resolution inference rule

The Predicate Calculus

  • In propositional calculus, each atomic sentence (P, Q etc.) denotes a proposition of some complexity. There is no way to access the components of an individual assertion. Predicate calculus provides this ability.

  • Example:

    In Propositional logic, we may write the entire sentence:

    P = “It rained on Tuesday”

    In Predicate Calculus, we may create a predicate weather that describes a relationship between a date/day and weather:

    weather (Tuesday, rain)

    Through inference rules, we can manipulate predicate calculus expressions, accessing their individual components and inferring new sentences.

Predicate Calculus

  • Predicate calculus allows expressions to contain variables.

  • Variables let us create general assertions about classes of entities:

    Example: we could state that for all values of X, where X is a day of the week, the statement

    weather(X, rain)

    is true; i.e. It rains everyday.

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