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Knowledge Representation
Dr. Ranjani ParthasarathiProfessor
Dept. of Information Science & TechnologyCEG, Anna University, Chennai
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Knowledge Human
characteristic
Cogito Ergo Sum(I Think, Therefore I Am)
- Rene Descartes(1637)
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A storyThe following scenario illustrates a possible use of an opportunistic
network deployed after an earthquake. One of its helpers, asurveillance system, looks at a public area scene with many objects.The image is passed to another helper that analyzes it, and recognizesone of the objects as an overturned car. Another helper decides that thelicense plate number of the car should be obtained, and (maybeanother) image analysis helper provides this information. The platenumber is used by another helper to check in a vehicle databasewhether the car is equipped with the OnStar communication system.If it is, the appropriate OnStar center facility is contacted, becomes ahelper, and obtains a connection with the OnStar device in the car. TheOnStar device in the car becomes a helper and is asked to contactBANs (body area networks) on and within bodies of car occupants.Each BAN available in the car becomes a helper and reports on thevital signs of its owner. The reports from BANs are analyzed byprioritizing helpers that schedule the responder teams to ensure thatpeople in the most serious condition are rescued sooner than others.
With the exception of the BAN link that is just a bit futuristic (itswidespread availability could be measured in years not in decades), all
other helper capabilities are already quite common.
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A simple activity
Identify the knowledge embedded/ implicitfor the above scenario to work !
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What is Knowledge
Numerous definitions Working Knowledge: how organisations manage what they
know
Harvard Business School Press, 1998, 2000
Knowledge is information combined with experience,context, interpretation, and reflection. It is a high-valueform of information that is ready to apply to decisions andactions." T. Davenport et al., 1998
Knowledge is information evaluated and organized by thehuman mind so that it can be used purposefully, e.g.,conclusions or explanations." Rousa, 2002.
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What is Knowledge (2)
Knowledge is a fluid mix of framed experience values,contextual information , and expert insight that provides aframework for evaluating and incorporating new
experiences and information. It originates and is applied inthe minds of knowers.
In organizations, it often becomes embedded not only indocuments or repositories but also in organizationalroutines, processes, practices and norms . - ThomasH. Davenport, Laurence Prusak (1998)
"Knowledge is... a mental grasp of fact(s) of reality,reached either by perceptual observation or by a process of reason based on perceptual observation." Rand, 1967.
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Knowledge representation (KR)
Knowledge is a description of the world Describes a systems competence by what it
knows
Representation is the way it is encoded Defines a systems performance in doing
something
Different types of knowledge may requiredifferent types of representation Logic, Rules, Frames, Semantic Nets
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How KR Works
Intelligence requires knowledge Computational models of intelligence require
models of knowledge Use formalisms to write down knowledge
Expressive enough to capture human knowledge Precise enough to be understood by machines
Separate knowledge from computationalmechanisms that process it
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What goes into KR ?
How do we decide what we want to represent? Entities, categories, events, time, aspect Predicates, relationships among entities, arguments
(constants, variables) Andquantifiers, operators (e.g. temporal) Concepts, relations, attributes
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Early KR Schemes Model-based representations reflecting the
structure of the domain, and then reasonbased on the model. Semantic Nets Frames Scripts
Sometimes called associative networksCSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek &Papalaskari
Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04 , Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC-481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html
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Basics of Associative Networks
All include Concepts Various kinds of links between concepts
has - part or aggregation is -a or specialization More specialized depending on domain
Typically also include Inheritance Some kind of procedural attachment
Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04 , Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC-481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html
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Semantic Netslabeled, directed graphnodes represent objects, concepts, or situations
labels indicate the namenodes can be instances (individual objects) or classes(generic nodes)
links represent relationshipsthe relationships contain the structural information of the
knowledge to be representedthe label indicates the type of the relationship
Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04 , Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC-481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html
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Semantic Net ExamplesShip
Hull Propulsion
Steamboat
HasAHasA
InstanceOf
giveBob
Candy
Mary
Person
agent recipient
object
InstanceInstance
Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04 , Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC-481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html
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Generic / Individual Generic describes the idea-- the notion
static
Individual or instance describes a real entity must conform to notion of generic dynamic individuate or instantiate
A lot of NLP using semantic nets involves instantiatinggeneric nets based on a given piece of text.
Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04 , Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC-481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html
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Individuation examplegivePerson
Thing
Personagent recipient
object
giveBob
Candy
Maryagent recipient
object
Generic Representation Process the sentenceBob gave Mary some candy.
Instantiation Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04 , Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC-481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html
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FramesRepresent related knowledge about a subjectFrame has a title and a set of slots
Title is what the frame is the conceptSlots capture relationships of the concept to other things
Typically can be organized hierarchicallyMost frame systems have an is-a slotallows the use of inheritance
Slots can contain all kinds of itemsRules, facts, images, video, questions, hypotheses, other frames
In NLP, typically capture relationships to other frames or entitiesSlots can also have procedural attachments
on creation, modification, removal of the slot value
Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04 , Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC-481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html
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Simple Frame ExampleSlot Name Filler
Restaurant Lemon GrassCuisine Thai, Vegetarian
Price ExpensiveService ExcellentAtmosphere Good
Location KoPWeb page Ask Google.
Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04 , Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC-481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html
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Usage of Frames
Most operations with frames do one of twothings:
Fill slots Process a piece of text to identify an entity for
which we have a frame Fill as many slots as possible
Use contents of slots Look up answers to questions Generate new text
[Rogers 1999]
Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04 , Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC-481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html
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Scripts
Describe typical events or sequences Components are
script variables (players, props) entry conditions transactions exit conditions
Create instance by filling in variables
Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04 , Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC-481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html
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Restaurant Script Example
Generic template for restaurants different types
default values Script for a typical sequence of activities at
a restaurant
Often has a frame behind it; script isessentially instantiating the frame
Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04 , Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC-481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html
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CSC 9010: SpecialTopics, NaturalLanguage Processing.Spring, 2005.Matuszek &Papalaskari
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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-PersonsTime-of-Occurrence : (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 Scriptthen : Order-Food-Scriptthen : Eat-Food-Script unless (Long-Wait) when Exit-Restaurant-Angry
Scriptthen : if (Food-Quality was better than Palatable)
then Compliments-To-The-Chef Scriptthen : Pay-For-It-Scriptfinally : Leave-Restaurant Script
[Rogers 1999]
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Comments on Scripts
Obviously takes a lot of time to developthem initially. The script itself has much of the knowledge May be serious overkill for most NLP tasks
We need this level of detail if we want to
include answers based on reasoning likeMost restaurants do serve dinner.
Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04 , Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC-481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html
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First Order Predicate Calculus (FOPC)
Terms Constants: Lemon Grass Functions: LocationOf(Lemon Grass)
Variables: x in LocationOf(x) Predicates: Relations that hold among objects
Serves(Lemon Grass,VegetarianFood) Logical Connectives: Permit compositionality of
meaning I only have $5 and I dont have a lot of time Have(I,$5) Have(I,LotofTime)
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Enter - WWW
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Online Social NetworksBuddy Lists, AddressBooks
--
Google Scholar, BookSearch
CiteSeer, ProjectGutenberg
Community PortalsMessage Boards
BlogsPersonal Websites
Google Personalised,DumbFind
Altavista, Google
WikisContent ManagementSystems
Web 2.0 Web 1.0 Semantic Web
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Semantic Web / Web 3.0
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Beyond the Limits of Keyword
Search
Amount of data
P r o
d u c
t i v i
t y o f
S e a r c
h
Databases
2010 - 2020
Web 1.02000 - 2010
1990 - 2000
PC Era1980 - 1990
2020 - 2030
Web 3.0
Web 4.0
Web 2.0The World Wide Web
The DesktopKeyword search
Natural language search
Reasoning
Tagging
Semantic SearchThe
SemanticWeb
TheIntelligentWeb
Directories
The SocialWeb
Files & Folders
Content attributed to Nova Spivack, http://www.mindingtheplanet.net
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The Intelligence is in the
Connections
Connections between people
C o n n e c
t i o n s
b e t w e e n
I n f o r m a t
i o n
Email
Social Networking
Groupware
JavascriptWeblogs
Databases
File Systems
HTTPKeyword Search
USENET
Wikis
Websites
Directory Portals
2010 - 2020
Web 1.0
2000 - 2010
1990 - 2000
PC Era1980 - 1990
RSSWidgets
PCs
2020 - 2030
Office 2.0
XML
RDF
SPARQLAJAX
FTP IRC
SOAP
Mashups
File Servers
Social Media Sharing
Lightweight Collaboration
ATOM
Web 3.0
Web 4.0
Semantic SearchSemantic Databases
Distributed Search
Intelligent personal agents
JavaSaaS
Web 2.0Flash
OWL
HTML
SGML
SQLGopher
P2P
The Web
The PC
Windows
MacOS
SWRL
OpenID
BBS
MMOs
VR
Semantic Web
Intelligent Web
The Internet
Social Web
Web OS
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The Big Opportunity The social graph just connects people
People
Groups
The semantic graph connects everything
EmailsCompanies
Products
Services
Web Pages
Multimedia
Documents
Events
Projects
Activities
Interests
Places
Better search
More targeted ads
Smarter collaboration
Deeper integration
Richer content
Better personalization
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A Higher Resolution Web
ColdplayBand
Palo AltoCity
JanePerson
IBMCompany
DavePerson
BobPerson
DesignTeamGroup
StanfordAlumnaeGroup
IBM.comWeb Site
123.JPGPhotoDave.com
Weblog
SuePerson
JoePerson
Dave.comRSS Feed
Lives in
Publisher of
Friend of
Depiction of
Depiction of
Member of
Married to
Memberof
Member of
Member of
Fan of
Lives in
Subscriber to
Source of
Author of
Member of
Employee of
Fan of
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Five Approaches to Semantics
Tagging Statistics Linguistics Semantic Web Artificial Intelligence
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The Approaches Compared
Make the software smarter
Make the Data Smarter
Statistics
Linguistics
SemanticWeb
A.I.
Tagging
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Two Paths to Adding Semantics Bottom -Up (Classic)
Add semantic metadata to pages and databases all overthe Web
Every Website becomes semantic Everyone has to learn RDF/OWL
Top- Down (Contemporary) Automatically generate semantic metadata for vertical
domains Create services that provide this as an overlay to non-
semantic Web Nobody has to learn RDF/OWL
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In Practice: Hybrid ApproachWorks Best
Tagging
Semantic WebTop-down
StatisticsLinguisticsBottom-up
Artificial intelligence
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The Semantic Web is a Key
Enabler Moves the intelligence out of applications, into
the data
Data becomes self-describing; Meaning of databecomes part of the data
Apps can become smarter with less work, becausethe data carries knowledge about what it is andhow to use it
Data can be shared and linked more easily
S i W b hi h ?
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Web resources / services / DBs / etc.
Sharedontology
Web users(profiles,preferences)
Web accessdevices
Web agents / applications
External worldresources
Smartmachines
and devices
Industrial andbusiness processes
Semantic Web: which resources to annotate ?
Multimediaresources
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The Semantic Web = Open database layer for the Web
UserProfiles
WebContent
DataRecords
Apps &Services
Ads &Listings
Open Data Mappings
Open Data Records
Open Rules
Open Ontologies
Open Query Interfaces
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Semantic Web Open Standards
RDF (Resource Description Framework) Store data as triples
OWL (Web Ontology Language) Definesystems of concepts called ontologies
Sparql Query data in RDF SWRL Define rules
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Dr.T.V.Geetha, AnnaUniversity 38
Ontology
An ontology formally defines a common setof terms that are used to describe andrepresent a domain (e.g., librarianship,medicine, etc.)
Ontologies include computer-usabledefinitions of basic concepts in the domainand the relationships among them
Ontologies are usually expressed in a logic-based language
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RDF Triples
the subject, which is an RDF URI reference or a blank node
the predicate, which is an RDF URIreference
the object, which is an RDF URI reference ,a literal or a blank node
Source: http://www.w3.org/TR/rdf-concepts/#section-triples
Subject ObjectPredicate
http://www.w3.org/TR/rdf-concepts/http://www.w3.org/TR/rdf-concepts/http://www.w3.org/TR/rdf-concepts/http://www.w3.org/TR/rdf-concepts/http://www.w3.org/TR/rdf-concepts/http://www.w3.org/TR/rdf-concepts/http://www.w3.org/TR/rdf-concepts/http://www.w3.org/TR/rdf-concepts/http://www.w3.org/TR/rdf-concepts/http://www.w3.org/TR/rdf-concepts/http://www.w3.org/TR/rdf-concepts/http://www.w3.org/TR/rdf-concepts/http://www.w3.org/TR/rdf-concepts/http://www.w3.org/TR/rdf-concepts/ -
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Semantic Web Data is Self-Describing LinkedData
Data Record ID
Field 1 Value
Field 2 Value
Field 3 Value
Field 4 Value
Definition
Definition
Definition
Definition
Definition
Definition
Definition
Ontologies
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More on Ontology
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Aristotle - Ontology Before: study of the nature of being Since Aristotle: study of knowledge
representation and reasoning Terminology:
Genus: (Classes) Species: (Subclasses) Differentiae: (Characteristics which
allow to groupor distinguish objects from each
other)
Syllogisms (Inference Rules)[Aristotle] Science of Being,
Methapysics, IV, 1
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Dr.T.V.Geetha, AnnaUniversity 43
What is ontology?
Philosophy (400BC) : Systematic explanation of Existence
Neches (91): Ontology defines basic terms and relations comprising thevocabulary of a topic area as well as the rules for combining
terms and relations to define extensions to the vocabulary
Gruber (93): Explicit specification of a conceptualization
Borst (97): Formal specification of a shared conceptualization
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Studer(98): Formal, explicit specification of a shared conceptualization
Machinereadable
Concepts, properties,functions, axiomsare explicitly defined
Consensualknowledge
Abstract model of some phenomenain the world
What is ontology (2) ?
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Dr.T.V.Geetha, AnnaUniversity 45
What is Ontology (3) ?
Concepts: Units of thought: Classes andindividuals;
Protein, Gene, DNA, Hexokinase, glycolysis , Terms: Labels for concepts Protein, Gene, Relationships: Semantic links between concepts
Is-a-kind, is-a, part-of, name- of,
Taxonomy backbone of ontology
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Concept - Instance
Concept / Class / Universal (Metaphysics) an abstract or general idea inferred or derived
from specific instances
Instance / Individual / Particular (Metaphysics)
object in reality, a copy of an abstract conceptwith actual values for properties
Name: Thomas Wchter
Studied: Computer Science
LivesIn: Dresden
WorksAt: Biotec, TU-Dresden
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Developing an ontology Defining classes in the ontology
Concepts in a domain of discourse (classes -sometimes calledconcepts)
Arranging the classes in a taxonomic (subclass-superclass) hierarchy
Defining slots and describing allowed values for these slots Properties of each concept describing various features and
attributes of the concept (slots - sometimes called roles orproperties)
Restrictions on slots (facets - sometimes called role restrictions) Filling in the values for slots for instances
Ontology + set of individual instances of classes => knowledge
O t l gi l E gi i g d R l t d
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Dr.T.V.Geetha, AnnaUniversity 48
Ontological Engineering and RelatedDisciplines
Ontology
Formal Ontology Informal Ontology
Philosophy
Formal Semantics
Logic
Formal Methods Linguistics
Database Theory
Ontological Engineering
Object Modeling
Conceptual Modeling
Knowledge Engineering Software/Data Engineering
Knowledge Representation
Enterprise Engineering
Knowledge Management
Sociology
Industrial Engineering
Business Management
Artificial Intelligence
Mathematics
ComputerScience
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Benefits
Building an ontology is not a goal in itself.
Communication between peopleInteroperability between software agents
Reuse of domain knowledge
Make domain knowledge explicit
Analyze domain knowledge
f l
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Types of ontologies
[Guarino et al. 1999] - N. Guarino, C. Masolo, G. Vetere. OntoSeek: Content-BasedAccess to the Web. In: IEEE Intelligent Systems, 14(3), 70--80, 1999.
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Ontology -Examples
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Dr.T.V.Geetha, AnnaUniversity 5252
Ontology - Simple examples
Taxonomy fruit
pomme citron orange
fruit
apple lemon orange
fruit
apple citrus pear
lime lemon orange
fruit
tropical temperate
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Dr.T.V.Geetha, AnnaUniversity 53
Ontology- Example II
University Related Ontology
Person
Student Researcher
subClassOf subClassOf
Jeen
type
hasSuperVisordomain range
Frank
type
hasSuperVisor
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Ontology Example III
Living Thing
Grass
Animal
Plant
Tree
Body Part
Arm
Leg
Person
CowCarnivore
Herbivoreeats
eats
eats
has part
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Ontologies Example IV
OntologyF-Logic
similar
city
NeckarZugspitze
Geographical Entity (GE)
Natural GE Inhabited GE
countryrivermountain
instance_of
Germany
BerlinStuttgart
is-a
flow_through
located_in
capital_of
flow_through
flow_through
located_in
capital_of
367
length (km)
2962
height (m)
Design: Philipp Cimiano
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Ontology Example V
Representation Languages: RDF(S); OWL; Predicate Logic; F-
Logic
Object
Person DocumentTopic
Student LetterResearcher Emailis_similar_to
knows described_in
Doctoral StudentPhD Student
Tel
Affiliation
Affiliation
is_a -1
is_a -1
is_a -1
is_a -1
is_a -1is_a -1
instance_of -1
is_a -1
Ram
is_a -1
AIFB+49 721 608 .
T D T D
D T P T
described_in
is_about knows
is_about
P writes
RULES:
writes
related_to
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Chemical
AtomElementCompoundMolecule Ion
MetalNon-Metal
Metaloid
MolecularCompound
MolecularElement
IonicCompound
IonicMolecule
Ionic MolecularCompound
Ontology Example VI
E C
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EcoCyc
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Ontologies and their relatives
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Structured Ontology Spectrum
The term ontology has been used to describemodels with different degrees of structure(Ontology Spectrum)
Less structure: Taxonomies (Semiotaxonomies, Yahoo hierarchy, biologicaltaxonomy), Database Schemas (many) andmetadata schemes (ICML, ebXML, WSDL)
More Structure: Thesauri (WordNet, CALL,DTIC), Conceptual Models (OO models, UML)
Most Structure: Logical Theories (Ontolingua,
TOVE, CYC, Semantic Web)
O l S O Vi
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62weak semantics
strong semantics
Is Disjoint Subclass ofwith transitivityproperty
Modal Logic
Logical Theory
Thesaurus Has Narrower Meaning Than
Taxonomy Is Sub-Classification of
Conceptual Model Is Subclass of
DB Schemas, XML Schema
UML
First Order Logic
RelationalModel, XML
ER
Extended ER
Description LogicDAML+OIL, OWL
RDF/SXTM
Ontology Spectrum: One View
Syntactic Interoperability
Structural Interoperability
Semantic Interoperability
O t l S t O Vi
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Logical Theory
Thesaurus Has Narrower Meaning Than
Taxonomy Is Sub-Classification of
Conceptual Model Is Subclass of
Is Disjoint Subclass ofwith transitivityproperty
weak semantics
strong semantics
DB Schemas, XML Schema
UML
Modal LogicFirst Order Logic
RelationalModel, XML
ER
Extended ER
Description LogicDAML+OIL, OWL
RDF/SXTM
Ontology Spectrum: One View
Problem: Very GeneralSemantic Expressivity: Very High
Problem: LocalSemantic Expressivity: Low
Problem: GeneralSemantic Expressivity: Medium
Problem: LocalSemantic Expressivity: High
Syntactic Interoperability
Structural Interoperability
Semantic Interoperability
l l
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Ontology Applications Information Retrieval
Query Expansion Information Extraction
Template Definition, Semantic Integration Question Answering
Question Analysis, Answer Selection Knowledge Portal Construction
Knowledge Structure
Document Clustering/Classification Extend Bag-of-words
Knowledge Management Check Consistency, Infer New Knowledge
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Big Ontologies There are several large, general ontologies that
are freely available. Some examples are:
Cyc - Original general purpose ontology
OntoSem a lexical KR system and ontology WordNet - a large, on-line lexical reference system World Fact Book -- 5Meg of KIF sentences! UMLS - NLMs Unified Medical Language System SUMO Standard Upper Merged Ontology
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Building an Ontology
Ontology Elements
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Ontology Elements
Concepts(classes) + their hierarchy
Concept properties (slots/attributes)
Property restrictions (type, cardinality, domain)
Relations between concepts (disjoint, equality)
Instances
How to build an ontology?
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How to build an ontology?
Steps: determine domain and scope enumerate important terms define classes and class hierarchies define slots define slot restrictions (cardinality, value-type)
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Consider Reuse
With the spreading deployment of theSemantic Web, ontologies will become
more widely available We rarely have to start from scratch when
defining an ontology
There is almost always an ontology availablefrom a third party that provides at least a usefulstarting point for our own ontology
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Determine Scope (2)
Basic questions to be answered at this stageare: What is the domain that the ontology will
cover? For what are we going to use the
ontology?
For what types of questions should theontology provide answers ? Who will use and maintain the ontology?
Step 1: Determine Domain and Scope
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p p
Domain: geography
Application: route planning agent
Possible questions: Distance between two cities?What sort of connections exist between two cities?
In which country is a city?How many borders are crossed?
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Enumerate Terms
Write down in an unstructured list all the relevantterms that are expected to appear in the ontology
Nouns form the basis for class names Verbs (or verb phrases) form the basis for
property names Traditional knowledge engineering tools can be
used to obtain the set of terms an initial structure for these terms
Step 2: Enumerate Important Terms
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Step 2: Enumerate Important Terms
country
city capital
border
connection
Connection_on_land
Connection_in_air
Connection_on_water
road
railway
currency
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Identify properties
What properties do the terms have? What would you like to say about the terms? Initially get comprehensive list of terms do not
worry about overlap between concepts they represent Relations among terms Properties concepts have Whether concepts classes or slots
Closely integrated steps Developing Class hierarchy Defining properties of concepts (slots)
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Define Taxonomy
Relevant terms must be organized in ataxonomic hierarchy Opinions differ on whether it is more
efficient/reliable to do this in a top-down or a
bottom-up fashion Ensure that hierarchy is indeed a
taxonomy: If A is a subclass of B, then every instance of
A must also be an instance of B (compatiblewith semantics of rdfs:subClassOf)
Step 3: Define Classes and Class Hierarchy
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Define facets Define facets of Slots
Value type --- string, number, boolean,enumerated, instance type
Allowed values Number of values (cardinality) Single and multiple Minimum and maximum cardinality
Other features slot can take
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Define Properties
Often interleaved with the previous step The semantics of subClassOf demands that
whenever A is a subclass of B, everyproperty statement that holds for instancesof B must also apply to instances of A
It makes sense to attach properties to thehighest class in the hierarchy to whichthey apply
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Define Properties (2)
While attaching properties to classes, itmakes sense to immediately provide
statements about the domain and range of these properties There is a methodological tension here
between generality and specificity: Flexibility (inheritance to subclasses) Detection of inconsistencies and
misconceptions
Step 4: Define Slots of Classes
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Step 5: Define slot constraints Slot-cardinality
Ex: Borders_with multiple , Start_point single
Slot-value typeEx: Borders_with- Country
Geographic_entity
Country CityHas_capital
Capital_of Borders_with
ConnectionStart_point
End_point
Capital_city
Issues on class hierarchy
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y
- all is-a relations hold?Inst(B) Inst(A)B
A
C
D- check transitivityC Subclass_of(A)
D Subclass_of(C) D Subclass_of(A)
- avoid unexpected cyclesB Subclass_of(A)
A Subclass_of(B) A=B
Issues on Slots
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-transitive slotsA.connection(B)B.connection(C) A.connection(C)
-symmetric slots
Ex. A borders_with B B borders_with A
- inverse slots (redundant, but explicit)
CountryHas_capital
Capital_of
Capital_city
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Define Instances
Filling the ontologies with instances is aseparate step
Number of instances >> number of classes Thus populating an ontology with instances
is not done manually
Retrieved from legacy data sources (DBs) Extracted automatically from a text corpus
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Languages for Ontology
Languages for Ontology
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Wide variety of languages for Explicit Specification Graphical notations
Semantic networks Topic Maps (see http://www.topicmaps.org/) UML RDF
Logic based Description Logics (e.g., OIL, DAML+OIL, OWL) Rules (e.g., RuleML, LP/Prolog) First Order Logic (e.g., KIF) Conceptual graphs (Syntactically) higher order logics (e.g., LBase) Non-classical logics (e.g., Flogic, Non-Mon, modalities)
Probabilistic/fuzzy Degree of formality varies widely
Increased formality makes languages more amenable to machineprocessing (e.g., automated reasoning)
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Web Schema Languages
Existing Web languages extended to facilitate content description XML XML Schema ( XMLS ) RDF RDF Schema ( RDFS )
XMLS not an ontology language Changes format of DTDs (document schemas) to be XML Adds an extensible type hierarchy
Integers, Strings, etc. Can define sub-types, e.g., positive integers
RDFS is recognisable as an ontology language Classes and properties Sub/super-classes (and properties) Range and domain (of properties)
d l d l
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A traditional Indian logic &
ontology
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Nyaaya (Tarka)
Ontology Structure of knowledge
A systematic account of Existence Defines the terms used to describe andrepresent knowledge
Allows detailed, accurate, precise, consistent,
sound, and meaningful distinctions among theclasses, properties, and relations. Inference
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Nyaaya Ontology - Classification
Seven categories Substance (dravya) - 9
Quality (guna) - 24 Action (karma) - 5 Universal/Commonality (saamaanya) Particularity (visesha) Inherence (samavaaya) Non-existence (abhaava) - 4
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Definitive Qualities of substances
Earth smell as its inherent quality Also has color, taste, touch, sound etc.
Water cold touch as its inherent quality Also has color (according to nyaaya white), taste,sound
Fire hot to touch
Also has color, sound Air colorless but has touch
Necessity to use both terms for the definition
Space (ether) sound as its inherent quality
D fi i i Q li i f b (2)
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Definitive Qualities of substances (2)
Time & direction All pervading, substratefor everything
Mind Emotions (pleasure, pain etc) That which has action, but cannot be touched
Soul Knowledge (gnaanam) is thedefinitive quality
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Gnaanam cognition/knowledge
The quality which is the cause of all kindsof transactions (communication)
Two types Remembrance (smrti)
Born of mental impressions
Apprehension (anubhava) True & Untrue ( conforming to the object or not)( valid / invalid )
G
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Gnaanam -
cognition/knowledge( 2) Valid apprehension (4 types) Perception (pratyaksha)
Inference (anumaana) Analogy based (upamaana) Verbal testimony (shabda)
2 more types (in other schools of thought) Implication (arthaapatti) Non-apprehension (anupalabdhi)
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Perception
Laukika (ordinary) vs Alaukika (extraordinary) External
Visual Intuitive Tactual Gustatory Olfactory
Auditory Internal Mind
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Cause for Perception Six operative causes
Conjunction in perceiving the jar Inherence with the conjoint in perceiving the color
of the jar Inherent union with the inherent which is conjoint inperceiving the genus colorness
Inherence in perceiving sound
Inherence with the inherently united - in perceivingsoundness
Relation of attribute with the subject in perceivingabsence of jar on the surface
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Inference
Extract the hidden essence from observablefacts
Identify the implied Cause and effect Deduction and Induction
Universal to Particular Vs Particular to General
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Inference Nyaaya View
There is smoke in the mountain. Smoke is always pervaded by fire. We've
seen this in case of hearth, kerosene stoveetc.
So, if there is smoke somewhere, there
should also be fire there. Hence, there is fire in the mountain.
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Inference the parts
Fire" is that which is to be proved. The reason for our conclusion of the
presence of fire is "smoke". There is an indisputable associationbetween smoke and fire i.e, wherever thereis smoke, there is fire.
Mountain is the current place where theabove association exists.
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Inference the parts (2)
Paksha - The subject or the receptacle on whichwe formulate our deductions.
Hetu - Reason existing in the paksha Saadhya - The thing we are trying to prove. Vyaapti - Vyaapti is the quality of Saadhya being
certainly co-existent with hetu in the samesubject. This forms the central part of inference
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Inference - Formalism
Five part syllogism Prathigna (Statement) the mountain has fire Hetu (reason) - because there is smoke Vyaapti (invariable concomitance)
Smoke is always pervaded by fire
Udaharana (example/illustration) As in a hearth / kerosene stove
Nigamana (conclusion) Hence there is fire in the mountain
Vyaapti
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Vyaapti
Invariable concomitance 3 types Anvaya vyaapti
A implies B Vyatireka vyaapti
Not B implies Not A
Anvaya-vyatireka vyapti Both
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Verbal Testimony
Shabda (words) Vakya (sentences)
Valid sentences Akaanksha (mutual expectation) Sannidhi (proximity)
Yogyata (fitness / aptness/ no-nonsense)
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KRIL
Knowledge representation based on IndianLogic
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Another Conceptual
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Another Conceptual
Representation Framework
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Tamil
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Tamil examples for UNL graph andexpression
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expression
UNL Expression
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UNL Expression[d]
[s][w]
;tirunelveli;icl>place;1
;nellaiappar temple;iof>temple;2
;gandimathi amman temple;iof>temple;3
;krishnapuram temple;iof>temple;4
;sree vaikundam temple;iof>temple;5;necessity;icl>attribute;6
;go;icl>do;7
;pure;aoj>thing;8
;place;icl>thing;9
[/w]
[r]
0 plc 1 c1
0 man 6 c2
0 pur 7 c3
9 mod 8 c4 [/r] [/s]
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agt/obj
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frm/tmt/aoj
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agt/cag/to
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plc
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pos
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plc/and
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int
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plf/plt/via/agt
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nam
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iof
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UNL b sed pps
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UNL based apps
Machine translation Conceptual Cross-Lingual IR
1/25/2012 KR Workshop @ SRM Jan 2012
Summary
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Summary
State of art on KR Details on Ontology Construction
Conceptual Inter-lingua framework
1/25/2012 KR Workshop @ SRM Jan 2012
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!! Thank you !!
Substance (dravya)
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Substance (dravya)
Substratum of qualities Nine substances
Earth (smell) Water (cold touch) Fire (warm touch)
Air (felt but not seen) Ether/Space (sound)
-Time
-Direction
-Soul (Knowledge)
-Mind (emotions)
Back
Quality (guna)
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rupa (color) rasa (taste) gandha(smell) sparsha (touch) sankhya (no.) parimaNa(magnitude) Prthaktva(separateness) samyoga (conjunction) Vibhaga (disjunction) paratva (remoteness) Aparatva (proximity) gurutva (heaviness) Dravatva (fluidity) sneha (stickiness) shabda (sound) Budhi (cognition) sukha (pleasure) dukha (sorrow)
Iccha (desire) dvesa (dislike) praytna (effort) dharma (merit) Adharma (demerit) samskara(tendency)
Back
Action (karma)
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Action (karma)
Upward Downward
Contraction Expansion Motion (lateral)
Back
Non existence / Negation
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Non-existence / Negation
Pragabhaava (prior / antecedent negation) Before creation
Pradhvamsaabhaava (post / destructivenegation) After destruction
Anyonyaabhaava (mutual negation) Atyantaabhaava (absolute negation)
Back
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