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Course : T0264 – Artificial Intelligence
Year : 2013
LECTURE 11
Knowledge Representation
Outline
1.
2.
3.
4.
5.
6.
Ontological Engineering
Categories and Objects
Event
Mental Event and Mental Objects
Reasoning Systems for Categories
Reasoning with Default Information
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Ontological
Engineering
What Is An Ontology
• An ontology is an explicit description of a domain:
– concepts
– properties and attributes of concepts
– constraints on properties and attributes
• An ontology defines
– a common vocabulary
– a shared understanding
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Ontological
Engineering
What Is “Ontology Engineering”?
• Ontology Engineering: Defining terms in the domain and
relations among them
– Defining concepts in the domain (classes)
– Arranging the concepts in a hierarchy (subclasssuperclass hierarchy)
– Defining which attributes and properties (slots)
classes can have and constraints on their values
– Defining individuals and filling in slot values
• In computer science and information system, an
ontology formally represents knowledge as a set of
concepts within a domain, and the relationships among
those concepts.
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Ontological
Engineering
Ontology of the word
Anything
AbstractObject
Set
Categories
Numbers
GeneralizedEvents
Rep.Object
Sentences Measurements
Weights
Times
Interval
Moments
Places
PhyisicalObject
Things
Animals
Agents
Processes
Stuff
Solid
Liquid
Gas
Humans
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Ontological
Engineering
Two major characteristics of general-purpose ontologies :
 A general-purpose ontology should be applicable in more or
less any special-purpose domain (with the addition of
domain-specific axioms). This means that no
representational issue can be finessed or brushed under the
carpet.
 In any sufficiently demanding domain, different areas of
knowledge must be unified, because reasoning and problem
solving could involve several areas simultaneously. A robot
circuit-repair system, for instance, needs to reason about
circuits in terms of electrical connectivity and physical layout,
and about time, both for circuit timing analysis and
estimating labor costs
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Ontological
Engineering
Layers of Functional Ontology and Knowledge
Specific to an
object.
Dependent
on
designers
viewpoint
Functional model of the
target artifact
Function
decomposition tree
Generic function
decomposition tree
Attribute trees
combination
Ways of functional
achievement
General
knowledge
Description of way of achievement
General
concepts
Viewpoint-specific
structuring
Functional concept
ontology
Part library
reference
Physical law
Principle
Conceptualization of function
Extended device ontology
Fundamental
Specialization from device-centered view
Top level ontology(entity, process, time, etc.)
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Categories
and Objects
• First-order logic makes it easy to state facts about
categories, either by relating objects to categories or by
quantifying over their members.
• Some types of facts :
- An object is member of category
- A category is a subclass of another category
- All members of category have some properties
- Member of a category can be recognized by some
properties
- A category as a whole has some properties
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Categories
and Objects
The organization of objects into categories is a vital part of
Knowledge Representation.
Important relationships are
subclass relation (AKO - a kind of)
<category> AKO <category>.
instance relation ( ISA - is a)
<object> ISA <category>.
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Categories
and Objects
Categories
• Category is a kind of set and denotes a set of objects.
• A category has a set of properties that is common to all its
members.
• Categories are formally represented in logic as predicates, but
we will also regard categories as a special kind of objects.
• We then actually introduce a restricted form of second order
logic, since the the terms that occur may be predicates.
Example: Elephants and Mammals are categories.
• The set denoted by Elephants is a subset of the set denoted by
Mammals.
• The set of properties common to Elephants is a superset of the
set of properties common to Mammals.
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Categories
and Objects
Taxonomy
Subcategory relations organize categories into a
taxonomy or taxonomic hierarchy.
Other names are type hierarchy or class hierarchy.
We state that a category is a subcategory of another
category by using the notation for subsets
Basketball  Ball
We will also use the notation
ako (basketball, ball).
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Categories
and Objects
Category Representations
There are two choices of representing categories in first order logic:
predicates and objects. That is, we can use the predicate Basketball(b) or
we can reify the category as an ”object” basketball. We could then write
member(x,basketball)
or
x  basketball
We will also use the notation
isa(x,basketball).
Basketball is a subset or subcategory of Ball, which is abbreviated
Basketball  Ball
We will also use the notation
ako(basketball,ball).
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Categories
and Objects
Physical Compositions
One object can be a part of another object.
Example, declaring direct parts
part(bucharest,romania).
part(romania,eastern_europe).
part(europe,earth).
We can make a transitive extension partof
part(Y,Z) and partof(X,Y) => partof(X,Z).
and reflexive (*)
partof(X,X).
Therefore we can conclude that partof (bucharest, earth)
(*) depending on definition
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Events
• Events are described as instances of event categories.
“The event E1 of Shankar flying from San Francisco to
Washington DC” is described as :
E1  Flyings  Flyer(E1,Shankar)  Origin(E1, SF)  Destinations(E1, DC).
• Process : Categories of event with these property
• Intervals: events that include as sub-events all events
occurring in a given time period (thus they are temporal
sections of the entire spatial universe).
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Events
• Places: spatial sections of the spatio-temporal universe
that extend through time.
• Location function: maps an object to the smallest place
that contains it:
x,l Location(x) = l  At(x, l)  ll At(x, ll)  In(l, ll)
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Events
Human Activity Detection
• Nevatia/Medioni/Cohen
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Events
Low-level processing
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CASE STUDY
Text Classification
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Text Classification
• Machine Learning is a broad are of AI concerned with
the design and development of algorithm that learn
patterns presents in data provided as input.
• The process of inserting the documents into the classes
i.e., of associating one or more class labels with each
document, is commonly referred to as text
classification.
Spam filtering
Another text classification task
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Sec. 13.1
Document Classification
“planning
language
proof
intelligence”
Test
Data:
(AI)
(Programming)
(HCI)
Classes:
ML
Training
Data:
Planning
Semantics
Garb.Coll.
learning
planning programming
intelligence temporal semantics
algorithm
reasoning language
reinforcement plan
proof...
network...
language...
Multimedia
garbage
...
collection
memory
optimization
region...
GUI
...
An algorithm is said to be unsupervised when no
information on training examples, i.e examples of
documents that belong to pre-specified classes, is
given as input.
Classification Methods:
Supervised learning
Given:
A document d
A fixed set of classes:
C = {c1, c2,…, cJ}
A training set D of documents each with a label
in C
Determine:
A learning method or algorithm which will
enable us to learn a classifier γ
For a test document d, we assign it the class
γ(d) ∈ C
Supervised Learning
• Supervised learning
– Naive Bayes (simple, common)
– k-Nearest Neighbors (simple, powerful)
– Support-vector machines (new, generally more powerful)
• Many commercial systems use a mixture of methods
Training
• Usually, the larger the number of training examples, the
better is the fine tuning of the classifier.
• If cannot be used to predict the classes, an event
commonly referred to as overfitting.
• To evaluate the classifier, we apply to a set of unseen
object – the test set.
Clustering
• The task of the classifier is to separate the documents
into groups or clusters – clustering
Text clustering: given a collection D of documents, a text
clustering method automatically separates these
documents into K clusters according to some predefined
criteria.
In K-means clustering, the number K of clusters to be
generated is provided as input.
Supervised Algorithms
• The training phase of a classifier
Rocchio Classification
 Rocchio forms a simple representative for each class:
the centroid/prototype
 Classification: nearest prototype/centroid
 It does not guarantee that classifications are consistent
with the given training data
SVM Classifier
Vector Machine (SVM) classifiers are relatively new
method of classification introduced by Vapnik and first
used in text classification by Joachims.
SVM Basic Technique
SVM Basic Technique
SVM Basic Technique
Determining Sentiments
• Sentiment analysis refers to a group of task that use
statistics and NLP to mine opininions to identify and
extract subjective information from texts
• Positive sentiments (brilliant, awesome, spectacular),
negative sentiments (awful, stupid and hideous)
• The movie review corpus includes 2000 movie reviews.
Sentiment analysis
(sentiment140.com)
• Go et al use classification methods includeing naïve
Bayes, MaExnt and SVM over the training and testing
dataset to perform sentiment classifications.
Summary
• Special-purpose representation systems, such as
semantic networks and description logics, have been
devised to help in organizing a hierarchy of categories.
Inheritance is an important form of inference, allowing
the properties of objects to be deduced from their
membership in categories.
• Non-monotonic Logics such as circumscription and
default logic, are intended to capture default
reasoning in general.
• Truth maintenance systems handle knowledge
updates and revisions efficiently
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References
• Stuart Russell, Peter Norvig,. 2010. Artificial intelligence : a
modern approach. PE. New Jersey. ISBN:9780132071482,
Chapter 12
• Elaine Rich, Kevin Knight, Shivashankar B. Nair. 2010. Artificial
Intelligence. MHE. New York. , Chapter 4 & 6
• Knowledge Representation and Reasoning Logics for Artificial
Intelligence:
http://www.cse.buffalo.edu/~shapiro/Courses/CSE563/Slides/k
rrSlides.pdf
• Knowledge Representation:
http://artint.info/html/ArtInt_8.html
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<< CLOSING >>
End of Session 11
Good Luck
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