Knowledge-Based Systems

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Knowledge-Based Systems
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Course Overview

• Introduction
• Knowledge Representation
• Semantic Nets, Frames, Logic
• Reasoning and Inference
• Predicate Logic, Inference Methods, Resolution
• Reasoning with Uncertainty
• Probability, Bayesian Decision Making

• Pattern Matching
• Variables, Functions, Expressions, Constraints
• Expert System Design
• ES Life Cycle
• Expert System Implementation
• Salience, Rete Algorithm
• Expert System Examples
• Conclusions and Outlook

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Overview Knowledge Representation

• Motivation
• Objectives
• Chapter Introduction
• Review of relevant concepts
• Overview new topics
• Terminology
• Knowledge and its Meaning
• Epistemology
• Types of Knowledge
• Knowledge Pyramid

• Knowledge Representation Methods
• Production Rules
• Semantic Nets
• Schemata and Frames
• Logic
• Semantic Web and KR
• Ontologies
• OWL
• RDF
• Important Concepts and Terms
• Chapter Summary

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Motivation

• KBS are useless without the ability to represent
  knowledge
• different knowledge representation schemes may be
  appropriate
• depending on tasks and circumstances
• knowledge representation schemes and reasoning
  methods must be coordinated

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Objectives

• know the basic principles and concepts for
  knowledge representation
• knowledge - information - data
• meaning
• be familiar with the most frequently used
  knowledge representation methods
• logic, rules, semantic nets, schemata
• differences between methods, advantages,
  disadvantages, performance, typical scenarios
• understand the relationship between knowledge
  representation and reasoning
• syntax, semantics
• derivation, entailment
• apply knowledge representation methods
• usage of the methods for simple problems

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Knowledge Engineering

• The process of building an expert system is
• called knowledge Engineering
• Iterative and incremental

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Knowledge Engineering Phases
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Phase 1 Assessment Phase

• Problem practicality
• Expert System Creation Justification
• Determine General Project Idea
• Determine needed resources

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Phase 2 knowledge acquisition

• Knowledge Acquisition study of knowledge ,
  acquisition, organization
• Knowledge Acquisition ? Knowledge base creation
• Identification of knowledge resource (human or
  nonhuman)
• Design methods for knowledge extraction from
  resources according to resource type
• Extract knowledge from resources according to
  designed methods
• Knowledge integration

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Phase 3 Design

• Representation techniques
• Save knowledge in knowledge base
• Knowledge Processing and inferences Techniques
• Prototype

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Test

• Feedback to previous Phases
• Objective System general structure and extracted
  knowledge validation and verification
• Use Expert guidance

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Knowledge and its Meaning

• Epistemology(??? ?????? ????)
• ??? ??? ?? ?????? ?????? ? ???? ???? ?? ? ???
  ????.
• Types of Knowledge
• Knowledge Pyramid

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???? ????? ??? ????? ?? ????? ??????

• ????? ??????
• ???? ????? ???? ????? ???? ????
• ?????
• ?????
• ????
• ???
• ???

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Types of Knowledge

• a priori knowledge (theoretical knowledge)
• comes before knowledge perceived through senses
• considered to be universally true
• a posteriori knowledge (empirical knowledge)
• knowledge verifiable through the senses
• may not always be reliable
• procedural knowledge
• knowing how to do something
• declarative knowledge
• knowing that something is true or false

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Types of Knowledge

• Tacit knowledge (unconscious knowledge)
• knowledge not easily expressed by language
• ?????? ???? ???? ???? gt ?????? ? ?????? ???
  ?????? ? ?????? gt ?????? ?? ? ?????? ?? ?????
  ????? ? ????? ?? ?????
• Neural Network
• Certain Knowledge
• Uncertain knowledge

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Knowledge in Expert Systems
Conventional Programming
Knowledge-Based Systems
Algorithms Data Structures Programs
Knowledge Inference Expert System
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Knowledge Pyramid
Meta-Knowledge
Knowledge
Information
Data
Noise
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Knowledge Representation Methods

• Suppose Access to Problem knowledge
• Problem Knowledge gt L
• Knowledge Intelligible for machines? L
• Convert L to L
• Inefficiency Of Conventional Language
• Need New language

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Knowledge Representation Methods L Properties

• Support knowledge representation (procedural/
  declarative, certain/ uncertain)
• Easy

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Knowledge Representation Methods L

• Production Rules
• Semantic Nets
• Scripts and Frames
• Logic
• Conceptual graphs
• Object-Attribute-Value Triple
• Comparison based on superficial independence ,
  simplicity and intelligibly

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Production Rules

• frequently used to formulate the knowledge in
  expert systems
• Knowledge of Problem is formulated in the form of
  rules
• C1, c2, c3, ? X
• Each rule identify the relationship between a
  sequence of observations and a result

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Production Rules

• Observations
• Attribute - value pair
• Result of a rule
• Procedure
• Example
• Weather- cold, cloudy-yes ? Rainy
• Holiday- yes, Rainy-yes ?stay at home

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Production Rules

• Rules have Superficial independence but can be
  dependent semantically
• Superficial independence
• Easy management

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Production Rules

• 1. If the balls color is
• red Then I like the ball
• 2. If I like the ball
• Then I will buy the
• ball
• Question Balls Color?
• Answer Red

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Example

• 1. If the balls color is
• red Then I like the ball
• 2. If I like the ball
• Then I will buy the
• ball

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Rules Type

• Relationship Rules
• IF the battery is dead Then the car will not
  start
• Recommendation Rules
• IF the car will not start THEN take a cab
• Directive Rules
• IF the car will not start AND the fuel system is
  ok
• THEN check out the electrical system

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Rules Type

• Strategy Rules
• IF the car will not start THEN first check out
  the
• fuel system THEN check out the electrical system
• Heuristic Rules
• IF the car will not start AND the car is a 1957
• Ford THEN check the float

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Rules Type

• Pattern Matching Rules
• IF ?X is Employee AND ?X Agegt65
• THEN ?X can retire
• Meta Rules
• IF the car will not start AND the electrical
  System
• is operating normally
• THEN use rules concerning the fuel system

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Productions

• One formal notation for defining productions is
  the BNF( Backus-Naur form)
• This notation is a Metalanguage for defining the
  syntax of a language
• Syntax define form
• Semantic refer to meaning
• A metalanguage is language for describing
  languages

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Productions

• Many Type of languages
• Natural languages, logic languages, mathematical
  languages, and computer languages
• BNF notation for a simple language rule that a
  sentence consists of a noun and a verb followed
  by punctuation is the following production rule
• ltgt , are symbols of metalanguage
• means is defined as and is BNF equivalent of
  ?
• Term within ltgt are nonterminal symbols
• Terminal cannot be replaced by anything else and
  so is a constant

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Productions

• The following rules complete the nonterminals by
  specifying their possible terminals
• Bar means or in the metalanguage

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Productions

• String (set of terminals)
• Valid Sentence( string can be derived from the
  start symbol)
• Grammar Complete set of production rules that
  define a language??

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Productions

• Parse Tree or Derivation Tree
• Graphic representation of a sentence decomposed
  into all the terminals and nonterminals used to
  derive the sentence

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Productions
Compiler create a parse tree when it tries to
determine whether statements in a program Conform
to the valid syntax of a language
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Production System

• Knowledge base (Production Rules)
• Working Memory
• Interpreter (Inference Engine)
• Three steps called the recognize-act cycle
• 1. Match the variables of the antecedent of a
  rule in knowledge base with WM
• 2. If more than one rule is available decide
  which rule to fire (a strategy for conflict
  resolution)
• 3. Add new item to WM or delete old item from WM
  and go to step 1

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Conflict Resolution strategies

• Refractoriness
• Same rule could not be fired more than once when
  instantiated with the same set of data
• Solution discard or delete the instantiations
  from WM which have been used once ? avoid loop
• Recency
• Most recent element of the working memory be used
  up for instantiating one of the rules
• Specificity
• Rule with more number of antecedent clauses be
  fired than rules handling fewer antecedent
  clauses

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Conflict Resolution strategies

• Specificity
• Rule with more number of antecedent clauses be
  fired than rules handling fewer antecedent
  clauses
• Example
• PR1 Bird(X) ?Fly(X)
• PR2 Bird(X),Not emu(X)? Fly(x)
• Suppose WM contains the Data Bird(X) and Not
  emu.
• Both rule are firable. However the second rule
  should be fired.

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Conflict Resolution strategies

• MYCYN use another approach for resolving
  conflicts via metarules
• Metarules can be either domain-specific or
  domain-free
• Domain-Specific metarule applicable for
  identifying the rule to fire only in a specific
  domains
• Domain-free rules very general kind

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Conflict Resolution strategies

• Domain-Specific metarule
• If 1) the infection is pelvic abscess
• 2)and there are rules which mention in their
  premise entero-bactoriae and
• 3) there are rules which mention in their
  premise gram-positive rods
• Then there exists suggestive evidence (0.4) that
  the former should be applied before the later

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Conflict Resolution strategies

• Domain-free rule
• If 1) there are rules which do not mention the
  current goal in their premise and
• 2) there are rules which mention the current
  goal in their premise
• Then it is definite (1.0) that former should be
  applied before the later

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The conflict resolution with two rules PRi and
PRj has been demonstrated in this architecture.
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An Illustrative Production System

• water-jug problem
• Given 2 water jugs, 4 liters and 3 liters.
  Neither has
• Any measuring marks on it. There is a pump that
• can be used to fill the jugs. How can you get
• exactly 2 liters of water into 4-liter jugs?

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An Illustrative Production System

• U denote content of 4L jug
• V denote content of 3L jug
• Content of two jug will be represented by (U,V)
• Start up element in WM is (0,0)

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An Illustrative Praoduction SystemPR
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An Illustrative Production System

• keep track of the reasoning process ? draw a
  state-space for the problem.
• leaves generated after firing of the rules,
  should be stored in WM.
• first consider all possibilities of the solution
  (i.e., without resolving the conflict).
• Later we would fire only one rule even though
  more than one are fireable.

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State Space without conflict resolution
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Conflict Resolution Strategy

• Avoid doubling back, whenever possible. In other
  words, never attempt to generate old entries.
• Rete Match Algorithm?

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Type of Production Systems

• two special types of production systems
• i) commutative system (??????? ????)
• ii) decomposable system(????? ????)

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Commutative Production System

• A production system is called commutative if for
  a given set of rules R and a working memory WM
  the following conditions are satisfied
• i) Freedom in orderliness of rule firing
  Arbitrary order of firing of the applicable rules
  selected from set S will not make a difference in
  the content of WM.
• In other words, the WM that results due to an
  application of a sequence of rules from S is
  invariant under the permutation of the sequence.

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Commutative Production System

• ii) Invariance of the pre-condition of attaining
  goal If the pre-condition of a goal is satisfied
  by WM before firing of a rule, then it should
  remain satisfiable after firing of the rule.
• iii) Independence of rules The firability
  condition of an yet unfired rule Ri with respect
  to WM remains unaltered, even after firing of the
  rule Rj for any j.

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Decomposable Production System

• A production system is called decomposable if the
  goal G and the working memory can be partitioned
  into Gi and WMi, such that
• G ANDi (Gi ),
• WM ? WMi
• ?i
• rules are applied onto each WMi independently or
  concurrently to yield Gi.
• The termination of search occurs when all the
  goals Gi for all i have been identified.

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Forward versus BackwardProduction Systems

• Most of the common classical reasoning problems
  of AI can be solved by any of the following two
  techniques
• i) forward reasoning or forward chaining
  (Top-Down)
• ii) backward reasoning backward chaining
  (Bottom-UP)

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Forward versus BackwardProduction Systems

• In a forward reasoning problem such as 4-puzzle
  games or the water-jug problem, where the goal
  state is known, the problem solver has to
  identify the states by which the goal can be
  reached.
• These class of problems are generally solved by
  expanding states from the known starting states
  with the help of a domain-specific knowledge
  base.
• The generation of states from their predecessor
  states may be continued until the goal is
  reached.

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Forward versus BackwardProduction Systems

• On the other hand, consider the problem of system
  diagnosis or driving a car from an unknown place
  to home.
• Here, the problems can be easily solved by
  employing backward reasoning, since the
  neighboring states of the goal node are known
  better than the neighboring states of the
  starting states.
• For example, in diagnosis problems, the
  measurement points are known better than the
  cause of defects.
• for the driving problem, the roads close to home
  are known better than the roads close to the
  unknown starting location of driving.
• It is thus clear that, whatever be the class of
  problems, system states from starting state to
  goal or vice versa are to be identified, which
  requires expanding one state to one or more
  states.

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Forward versus BackwardProduction Systems

• If there exists no knowledge to identify the
  right offspring state from a given state, then
  many possible offspring states are generated from
  a known state. This enhances the search-space for
  the goal.
• When the distance (in arc length) between the
  starting state and goal state is long,
  determining the intermediate states and the
  optimal path (minimum arc length path) between
  the starting and the goal state becomes a complex
  problem.

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Forward versus BackwardProduction Systems

• Forward Reasoning

ES
Rule Base
2
1
3
Forward Inference Engine
Response
Observations
1
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Forward Reasoning Inference Mechanism

• 1. Perceive Inputs
• 2. Interpret Inputs based on observations
• 3. Apply action in the environment
• Point ?
• No explicit input problem
• Use WM for observation management
• Relation of inference engine and environment
  by using WM
• WM is a World model (Inference mechanism0

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Forward Reasoning Algorithm

• Based on WM search KB for rules that their
  condition are available in WM (loop until find
  something)
• 1. If more than one rule are available select one
  of them (Conflict resolution)
• 2. Execute (fire) the selected rule (transfer its
  consequence to WM)
• 3. Go to step 1
• Point Time between rule selection and
  execution!!!

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Forward Reasoning Algorithm

• Example
• X ? A-1, B-2, C-3
• Y? C - gt2
• Z? Y, D-1
• X? D-1, M-4
• WM A-1, B-2, C-3, M-4, D-1

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Forward versus BackwardProduction Systems

• Backward Reasoning

ES
Rule Base
2
1
4
Backward Inference Engine
Response
Problem
Observations
3
3
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Inference Mechanism

• 1. Problem trigger inference engine
• 2. Looking for observations which are needed for
  solving the problem
• 3. Apply action in the environment
• Point ? Identification of the problem (correct
  problem)

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Forward Backward Comparison

• Forward
• Observe the entire environment at any instance of
  time
• Observation management (by using working memory)
• Backward
• No need to observe
• Simple observation management mechanism

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Forward versus BackwardProduction Systems

• The following example illustrates the principle
  of forward and backward reasoning with reference
  to the well-known farmers fox-goat-cabbage
  problem.

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farmers fox-goat-cabbage problem

• Example The problem may be stated as follows. A
  farmer wants to transfer his three belongings, a
  wolf, a goat and a cabbage, by a boat from the
  left bank of a river to its right bank. The boat
  can carry at most two items including the farmer.
  If unattended, the wolf may eat up the goat and
  the goat may eat up the cabbage. How should the
  farmer plan to transfer the items?

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farmers fox-goat-cabbage problem

• The illegal states in the problem are (W,G
  F,C) , (G,C F,W), (F, W G, C) and ( F, C
  W, G) where F, G, , W and C denote the
  farmer, the goat, the river, the wolf and the
  cabbage respectively.

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part of the knowledge base

• PR 1 (F, G, W, C Nil ) ? ( W, C F, G)
• PR 2 (W, C F, G) ? ( F, W, C G)
• PR 3 (F, W, C G) ? (C F, W, G)
• PR 4 (C F, W, G) ? ( F, G, C W)
• PR5 (F, G, C W) ? (G F, W, C)
• PR 6 ( G F, W, C) ? ( F, G W, C)
• PR 7 ( F, G, W, C) ? ( Nil F,G, W, C)
• PR 8 ( F, W, C G) ? ( W F, G, C)
• PR 9 ( W F, G, C) ? ( F, G, W C)
• PR 10 ( F, G, W C) ? ( G F, W, C)
• PR 11 ( G F, W, C) ? ( F, G W,C)
• PR 12 ( F, G W, C) ?( Nil F, G, W, C)

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Forward Reasoning

• Starting state ( F, G, W, C Nil)
• Goal state (Nil F, G, W, C)
• one may expand the state-space, starting with
  (F,G,W,C Nil) by the supplied knowledge base,
  as follows

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Backward Reasoning

• The backward reasoning scheme can also be invoked
  for the problem. The reasoning starts with the
  goal and identifies a rule whose right-hand side
  contains the goal. It then generates the left
  side of the rule in a backward manner.
• The resulting antecedents of the rules are called
  sub-goals.
• The sub-goals are again searched among the
  consequent part of the rules and on a successful
  match the antecedent parts of the rule are
  generated as the new sub-goals.
• The process is thus continued until the starting
  node is obtained.

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A caution about backward reasoning

• Backward reasoning1 in many circumstances does
  not support the logical semantics of problem
  solving.
• It may even infer wrong conclusions, when a goal
  or sub-goal (any intermediate state leading to
  the goal ) has multiple causes for occurrence,
  and by backward reasoning we miss the right cause
  and select a wrong cause as its predecessor in
  the state-space graph.

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Example

• Example 3.4 Consider the following knowledge
  base, the starting state and the goal state for a
  hypothetical problem. The , in the left-hand
  side of the production rules PR 1 through PR 4
  denotes joint occurrence of them.
• PR 1 p, q ? s
• PR 2 s, t ? u
• PR 3 p, q, r ? w
• PR 4 w ? v
• PR 5 v, t ? u
• Starting state p and q
• Goal state u.
• Other facts t.

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• The state-space graph for the hypothetical
  problem indicates that the goal can be correctly
  inferred by forward reasoning.
• However, backward reasoning may infer a wrong
  conclusion
• p and q and r, if PR 5, PR 4 and PR 3 are used
  in order starting with the goal.
• Note that r is an extraneous premise, derived by
  backward reasoning. But in practice the goal is
  caused due to p, q and t only.
• Hence, backward reasoning may sometimes yield
  wrong inferences.

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Bi-directional Reasoning

• Instead of employing either forward or backward
  reasoning, both of them may be used together in
  automated problem solving.
• This is required especially in situations when
  expanding from either direction leads to a large
  state-space.

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Bi-directional Reasoning
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Advantages of Production Rules

• expressiveness
• can relevant aspects of the domain knowledge be
  stated through rules?
• computational efficiency
• easy to understand?
• can humans interpret the rules
• easy to generate?
• how difficult is it for humans to construct rules
  that reflect the domain knowledge

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Advantages of Production Rules

• straightforward implementation in computers
  possible

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Problems with Production Rules

• simple implementations are very inefficient
• some types of knowledge are not easily expressed
  in such rules
• large sets of rules become difficult to
  understand and maintain

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Semantic Network
Much human information is organized in terms of
concepts that are linked to each other. Nouns are
organized in terms of kind and part
relations. E.g. a spaniel is a kind of dog which
is a kind of animal. E.g. a claw is part of a
foot which is part of a leg which is part of a
dog. Verbs are organized in terms of ways of
doing, e.g. digging is one way of removing.
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Semantic Nets

• graphical representation for propositional
  information
• originally developed by M. R. Quillian as a model
  for human memory
• Knowledge Representation using graph composed of
  nodes and edges
• nodes represent objects, concepts, or situations
• labels indicate the name
• nodes can be instances (individual objects) or
  classes (generic nodes)
• links represent relationships
• the label indicates the type of the relationship
• without relationships, knowledge is an unrelated
  collection of facts

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Types of Relationships

• relationships can be arbitrarily defined by the
  knowledge engineer
• allows great flexibility
• for reasoning, the inference mechanism must know
  how relationships can be used to generate new
  knowledge
• inference methods may have to be specified for
  every relationship
• frequently used relationships
• IS-A
• relates an instance (individual node) to a class
  (generic node)

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Objects and Attributes

• AKO (a-kind-of)
• relates one class (subclass) to another class
  (superclass)
• attributes provide more detailed information on
  nodes in a semantic network
• often expressed as properties
• combination of attribute and value
• attributes can be expressed as relationships
• e.g. has-attribute

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Semantic Nets

• Points
• Bird has wings
• Move with fly
• canary is-a bird
• direction is important
• Graph has three attributes
• Has, Is-A, Travel

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Semantic Networks

• Semantic Networks can extend by
• Same Concepts
• Specialize
• Generalize

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Semantic Networks
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Inheritance

• Is an important attribute in Semantic Networks
• Means that Concept or attribute inherit from a
  node
• Represent by Is-A
• Example Bird has all attributes of animals
• Inheritance decrease knowledge base, prevent
  repetition

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Semantix Net Example
Abraracourcix
Astérix
is-boss-of
is-boss-of
Cétautomatix
is-a
is-a
is-friend-of
buys-from
is-a
Obélix
Gaul
is-a
fights-with
is-a
AKO
Dog
Panoramix
takes-care-of
is-a
lives-with
Human
is-a
sells-to
barks-at
Idéfix
Ordralfabetix
http//www.asterix.tm.fr
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Semantix Net Example
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Problems Semantic Nets

• expressiveness
• no internal structure of nodes
• relationships between multiple nodes
• no easy way to represent heuristic information
• best suited for binary relationships
• efficiency
• may result in large sets of nodes and links
• usability
• lack of standards for link types
• naming of nodes
• classes, instances

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Semantic network

• Unsuitable
• Declarative Knowledge
• Procedural Knowledge
• Superficial Knowledge Structure

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Semantic Network

• Superficial Knowledge Structure
• Knowledge structured in the form of relationship
  and semantic network nodes
• Deep Knowledge Structure
• causal relationships between a percept or action
  and its outcome
• Explain why events occurred

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Semantic Networks

• Medicine Expert System
• Superficial Knowledge
• First rule
• IF a person has a fever then take an asprin
• Biochemical Bases of human body? Why asprin
  decreases fever
• If a person has a pink monkey then take a
  refrigerator

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Semantic Networks

• Superficial aspect of the knowledge of expert
  system ? depends on combination of sentences not
  their meanings
• You can replace every two words with X,Y in the
  Following Rule
• If a person has a x then take a y
• X and Y arent variables but identify any Two
  Words

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Semantic Networks

• Medicos have causal knowledge
• Various careers and have many experiences
• If a method doesnt work Medico can reason and
  replace method with another method
• Knowledge Of real environments often cant
  represent by semantic networks
• We need more complicated Structures

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Semantic Networks

• Knowledge Structure and Data Structure
• Instead of data an ordered set of knowledge
  considered

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There are other kinds of links between concepts
representing other kinds of links, e.g. STUDENT
is linked to COURSES because students TAKE
courses. Exercise draw a semantic network for
UNIVERSITY, including part, kind, and other
relations
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Object-Attribute-Value

• Semantic Net
• One problem No standard definition of link names
• IS-A (IS-A and AKO)
• IS-A and Instance-of
• ART Expert system IS-A (AKO) and Instance-of

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Object-Attribute-Value

• Another common link is HAS-A
• HAS-A( class to subclass opposite AKO)
• IS-A relate a value to an attribute whereas a
  HAS-A relates an object to an attribute

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Object-Attribute-Value

• Three items of object-attribute-value( OAV) occur
  frequently ? build simple semantic net using them
• The semantic net for such as system consists of
  nodes for objects, attributes and values
  connected by HAS-A and IS-A links

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Schemata

• suitable for the representation of more complex
  knowledge
• causal relationships between a percept or action
  and its outcome
• deeper knowledge than semantic networks
• nodes can have an internal structure
• for humans often tacit knowledge
• related to the notion of records in computer
  science

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Concept Schema

• abstraction that captures general/typical
  properties of objects
• has the most important properties that one
  usually associates with an object of that type
• may be dependent on task, context, background and
  capabilities of the user,
• similar to stereotypes
• makes reasoning simpler by concentrating on the
  essential aspects
• may still require relationship-specific inference
  methods

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Schema

• Schemata Has internal structures for nodes
• unlike semantic networks (labels says every
  thing)
• Semantic networks ? data structures that search
  is based on the data saved in the nodes
• Schemata is same as a data structure that nodes
  contains records

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Schema Examples

• the most frequently used instances of schemata
  are
• frames Minsky 1975
• scripts Schank 1977
• frames consist of a group of slots and fillers to
  define a stereotypical objects
• scripts are time-ordered sequences of frames

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????

• ?? ????? ????? ??? ?? ???? ??? ???? ????? ???.
• ????? ?? ??? ???? ????? ?? ?????? ?? ?? ???
  (IS-A)
• ????? ???? ???? ??? ?????? ???? ?? ???. ?????
  ???? ??????.
• ????? ???? ?? ???? ??? ???? gt ????? ?? ????
• ???? ???? ????????? ????? ??? ???????? ??? ??
  ???? ??? ????? ?? ??? ???.

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????

• ????? ????? ??? ???? ??? ???? ?????? ???? ?? UML
  ???.
• ??? ???? ??? ???? ?? ??? ?????? ??? gt ???? ????
  ???? ????? ?? ??? gt ???? ???? ????? lisp ??
  ???? ?? ?????? ???? ??? ???? ?? ?? ??????.
• ????? ??? procedure ?? ???? ??? ???? gt ????

128
????
???? ?? ?????? ? ???? ??
129
????

• ???? ?? ????? ?????? ?? ?????? ?????? ?? ????? ??
  ??? ??? ????? ?? ???. ?? ??? ?????? ??? ?? ??
  ?????? ?? ?????? ?? ?? ???
• ????? ????? ???? ?????? ????? gt ??? ???
• ?????? ????? ?????? ????? ????? gt ???
• ??? ????? 1 ? ????? ???? x gt ??? ???

130
????

• ?? ?? ????? ???? ????? ?????? ( ???? ??????--gt
  ????? ?? ?? ???? ????? ??? ?????) ???? ???? ????
  ???? ????? (???? ???? ?? --gt ????? ?? ?? ????
  ????? ?? ?????) ?? ??? ???? ????.
• ?? ??? ?????? ??? ?? ???? ?????? ? ???? ?? ??
  ??????? ?? ????? ??? ?????? ? ????? ?????? ??????
  ? ?? ??? ???? ?? ???.
• ??? ?? ?? ?????? ?? ?????? ?? ????? ????? ????
  ?????.

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????

• ?? ???? ??? ?????
• ?????
• ???
• ?? ??? ??? ????? 1 ??? ??? ?????? ???? ?? ?? ????
  ?? ???? ??? ?? ??? ????
• ??? ??? ???? (concept )
• ??? ??? ????? (instance )

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????

• ??? ??? ???? ????
• ???
• ?? ?????? ?????? ?? ????? ?? ( ?? ?? ???? ??
  ????? ?? ???? ?? ???? ?? ?????? ?? ???? ??? ????
  ??? ??? ???? ?? ???)?
• ??? ? ??? ?????? ???.
• ?????? ????? ??? ?????? ?? ???? ????? ??? ??????
  ????? ????.

133
????

• ????? ????
• ??? ????
• ??????? string(30)
• ????? ??? ?????
• ????? ????? ????

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????

• ??? ????? ????
• ???
• ???? ?? ....(??? ????)?
• ?? ???? ?? ????? ?? ??? ???? ?? ??? ???? ???? ??
  ????? ?? ??? ????? ?? ??? ????? ?? ???.(slot )

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????

• ???? 01
• ????
• X
• 11/11/2004
• 250
• ??????? ??? ??? ??? ????? ????? ?? ??? (?? ????
  ??????? ?? slot ?? ( ????? ? ???????))

136
????

• ????? ????? ?? ????? ?? ???? if-needed ?
  if-changed ???? ?? ?????.

137
?????? ??? ???
138
Frame

• represents related knowledge about a subject
• provides default values for most slots
• frames are organized hierarchically
• allows the use of inheritance
• knowledge is usually organized according to cause
  and effect relationships
• slots can contain all kinds of items
• rules, facts, images, video, comments, debugging
  info, questions, hypotheses, other frames
• slots can also have procedural attachments
• procedures that are invoked in specific
  situations involving a particular slot
• on creation, modification, removal of the slot
  value

139
Simple Frame Example
140
Overview of Frame Structure

• two basic elements slots and facets (fillers,
  values, etc.)
• typically have parent and offspring slots
• used to establish a property inheritance
  hierarchy (e.g., specialization-of)
• descriptive slots
• contain declarative information or data (static
  knowledge)
• procedural attachments
• contain functions which can direct the reasoning
  process (dynamic knowledge) (e.g., "activate a
  certain rule if a value exceeds a given level")
• data-driven, event-driven ( bottom-up reasoning)
• expectation-drive or top-down reasoning
• pointers to related frames/scripts - can be used
  to transfer control to a more appropriate frame

Rogers 1999
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Slots

• each slot contains one or more facets
• facets may take the following forms
• values
• default
• used if there is not other value present
• range
• what kind of information can appear in the slot
• if-added
• procedural attachment which specifies an action
  to be taken when a value in the slot is added or
  modified (data-driven, event-driven or bottom-up
  reasoning)
• if-needed
• procedural attachment which triggers a procedure
  which goes out to get information which the slot
  doesn't have (expectation-driven top-down
  reasoning)
• other
• may contain frames, rules, semantic networks, or
  other types of knowledge

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Usage of Frames

• filling slots in frames
• can inherit the value directly
• can get a default value
• these two are relatively inexpensive
• can derive information through the attached
  procedures (or methods) that also take advantage
  of current context (slot-specific heuristics)
• filling in slots also confirms that frame or
  script is appropriate for this particular
  situation

Rogers 1999
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Restaurant Frame Example

• generic template for restaurants
• different types
• default values
• script for a typical sequence of activities at a
  restaurant

Rogers 1999
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Generic RESTAURANT Frame Specialization-of
Business-Establishment Types range
(Cafeteria, Fast-Food, Seat-Yourself,
Wait-To-Be-Seated) default
Seat-Yourself if-needed IF
plastic-orange-counter THEN Fast-Food,
IF stack-of-trays THEN Cafeteria,
IF wait-for-waitress-sign
or reservations-made THEN Wait-To-Be-Seated,
OTHERWISE
Seat-Yourself. Location range
an ADDRESS if-needed (Look at the
MENU) Name if-needed (Look at the
MENU) Food-Style range (Burgers,
Chinese, American, Seafood, French)
default American if-added
(Update Alternatives of Restaurant) Times-of-Opera
tion range a Time-of-Day
default open evenings except
Mondays Payment-Form range
(Cash, CreditCard, Check, Washing-Dishes-Script) E
vent-Sequence default
Eat-at-Restaurant Script Alternatives
range all restaurants with same
Foodstyle if-needed (Find all
Restaurants with the same Foodstyle)
Rogers 1999
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Restaurant Script
EAT-AT-RESTAURANT Script Props
(Restaurant, Money, Food, Menu, Tables,
Chairs) Roles
(Hungry-Persons, Wait-Persons, Chef-Persons) Point
-of-View Hungry-Persons Time-of-Occur
rence (Times-of-Operation of
Restaurant) Place-of-Occurrence (Location of
Restaurant) Event-Sequence first
Enter-Restaurant Script then if
(Wait-To-Be-Seated-Sign or Reservations)
then Get-Maitre-d's-Attention
Script then Please-Be-Seated
Script then Order-Food-Script
then Eat-Food-Script unless (Long-Wait)
when Exit-Restaurant-Angry Script then
if (Food-Quality was better than Palatable)
then Compliments-To-The-Chef
Script then Pay-For-It-Script
finally Leave-Restaurant Script
Rogers 1999
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Frame Advantages

• fairly intuitive for many applications
• similar to human knowledge organization
• suitable for causal knowledge
• easier to understand than logic or rules
• very flexible

148
Frame Problems

• it is tempting to use frames as definitions of
  concepts
• not appropriate because there may be valid
  instances of a concept that do not fit the
  stereotype
• exceptions can be used to overcome this
• can get very messy
• inheritance
• not all properties of a class stereotype should
  be propagated to subclasses
• alteration of slots can have unintended
  consequences in subclasses

149
Logic

• here emphasis on knowledge representation
  purposes
• logic and reasoning is discussed in the next
  chapter

150
Representation, Reasoning and Logic

• two parts to knowledge representation language
• syntax
• describes the possible configurations that can
  constitute sentences
• semantics
• determines the facts in the world to which the
  sentences refer
• tells us what the agent believes

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Reasoning

• process of constructing new configurations
  (sentences) from old ones
• proper reasoning ensures that the new
  configurations represent facts that actually
  follow from the facts that the old configurations
  represent
• this relationship is called entailment and can be
  expressed asKB alpha
• knowledge base KB entails the sentence alpha

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Inference Methods

• an inference procedure can do one of two things
• given a knowledge base KB, it can derive new
  sentences ? that are (supposedly) entailed by KB
  KB - ? gt KB ?
• given a knowledge base KB and another sentence
  alpha, it can report whether or not alpha is
  entailed by KB KB ? ? gt KB ?
• an inference procedure that generates only
  entailed sentences is called sound or
  truth-preserving
• the record of operation of a sound inference
  procedure is called a proof
• an inference procedure is complete if it can find
  a proof for any sentence that is entailed

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KR Languages and Programming Languages

• how is a knowledge representation language
  different from a programming language (e.g. Java,
  C)?
• programming languages can be used to express
  facts and states
• what about "there is a pit in 2,2 or 3,1 (but
  we don't know for sure)" or "there is a wumpus in
  some square"
• programming languages are not expressive enough
  for situations with incomplete information
• we only know some possibilities which exist

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KR Languages and Natural Language

• how is a knowledge representation language
  different from natural language
• e.g. English, Spanish, German,
• natural languages are expressive, but have
  evolved to meet the needs of communication,
  rather than representation
• the meaning of a sentence depends on the sentence
  itself and on the context in which the sentence
  was spoken
• e.g. Look!
• sharing of knowledge is done without explicit
  representation of the knowledge itself
• ambiguous (e.g. small dogs and cats)

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Good Knowledge Representation Languages

• combines the best of natural and formal
  languages
• expressive
• concise
• unambiguous
• independent of context
• what you say today will still be interpretable
  tomorrow
• efficient
• the knowledge can be represented in a format that
  is suitable for computers
• practical inference procedures exist for the
  chosen format
• effective
• there is an inference procedure which can act on
  it to make new sentences

Rogers 1999
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Example Representation Methods
Guinness 1995
157
Ontologies

• principles
• definition of terms
• lexicon, glossary
• relationships between terms
• taxonomy, thesaurus
• purpose
• establishing a common vocabulary for a domain
• graphical representation
• UML, topic maps,
• examples
• IEEE SUO, SUMO, Cyc, WordNet

158
Terminology

• ontology
• provides semantics for concepts
• words are used as descriptors for concepts
• lexicon
• provides semantics for all words in a language by
  defining words through descriptions of their
  meanings
• thesaurus
• establishes relationships between words
• synonyms, homonyms, antonyms, etc.
• often combined with a taxonomy
• taxonomy
• hierarchical arrangement of concepts
• often used as a backbone for an ontology

159
What is the Semantic Web?

• Based on the World Wide Web
• Characterized by resources, not text and images
• Meant for software agents, not human viewers
• Defined by structured documents that reference
  each other, forming potentially very large
  networks
• Used to simulate knowledge in computer systems
• Semantic Web documents can describe just about
  anything humans can communicate about

160
Ontologies and the Semantic Web

• Ontologies are large vocabularies
• Defined within Semantic Web documents (OWL)
• Define languages for other documents (RDF)
• Resources can be instances of ontology classes
• Upper Ontologies define basic, abstract concepts
• Lower Ontologies define domain-specific concepts
• Meta-ontologies define ontologies themselves

161
Ontology Terms

• precision
• a term identifies exactly one concept
• expressiveness
• the representation language allows the
  formulation of very flexible statements
• descriptors for concepts
• ideally, there should be a one-to-one mapping
  between a term and the associated concept (and
  vice versa) high precision, and high
  expressiveness
• this is not the case for natural languages
• parasitic interpretation of terms often implies
  meaning that is not necessarily specified in the
  ontology

162
IEEE Standard Upper Ontology

• project to develop a standard for ontology
  specification and registration
• based on contributions of three SUO candidate
  projects
• IFF
• OpenCyc/CycL
• SUMO
• Standard Upper Ontology Working Group (SUO WG),
  Cumulative Resolutions, 2003, http//suo.ieee.org/
  SUO/resolutions.html

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OpenCyc

• derived from the development of Cyc
• a very large-scale knowledge based system
• Cycorp, The Syntax of CycL, 2002,
  http//www.cyc.com/cycdoc/ref/cycl-syntax.html

164
SUMO

• stands for Suggested Upper Merged Ontology
• Niles, Ian, and Adam Pease, Towards a Standard
  Upper Ontology, 2001
• Standard Upper Ontology Working Group (SUO WG),
  Cumulative Resolutions, 2003, http//suo.ieee.org/
  SUO/resolutions.html

165
WordNet

• online lexical reference system
• design is inspired by current psycholinguistic
  theories of human lexical memory
• English nouns, verbs, adjectives and adverbs
• organized into synonym sets, each representing
  one underlying lexical concept
• related efforts for other languages

166
Lojban

• artificial, logical, human language derived from
  a language called Loglan
• one-to-one correspondence between concepts and
  words
• high precision
• high expressiveness
• audio-visually isomorphic nature
• only one way to write a spoken sentence
• only one way to read a written sentence
• Logical Language Group, Official Baseline
  Statement, 2005
• http//www.lojban.org/llg/baseline.html

167
What is Lojban?

• A constructed/artificial language
• Developed from Loglan
• Dr. James Cooke Brown
• Introduced between 1955-1960
• Maintained by The Logical Language Group
• Also known as la lojbangirz.
• Branched Lojban off from Loglan in 1987

Brandon Wirick, 2005
168
Main Features of Lojban

• Usable by Humans and Computers
• Culturally Neutral
• Based on Logic
• Unambiguous but Flexible
• Phonetic Spelling

• Easy to Learn
• Large Vocabulary
• No Exceptions
• Fosters Clear Thought
• Variety of Uses
• Demonstrated with Prose and Poetry

Brandon Wirick, 2005
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Lojban at a Glance
Example sentence in English Wild dogs
bite. Translation into Lojban loi cicyge'u cu
batci cilce (cic) - x1 is wild/untamed gerku
(ger, ge'u) - x1 is a dog/canine of species/breed
x2 batci (bat) - x1 bites/pinches x2 on/at
specific locus x3 with x4 cilce gerku ? (cic)
(ge'u) ? cicyge'u
Brandon Wirick, 2005
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How Would Lojban and the Semantic Web Work
Together?

• Currently, most upper ontologies use English
• Not really English, but arbitrary class names
• Classes meanings cannot be directly inferred
  from their names, nor vice-versa
• Translating English prose into Semantic Web
  documents would be difficult
• Class choices depend on context within prose
• English prose is highly idiomatic
• Lojban does not have these problems

Brandon Wirick, 2005
171
English v. Lojban
Brandon Wirick, 2005
172
OWL to the Rescue

• XML-based. RDF on steroids.
• Designed for inferencing.
• Closer to the domain.
• Dont need a PhD to understand it.
• Information sharing.
• RDF-compatible because it is RDF.
• Growing number of published OWL ontologies.
• URIs make it easy to merge equivalent nodes.
• Different levels
• OWL lite
• OWL DL (description logics)
• OWL full (predicate logic)

Frank Vasquez, 2005
173
Description Logic

• Classes
• Things, categories, concepts.
• Inheritance hierarchies via subclasses.
• Properties
• Relationships, predicates, statements.
• Can have subproperties.
• Individuals
• Instances of a class.
• Real subjects and objects of a predicate.

Frank Vasquez, 2005
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Visualizing the Data Model

• Venn Diagrams and Semantic Networks.

Images from University of Manchester
Frank Vasquez, 2005
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RDF Ontologies

• Dublin Core
• FOAF
• RDF vCard
• RDF Calendar
• SIMILE Location
• SIMILE Job
• SIMILE Apartment

Frank Vasquez, 2005
176
Fixing Modeling Conflicts
1. mapAL Match(MA, ML)
Frank Vasquez, 2005
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Post-Test
178
Evaluation

• Criteria

179
Important Concepts and Terms

• attribute
• common-sense knowledge
• concept
• data
• derivation
• entailment
• epistemology
• expert system (ES)
• expert system shell
• facet
• frame
• graph
• If-Then rules
• inference
• inference mechanism
• information
• knowledge

• knowledge base
• knowledge-based system
• knowledge representation
• link
• logic
• meta-knowledge
• node
• noise
• object
• production rules
• reasoning
• relationship
• rule
• schema
• script
• semantic net
• slot

180
Summary Knowledge Representation

• knowledge representation is very important for
  knowledge-based system
• popular knowledge representation schemes are
• rules, semantic nets, schemata (frames, scripts),
  logic
• the selected knowledge representation scheme
  should have appropriate inference methods to
  allow reasoning
• a balance must be found between
• effective representation, efficiency,
  understandability

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