Prof Ranjani hi

8/2/2019 Prof Ranjani hi

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1/25/2012

Knowledge Representation

Dr. Ranjani ParthasarathiProfessor

Dept. of Information Science & TechnologyCEG, Anna University, Chennai


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1/25/2012

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 !

1/25/2012 KR Workshop @ SRM Jan 2012


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1/25/2012

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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10

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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11

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



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12

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



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13

Semantic Net ExamplesShip

Hull Propulsion

Steamboat

HasAHasA

InstanceOf

giveBob

Candy

Mary

Person

agent recipient

object

InstanceInstance



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14

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.



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15

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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16

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



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17

Simple Frame ExampleSlot Name Filler

Restaurant Lemon GrassCuisine Thai, Vegetarian

Price ExpensiveService ExcellentAtmosphere Good

Location KoPWeb page Ask Google.



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18

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]



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19

Scripts

Describe typical events or sequences Components are

script variables (players, props) entry conditions transactions exit conditions

Create instance by filling in variables



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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



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CSC 9010: SpecialTopics, NaturalLanguage Processing.Spring, 2005.Matuszek &Papalaskari

21

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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22

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.



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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


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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42

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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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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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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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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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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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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Dr.T.V.Geetha, Anna

University 65

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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University 67

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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Dr.T.V.Geetha, Anna

University 68

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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Dr.T.V.Geetha, Anna

University 69

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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University 70

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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Dr.T.V.Geetha, Anna

University 71

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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Dr.T.V.Geetha, Anna

University 73

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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University 74

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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University 76

p y


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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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Dr.T.V.Geetha, Anna

University 78

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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University 80

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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University 81

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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University 82

-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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University 83

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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Dr.T.V.Geetha, Anna

University 85

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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Dr.T.V.Geetha, Anna

University 86

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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1/25/2012 KM Workshop AU, Dec2007

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


Another Conceptual


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Another Conceptual

Representation Framework



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Dr.T.V.Geetha, Anna

University 105

Tamil


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University 106


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University 107


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University 108


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University 110

Tamil examples for UNL graph andexpression


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Dr.T.V.Geetha, Anna

University 111

expression

UNL Expression


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Dr.T.V.Geetha, Anna

University 112

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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University 114


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University 115


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University 116


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University 117


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University 118


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Dr.T.V.Geetha, Anna

University 119

agt/obj


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Dr.T.V.Geetha, Anna University

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


Summary


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Summary

State of art on KR Details on Ontology Construction

Conceptual Inter-lingua framework



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!! Thank you !!

[email protected]

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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