Knowledge Engineering Second Term Fourth Year 1

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. 2 Data Information Knowledge applied for a purpose build and process

Knowledge Engineering Definition: The art of bringing the principles and tools of AI research to solve complex problems using expert’s knowledge. 3

Process of Knowledge Engineering Sources of Knowledge Knowledge Representation Knowledge Base Explanation, Justification Inferencing Knowledge Validation 4

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. 5

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. 6

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. 7

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. 8

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 9

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. 10

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. 11

Main KR Approaches Logic Frames/Semantic Networks/Objects Rule based systems 12

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.. 13

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.. 14

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 15

Example  `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 16

Truth Tables 17

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) 18

Example (i) Show that (P  Q)  (  P  Q) Proof: 19 PQ PQPQ PP(  P  Q) TTTFT TFTFT FTTTT FFFTF

Example (ii) Show that  (P  Q)  (  P  Q) Proof: 20 PQ PP QQ(P  Q)  (P  Q)  P  Q TTFFTFF TFFTFTT FTTFFTT FFTTFTT

Sentences 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. 21

Sentences 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. 22

Show that the following formula is valid: ((P  H)  H)  P Proof: Since formula is true for all possible combinations of truth values, the formula is valid. 23 Example PH PHPH HH((P  H)  H)((P  H)  H)  P TTTFFT TFTTTT FTTFFT FFFTFT

Example For each of the following sentences, decide whether it is valid, satisfiable, unsatisfiable or neither. (a)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. 24

Inference 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. 25

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. 26

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.) 27

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, 28

Truth table demonstrating the soundness of the resolution inference rule 29 ABC ABAB BCBCACAC FFFFTF FFTFTT FTFTFF FTTTTT TFFTTT TFTTTT TTFTFT TTTTTT

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. 30

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. 31