The document provides an overview of ontology and its various aspects. It discusses the origin of the term ontology, which derives from Greek words meaning "being" and "science," so ontology is the study of being. It distinguishes between scientific and philosophical ontologies. Social ontology examines social entities. Perspectives on ontology include philosophy, library and information science, artificial intelligence, linguistics, and the semantic web. The goal of ontology is to encode knowledge to make it understandable to both people and machines. It provides motivations for developing ontologies such as enabling information integration and knowledge management. The document also discusses ontology languages, uniqueness of ontologies, purposes of ontologies, and provides references.

1. ONTOLOGY AND ITS VARIOUS ASPECTS
COLLOQUIUM 3
Date : 26.03.2014.
CONDUCTED BY
STUDENTS OF MSLIS,
SECOND SEMESTER,
SESSION 2013-2015,
DRTC, ISIBC.

2. CO-ORDINATOR: Dr. Biswanath Dutta,
Assistant Professor,
DRTC, ISIBC.

3. Introduction
Samhati Soor (MLIB 08)

4. ORIGIN
● The term ontology derives from Greek words “ontologos”.
“onto” means “being”.
“logos” means “science”.
● So, ONTOLOGY is the SCIENCE OR STUDY OF BEING.
● Ontology aggregates to the
study of anything and
everything.
● For everything, it is a part of
being.
● For anything, it is under the
aspect of its being, of
what is involved in its
existing.

5. SCIENTIFIC vs PHILOSOPHICAL
● reasonably
distinguished
● easily extended to include
significant objects of
extra-scientific thinking
● synonymous with
metaphysics
● focuses on the categories
of being, whether things
can be said to exist or not.

6. SOCIAL ONTOLOGY
● The study of social entities or social things
● Now, We can say that social ontology has two
divisions:
1) Social scientific ontology
2) Social philosophic ontology
“SOCIAL ONTOLOGY :
recasting Political Philosophy Through
a Phenomenology of Whoness”
MICHAEL ELDERD

7. PERSPECTIVE
● Philosophy
● Library and
Information Science
● Artificial Intelligence
● Linguistics
● Natural Language
Processing
● The Semantic Web

8. GOAL
● Encoding knowledge to make it understandable
● Common vocabulary
● to facilitate agent interaction on the Web

9. MOTIVATION
● Inability to use the abundant information resources on the web
● Difficulty in Information Integration
● Problem in Knowledge Management
“People as well as machines can‘t share knowledge if they
do not speak a common language”
[T. Davenport]
●
Ontologies provide the required conceptualizations
and knowledge representation to meet these
challenges.

10. ONTOLOGY LANGUAGES
at a glance....
● The WWW Consortium (W3C) is developing the RDF, a
language for encoding knowledge on Web pages to make it
understandable to electronic agents searching for information.
● The Defense Advanced Research Projects Agency (DARPA), in
conjunction with the W3C, is developing DARPA Agent
Markup Language.
● Language (DAML) by extending RDF with more expressive
constructs aimed at facilitating agent interaction on the Web.
● Ontology adds higher semantic value for a better solution on
the web thrugh Web Ontology Language (OWL).

11. UNIQUENESS
● Defining things unambiguously
● Interoperability
● Can expand/narrow search terms
● Enable “Activity-based” search
● Can validate taxonomy membership
● Can be distributed and aggregated
● Mapping to DBMS, OOP and UML
modeling
● Ontologies + Rules = Inference
● Mature concepts

12. PURPOSE
● Sharing common understanding of information among people or agents
● Reusing of domain knowledge
● Making domain assumptions explicit
● Separating domain knowledge from operational knowledge
● Analysing domain knowledge

13. References
● A Conception of Ontology1 Tony Lawson Faculty of Economics Sidgwick Avenue
Cambridge CB3 9DD E-mail: Tony.Lawson@econ.cam/.ac.uk(Version: December 06
2004)
● What Is an Ontology ?Nicola Guarino1 , Daniel Oberle2 , and Steffen Staab3ITSC-
CNR, Laboratory for Applied Ontology, 38100 Trento, Italy,nicola.guarino@cnr.itSAP
Research, CEC Karlsruhe, 76131 Karlsruhe, Germany,d.oberle@sap.comUniversity
of Koblenz-Landau, ISWeb, 56016 Koblenz, Germany,staab@uni-koblenz.de
● Understanding ontology evolution: A change detection approachPeter Plessers ,∗
Olga De Troyer, Sven CasteleynVrije Universiteit Brussel, Pleinlaan 2, 1050
Brussels, Belgium
● Ontology Evolution: Not the Same as SchemaEvolutionNatalya F. Noy1 , Michel
Klein2StanfordVrijeMedical Informatics, Stanford University, Stanford, CA,
USAUniversity Amsterdam, Amsterdam, The Netherlands
● The Ontology of Concepts—Abstract Objects or Mental Representations? 1ERIC
MARGOLISUniversity of Wisconsin—MadisonSTEPHEN LAURENCEUniversity of
Sheffield
● Characterising concept’s properties in ontologiesValentina A.M. Tamma and Trevor
J.M. Bench-CaponDepartment of Computer Science, The University of
LiverpoolLiverpool L69 7ZF, United Kingdomemail: {valli,tbc}@csc.liv.ac.uk

14. Sub-topics Speakers
Ontology Evolution DIBAKAR SEN
Ontology Classification JAYANTA KR. NAYEK,
MANASA RATH
Ontology Development
Methodology
TANMAY MONDAL
Ontology Development
Tools
SANDIP DAS
Ontology Evaluation
Techniques
ANWESHA BHATTACHARYA
Ontology Applications MANASH KUMAR
Ontology Mapping and
Alignment
SHIV SHAKTI GHOSH
Projects and Conclusion MOHIT GARG

15. Ontology Evolution
Dibakar Sen (MLIB 02)

16. Lexicon – It gives all the possible formspossible forms of a wordword (singular,
plural, part of speech etc.). It tells how to use the
words.
Glossary – It covers only the words and its definitiondefinition relating
to a particular subject.particular subject.
Dictionary – A kind of reference books that provide an
alphabetical list of words with their meaning,meaning,
pronunciation, phonetic symbols etc.pronunciation, phonetic symbols etc.
Terminologies

17. Controlled vocabulary - A carefully selected list of words
and phrases, which are used to tag units of information
(document or work) so that they may be more easily retrieved
by a search.
Taxonomy – HierarchicalHierarchical list of terms. Parent- child
relationship among the terms (genus – species).

18. It provides associative relationshipassociative relationship in addition with parent-child
relationship.
BT - wider meaning (Broader Term)
NT - more specific term(Narrower Term)
RT - not broader or narrower
SN - explaining its meaning within the thesaurus
ex.- Communication
Use for general materials on communication in
its broadest sense, including the use of the spoken and
written word, sign, symbol or behaviour.
Thesauri

19. Taxonomy Thesaurus
Controlled vocabulary Collection of Network collection of
Relationship Hierarchical relation Hierarchical +
associative
Provides Genus and
Specis relationship
Provides BT NT and RT
Taxonomy vs Thesaurus

20. What is an ontology?
A formal, explicit specification of a shared
conceptualization. (Studer 1998, original definition by
Gruber in 1993)
Formal: it is machine-readable.
Explicit specification: it explicitly defines concepts,
relations, attributes and constraints.
Shared: it is accepted by a group.
Conceptualization: an abstract model of a phenomenon.

21. Taxonomy Thesaurus Ontology
Controlled
vocabulary
Collection of Network collection of Network colection of
Relationship Hierarchical
relation
Hierarchical +
associative
Hierarchical +
associative, and also
denotes how things are
related
Provides
Genus and
Specis
relationship
Provides BT NT and RT BT - Part of
NT - Instance of
RT – Related to
Comparison between Taxonomy,
Thesaurus and Ontology

22. Evolution Process
4. Add
Complex
Relation-
ship
4. Add
Complex
Relation-
ship

23. Taxonomy
Mechanical Device
Pump Engine
Hydrulic Pump Fuel Pump
Aircraft Engine Driven Pump
Jet Engine

24. Thesaurus
Mechanical Device
Pump Engine
Hydrulic Pump Fuel Pump
Aircraft Engine Driven Pump
Jet Engine
Pumping
Hydrulic System
Fuel Filter
= Boarder / NarrowerTerm = Related Term

25. Ontology
Mechanical Device
Pump Engine
Hydrulic Pump Fuel Pump
Aircraft Engine Driven Pump
Jet Engine
Pumping
Hydrulic System
Fuel Filter
= Boarder / narrowerTerm = Related Term
Supplies fuel to
Connected to
Done by
= How related
Used in

26. References
http://www.ontopia.net/topicmaps/materials/tm-vs-
thesauri.html#N412
http://www.coherencegroup.com/whatralphknows/2003/03/what-are-the-
differences-betwe-1.html
http://infogrid.org/trac/wiki/Reference/Pidcock
Article
http://protege.stanford.edu/conference/2006/submissions/slides/1.2_Us
chold.pdf

27. Ontology Classification
(Part-I)
Jayanta Kr. Nayek (MLIB 03)

28. Ontology Classifications
According to the -------
• expressivity and formality of the
languages used: natural language,
formal language, etc.
• scope of the objects described by
the ontology.

30. Level of Formality : Example

31. Ontology classification based on domain scope
Top-level
or Foundational Ontology
Application or Local
Ontology
General Ontology
Core Reference
Ontology
Task OntologyDomain
Ontology
Catherine Roussey, Francois Pinet, Myoung Ah Kang, and Oscar
Corcho.
Source: An Introduction to Ontologies and Ontology Engineering by
Specialize

32. Local Ontologies/Application Ontologies
• Local or application ontologies are
specializations of domain ontologies
where there could be no consensus or
knowledge sharing.
• This type of ontology represents the
particular model of a domain according
to a single viewpoint of a user or a
developer.

33. Domain Ontologies
• Domain ontology is only applicable to a
domain with a specific view point. That is
to say that this viewpoint defines how a
group of users conceptualize and
visualize some specific phenomenon.

34. Core Reference Ontologies
• Core reference ontology is a standard
used by different group of users. This
type of ontology is linked to a domain but
it integrates different viewpoints related
to specific group of users.
• This type of ontology is the result of the
integration of several domain ontologies.
A core reference ontology is often built to
catch the central concepts and relations
of the domain.

35. General Ontologies
• General ontologies are not dedicated to a
specific domain or fields. They contain
general knowledge of a huge area.

36. Foundational Ontologies/Top Level
Ontologies/Upper Level Ontologies
• Foundational or top level ontologies are generic
ontologies applicable to various domains. They
define basic notions like objects, relations,
events, processes and so on.
• Domain or core reference ontologies based on the
same foundational ontology can be more easily
integrated.
• For example, Fonseca et al. (2006) describes a top
level ontology of geographic objects and a
similarity measure to evaluate the interoperability
of domain ontologies based on this top level
ontology.

37. Complexity of Ontologies
Depending on the wide range of tasks to which the ontologies are put
ontologies can vary in their complexity Ontologies range from simple
taxonomies to highly tangled networks including constraints associated
with concepts and relations.
Light-weight Ontology
• concepts
• ‘ is-a’ hierarchy among concepts
• relations between concepts
Middle-weight Ontology
●
Some formal notations
●
but only a modest amount of logic and reasoning
Heavy-weight Ontology
• cardinality constraints
• taxonomy of relations
• Axioms (restrictions)

38. Examples
Local/Ap
plication
Ontology
Domain
Ontology
Core
Reference
Ontology
General
Ontolog
y
Fundamental/Top
level/Upper level
Ontology
USGS
Hydrologi
c Units
Ontology
Urban
Sprawl
Ontology,
ODGIS,
GENE
Ontology
Hydro
ontology,
City GML,
CIDO, C-
CRM and
CORA etc.
Open Cyc
Ontology
Descriptive
Ontology for
Linguistic and
Cognitive
Engineering
(DOLCE),
Basic Formal
Ontology(BFO)
Socio Cultural
Ontology
Geography Markup
Language (GML)
etc.

39. References
• Ontology based document annotation: trends and open research
problems by Oscar Corcho, Int. J. Metadata, Semantics and
Ontologies, Vol. 1, No. 1, 2006.
• An Introduction to Ontologies and Ontology Engineering by
Catherine Roussey, Francois Pinet, Myoung Ah Kang, and Oscar
Corcho.
• Handbook on Ontologies by Steffen Staab and Rudi Studer
(Eds.),2nd ed, Springer.
• Semantic Matching: Formal Ontological Distinctions for
Information Organization, Extraction, and Integration, Nicola
Guarino, Summer School on Information Extraction, Frascati, July
14-19, Frascati, Italy, published by Springer Verlag.

40. Ontology Classification
(Part-II)
Manasa Rath (MLIB 04)

41. Top Level Ontology: Cyc
artificial intelligence project that attempts to assemble a
comprehensive ontology and knowledge base of
everyday common sense knowledge
project was started in 1984 by Douglas Lenat at MCC and is
developed by the Cycorp company under an open source license

42. THING
INDIVIDUAL
OBJECT INTANGIBLE REPRESENTED
THING
STUFF INTANGIBLE
OBJECT COLLECTION
EVENT
INTANGIBLE
STUFF
RELATIONSHIP
Top level categories in Cyc

43. Cyc Representation
Collections-A member of a collection is called
an instance of that collection
Truth Functions which can be applied to one or more
other concepts and return either true or false
Functions, which produce new terms from given
ones.Eg:#$FruitFn, when provided with an argument
describing a type (or collection) of plants, will return the
collection of its fruits
Constants start with an optional "#$" and are case-
sensitive. There are constants for individual items known
as individuals, such as #$BillClinton or #$France

44. Predicates are written before their arguments, in parentheses:
(#$isa #$BillClinton #$UnitedStatesPresident
- An inference engine is a computer program that tries to
derive answers from a knowledge base. The Cyc inference
engine performs general deduction

46. Applications of Cyc:
Cleveland Clinic, Ohio,US

47. Domain Ontology: Gene Ontology (GO)
-major bioinformatics initiative to unify the representation
of gene and gene product attributes across all species
AIMS to:
 maintain and develop its controlled vocabulary of gene and gene
product attributes
 annotate genes and gene products, and assimilate and disseminate
annotation data
 provide tools for easy access to all aspects of the data provided by the
project, and to enable functional interpretation of experimental data
using the GO, for example via enrichment analysis

48. GO : Project
The Gene Ontology project provides an ontology of defined terms
representing gene product properties. The ontology covers three
domains:
cellular component, the parts of a cell or
its extracellular environment;
molecular function, the elemental activities of a gene product
at the molecular level, such as binding or catalysis;
biological process, operations or sets of molecular events with
a defined beginning and end, pertinent to the functioning of
integrated living units: cells, tissues, organs, and organisms
ACYCLIC GRAPH

49. Structure of Gene Ontology
CELLULAR
COMPONENT
BIOLOGICAL
COMPONENT
MOLECULAR
COMPONENT
PIGMENT
METABOLIC
PROCESS
PIGMENTATION
PIGMENTATION
DURING
DEVELOPMENT
POSITIVE
REGULATION
DURING
BIOLOGICAL
PROCESS
NEGATIVE
REGULATION
DURING
BIOLOGICAL
PROCESS
PIGMENTATION
REGULATION OF
BIOLOGICAL
PROCESS
Source: www.geneontology.org

50. EXAMPLE of a GO
term:

51. Annotation
GO annotations have the following data:
The reference used to make the annotation (e.g. a journal
article)
An evidence code denoting the type of evidence upon which
the annotation is based
The date and the creator of the annotation

53. References
1.http://www.geneontology.org/
2.http://www.cyc.com/
3.http://www.cyc.com/platform/opencyc
4.Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.
Ashburner M1, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight
SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE,
Ringwald M, Rubin GM, Sherlock G
.
5.http://supfam.cs.bris.ac.uk/SUPERFAMILY/GO.html
6.Diehl AD, Lee JA, Scheuermann RH, Blake JA (April 2007). "Ontology development for
biological systems: immunology". Bioinformatics 23 (7): 913–5.
doi:10.1093/bioinformatics/btm029. PMID 17267433.
7.The GO Consortium (2009-03-16). "gene_ontology.1_2.obo" (OBO 1.2 flat file). Retrieved 2009-
03-16.
8. The GO Consortium (2009-03-16). "AmiGO: P68032 Associations". Retrieved 2009-03-16
9.http://mba.eci.ufmg.br/downloads/toplev.pdf
10.John F. Sowa. ‘Top-level Categories’, http://users.bestweb.net/~sowa/ontolo
gy/toplevel.htm (August 8, 2006)

54. Ontology Development
Methodology
Tanmay Mondal (MLIB 10)

55. Methodology
When a new ontology is going to be built, several basic questions
arise related to the methodologies, tools and languages to be used
in its development process.
“Methodology can properly refer to the theoretical analysis of the
methods appropriate to a field of study or to the body of methods
and principles particular to a branch of knowledge.”
Thefreedictionary.com
The science of method or orderly arrangement and classification.

56. 
Construction of ontologies is very much an art
rather than a science .

Good methodology for developing ontologies .

Quality of the ontologies .
Why?

57. Cyc -
This oldest of knowledge bases and ontologies has been
mapped to many separate ontologies.Manual codification of
common sense knowledge extracted by hand,machine learning
tools for new knowledge acquisition
TOVE (Toronto Virtual Enterprise) –
A first-order logic approach to representing activities, states,
time, resources, and cost in an enterprise integration architecture
IDEF5 (Integrated Definition for Ontology Description Capture
Method) -
It is part of a broader set of methodologies developed by
Knowledge Based Systems, Inc.
Different Methodologies

58. METHONTOLOGY -
Framework enables the construction of
ontologies at the knowledge level and includes the
identification of the ontology development process.
OTK (On-To-Knowledge) -
It was a methodology that came from the major EU
effort at the beginning of last decade; it is a common sense
approach reflected in many ways in other Methodologies
UPON (United Process for ONtologies) -
It is a UML-based approach that is based on use cases,
and is incremental and iterative.

59. ONIONS (ONtologic Integration Of Naive Sources) -
A set of methods especially geared to integrating multiple
information sources, with a particular emphasis on domain
ontologies
COINS (COntext INterchange System) -
A long-running series of efforts from MIT's Sloan Schoo
ofManagement
SENSUS-
Using this ,air campaign plans,strategy development
Asistant is built.
DERA- is a faceted knowledge organization methodology.
YAMO-Yet another Methodology for Ontology

60. METHONTOLOGY
► Laboratory of Artificial Intelligence at the Polytechnic
University of Madrid.
►A life cycle based on evolving prototypes, and particular
techniques for carrying out each activity.
► WebODEand ODE provide support to METHONTOLOGY

61. Management Activities- Plan, control,
Quality
Development-oriented-
Specification,Coceptualization,Formalization,
implementation
Suppport Activites-
Knowledgeacquisition,Evaluation,integration,Do
cumentation
Ontology Life Cycle-How the stages are
related.
Development Process of
Methontology

63. Developed Ontology

CHEMICALS

ENVIRONMNT POLLUTANTS

REFERENCE

(KA)^2

64. Applications Using Ontologies Developed
With This Methodology
(Onto)^2Agent : Reference Ontology as a source of its
knowledge and retrieves descriptions of ontologies
that satisfy a given set of constraints.
Chemical OntoAgent : It students to learn chemistry
and to test their skills on this domain
Ontogeneration :It is a system that uses a domain
ontology (CHEMICALS) and a linguistic ontology to
generate Spanish text descriptions in response to
the queries of students in the domain of chemistry.

65. In DERA,
●
Organization of knowledge into a number
of facets by defining any number of domains.
●
A domain consists of three elementary
components namely entity, relation and
attribute. D=<E, R,A>
●
Around 377 Domains, out of which 115 are in
priority for development, more Than 150,000
terms (encoding concepts, relations and
attributes)codified using the terms codified in the
DERA facets.

66. Development Process of DERA
– Step 1: Identification of the atomic concepts
– Step 2: Analysis
– Step 3: Synthesis
– Step 4: Standardization
– Step 5: Ordering
●
Following the above steps leads to the creation
of a set of facets

67. Faceted ontology is an ontology in which
concepts are organized into facets
GeoWordNet -Is anexample of a faceted
ontology consists of facets such as body of
water ,geological formation and administrative
division Space ontology

68. References
1. Overview Of Methodologies For Building Ontologies
Fernández López, M.
Boadilla del Monte, 28660. Madrid, Spain.
2. SERIES Concluding Workshop -
Joint with US-NEES
JRC, Ispra, May 28-30, 2013
3. Ontology Development Methods
Pratibha Gokhale,* Sangeeta Deokattey** and K.
Bhanumurthy**
* University of Mumbai, Kalina, Vidyanagari, Santacruz (East),
Mumbai-400 098

69. Ontology Development Tools
Sandip Das (MLIB 07)

70. Why Tools????
An ontology can be expressed in
structured languages.
Maintaining the structure is
difficult.
Tools for building ontologies
attempts to simplify the task.

71. Tools for Ontology Construction

72. Open Source Commercial Free
Protégé
http://protege.stanford.edu/
TopBraid Composer
http://www.topquadrant.com/tools/IDE-topbraid-
composer-maestro-edition/
Knoodl
http://www.knoodl.com/ui/home.html;jsessionid=E1EAD
42A4DC8BAA6F3E2FE747608012D
KAON
http://sourceforge.net/projects/kaon/
a.k.a. software
http://www.a-k-a.com.au//?page_id=43
Fluent Editor 2
http://www.cognitum.eu/semantics/FluentEditor2/Defaul
t.aspx
Neologism
http://neologism.deri.ie/
Onto Studio
http://www.ontoprise.de/en/products/ontostudio/
Java Ontology
Editor
http://cit.cse.sc.edu/demos/java/joe/joeBeta-jar.html
Swoop
http://www.mindswap.org/2004/SWOOP
OWL GrEd
http://owlgred.lumii.lv/
KMgen
http://www.algo.be/ref-projects.htm#KMgen
NeOn Toolkit
http://neon-toolkit.org/wiki/Main_Page
PoolParty
Thesaurus
http://www.poolparty.biz/
Model Future IDEAS
Addln
http://www.modelfutures.com/software/
EMFText OWL2
Manchester
http://www.emftext.org/index.php/EMFText_Concrete_Sy
ntax_Zoo_OWL2_Manchester
Anzo express
http://www.cambridgesemantics.com/products/anzo-
express
Model Future OWL
Editor
http://www.modelfutures.com/owl/
OBO-Edit
http://geneontology.sourceforge.net/
Be Informed Suite
http://www.beinformed.com/BeInformed/website/en?
init=true&tempregion=US
Myweb
http://www.ontologyonline.org/main.html
TODE
http://en.wikipedia.org/wiki/Tool_for_Ontology_Develop
ment_and_Editing_(TODE)
Semaphore
Ontology Manager
http://www.smartlogic.com/
Semantic Turkey
http://semanticturkey.uniroma2.it/
TwoUse Toolkit
https://code.google.com/p/twouse/
Enterprise architect
http://www.sparxsystems.com/
Synaptica
http://www.synaptica.com/

73. Open source software.
Developed by Standford Center for
Biomedical Informatics Research.

74. Protégé Features
Support the creation, visualization and manipulation
of ontologies in various representation formats
Provides friendly possessing
Import languages are XML , RDFS , OWL and BioPortal
Export languages are XML, RDFS, OWL, SWRL-IQ and
MetaAnalysis
Allow graphical interface
Allow viewing and zooming
It store ontology on file and DBMS.

75. Figure1 : Protégé Screenshot

76. Figure2 : Protégé screenshot

77. • Developed by TopQuadrant Co.
• Comes in three editions : FE , SE , ME

78. TopBraid Composer Features
●It is based on the Eclipse platform and the Jena API
●It is a complete editor for RDF(S) and OWL models
●Checking mechanisms.
●Supports the import to RDFa, OWL, RDF(s), XHTML,
SPIN, TDB etc.
●Export languages are RDFS
●It store ontology on DBMS.

79. Figure3 : TopBraid Composer (FE) screenshot

80. Comparison of Protégé and
TopBraid

81. TABLE 2 : Interoperability
Features Protégé TopBraid
With
other
Ontology tools
PROMPT,
OKBC, JESS,
FaCT and Jena
Sesame ,
Jena and
Allegro Graph
Imports
From languages
XML(S), RDF(S),
OWL,
(RDF,UML,
XML)backend,
Excel, BioPortal and
DataMaster
RDFa, WOL, RDF(s)
,XHTML,Microdata
& RDFa Data
sources,
SPIN,News Feed,
RDF Files into a
new
TDB, Emailand
Excel
Exports to
languages
XML(S), RDF(S), OWL,
Clips, SWRL-IQ, Instance
Selection, MetaAnalysis,
OWLDoc, Queries and
(RDF,UML, XML)backend
RDF(S)

82. TABLE 1: Software Architecture and Tool
Evolution
Features Protégé TopBraid
Semantic
web
architecture
Standalone
and Client server
Standalone Eclipse
plug-in
Backup
management
No No
Ontology
storage
File and
DBMS
DBMS

83. TABLE 3: Inference Services
Features Protégé TopBraid
Built-in
Inference engine
PAL SPARQL
Other attached
Inference Engine
RACER, FACT,
FACT++, F-logic
and Pellet
OWLIM,
Pellet and SPARQL
consistency
checking
mechanisms
Yes Yes
Automatic
classifications
No No

84. TABLE 4 : Usability of Tools
Features Protégé TopBraid
Graphical
taxonomy
Yes No
Zooms Yes No
Collaborative
working
Yes Yes
Ontology libraries Yes Yes

85. References
• Comparison of Ontology Editors by Emhimed Salem
Alatrish
• Survey on Web Ontology Editing Tools by Sabin
Corneliu Buraga, Liliana Cojocaru, Ovidiu Cătălin
Nichifor
• http://en.wikipedia.org/wiki/Ontology_editor
• http://protegewiki.stanford.edu/wiki/Main_Page
• http://www.topquadrant.com/tools/IDE-topbraid-
composer-maestro-edition/

86. Ontology Evaluation Techniques
Anwesha Bhattacharya (MLIB 01)

87. Ontology Evaluation
Source1: Ontological evaluation and validation by Samir Tartir, I. Budak Arpinar, & Amit P. Sheths

88. What does it mean for an ontology to
be correct/best?
“goodness” or the “validity” of an
ontology might vary between different
users or different domains.

89. Choice of a suitable Ontology
evaluation approach
Depends on the-
 purpose of evaluation
 application in which the ontology is to be used
 aspect of the ontology we are trying to evaluate.
In the area of ontology-supported computing and
the semantic web, there is no single best or
preferred approach to ontology evaluation;

90. Classification of
Ontology Evaluation Approaches
Broadly speaking, most evaluation approaches fall into one of the following
categories:
 Golden standard: Those based on comparing the ontology to a “golden
standard” (which may itself be an ontology; e.g. MAEDCHE AND STAAB, 2002);
 Application based: Those based on using the ontology in an application and
evaluating the results (e.g. PORZEL &MALAKA, 2004);
 Data-driven: By comparing the ontology with a source of data from the
domain to be covered ; Assessment by domain experts. (e.g. BREWSTER et al.,
2004);
 Assessment by Humans: Those where evaluation is done by humans who
try to assess how well the ontology meets a set of predefined criteria, standards,
requirements, etc. (e.g. LOZANOTELLO AND GÓMEZ-PÉREZ, 2004).

91. Approach to evaluation
Level Golden
standard
Application-
based
Data-driven Assessment
by humans
Lexical, vocabulary,
concept, data
Hierarchy, taxonomy
Other semantic
relations
Context, application
Syntactic
Structure, architecture,
design
Source2: A survey of ontology evaluation techniques by Janez Brank, Marko Grobelnik, Dunja Mladenić

92. Current Approaches in
Ontology Evaluation & Validation
Evolution-Based
Logical (Rule-based)
Metric-based (Feature-based)

93. Evolution-Based
This approach tracks an important characteristic of
ontologies, change over time (evolve).
This may occur due to three causes as proposed in
[Nov. 2003]:
1. Changes in the domain,
2. Changes in conceptualization,
3. Changes in the explicit specification.

94. Logical (Rule-based)
This approach of ontology validation
and quality evaluation use rules -
1. built in the ontology languages
&
2. users provided to detect conflicts in
ontologies.

95. Metric-based (Feature-based)
This technique offers a quantitative perspective of
ontology quality. It-
 scan through the ontology to gather different
types of statistics about the knowledge
presented in the ontology.
OR
 ask the user to input some information that is
not included in the ontology itself.

96. Technical characteristics of ontology evaluation tools
Source3: D1.2.3 Methods for ontology evaluation by Jens Hartmann (UKARL)

97. Source3: D1.2.3 Methods for ontology evaluation by Jens Hartmann (UKARL)

98. Technique Approach Users Automatic/M
anual
Schema/KB Ontology
[Plessers 2005] Evolution Developers Manual Schema Entered
[Hasse 2005] Evolution Developers Manual Schema Entered
[Arpinar 2006] Logical Developers Manual Schema + KB Entered
Swoop Logical Developers Automatic Schema Entered
OntoMetric Metric Developers Manual Schema Entered
[Supekar 2004] Metric D + E Automatic Schema Crawled
AKTiveRank Metric D + E Automatic Schema Crawled
[Crocho 2004] Metric Developers Automatic Schema Entered
[Mostowfi
2006]
Metric Developers Automatic Schema Entered
oQual Metric D + E Manual Schema Entered
OntoClean Metric Developers Manual Schema Entered
OntoQA Metric D + E Automatic Schema + KB Entered

99. References
1) Samir Tartir, I. Budak Arpinar, & Amit P. Sheth. Ontological
evaluation and validation.
2) Janez Brank, Marko Grobelnik, Dunja Mladenić. (2005). A survey of
ontology evaluation techniques.
3) Jens Hartmann et.al. (2005). D1.2.3 Methods for ontology
evaluation. pp. 49,.V 1(3).1KWEB/2004/D1.2.3/v1.3.
4) http://www.OntoWeb.org
5) [Alani 2006] Alani H., Brewster C. and Shadbolt N. Ranking
Ontologies with AKTiveRank. 5th International Semantic Web
Conference. November, 5-9, 2 006
6) BREWSTER, C. et al. Data driven ontology evaluation. Proceedings
of Int. Conf. on Language Resources and Evaluation, Lisbon, 2004.

100. Ontology Applications
Manash Kumar (MLIB 05)

101. Application of Ontology
Ontologies are the structural frameworks for
organizing information and are used in
various fields.
We subdivide the space of uses for
ontologies into the following three categories:
-Communication
-Inter-Operability
-Systems engineering

102. Communication
Ontologies reduce conceptual and terminological confusion by
providing a unifying framework within an organization.
Now these are several aspects of the use of ontologies to
facilitate communication:
-Normative Models
-Networks of Relationships
-Consistency and Lack of Ambiguity
-Integrating Different User Perspectives

103. Interoperability
A major theme for the use of ontologies in domains such as
enterprise modeling and multi agent architectures is the
creation of an integrating environment for different
software tools.
Dimensions of Inter-Operability:
Internal Inter-Operability
External Inter-Operability
Integrated Ontologies Among Domains
Integrating Ontologies Among Tools

104. System Engineering
Specification
The shared understanding can assist the process of identifying requirements and
defining a specification for an IT system.
Reliability
Informal ontologies can improve the reliability of software systems by serving as a
basis for manual checking of the design against the specification.
Using formal ontologies enables the use of semi-automated consistency checking
of the software system with respect to the declarative specification.
Reusability
To be effective, ontologies must also support reusability so that we can import and
export modules among different software systems.
Ontologies provide an easy to reuse library of class objects for modeling problems
and domains.

105. Application Areas of Ontology
1.Information Retrieval
● As a tool for intelligent search through inference
mechanism instead of keyword matching
● Easy retrievability of information without using
complicated Boolean logic
● Cross Language Information Retrieval
● Improve recall by query expansion through the
synonymy relations
● Improve precision through Word Sense
Disambiguation (identification of the relevant
meaning of a word in a given context among all its
possible meanings)

106. 2.Digital Libraries
● Building dynamical catalogues from
machine readable metadata
● Automatic indexing and annotation of web
pages or documents with meaning
● To give context based organisation
(semantic clustering) of information
resources
● Site organization and navigational support

107. 3.Information Integration
Seamless integration of information from different
websites and databases
4.Knowledge Engineering and Management
● As a knowledge management tools for selective
semantic access (meaning oriented access)
● Guided discovery of knowledge
5.Natural Language Processing
● Better machine translation
● Queries using natural language

108. Example of Practical Application
of Ontology

109. GENE ONTOLOGY (GO)
GO, is a major bioinformatics initiative to
unify the representation of gene and gene
product attributes across all species.
GO is part of a larger classification effort,
the Open Biomedical Ontologies (OBO).

110. What can scientists do with GO?
● Access gene product functional information
● Find how much of a proteome is involved in a process/ function/
component in the cell
● Map GO terms and incorporate manual annotations into own
databases
● Provide a link between biological knowledge and …
- gene expression profiles
- proteomics data

112. http://amigo.geneontology.org/amigo
http://www.slideshare.net/faoaims/examples-of-
ontology-applications
http://en.wikipedia.org/wiki/Ontology_
%28information_science%29
References
http://idc.hust.edu.cn/~rxli/teaching/semanticweb.htm

113. Ontology Mapping and Alignment
Shiv Shakti Ghosh (MLIB 09)

114. Reasons for Mismatches
• Ontology is not a reality it is a subjective
representation of it
• Different designers have different views
• Different tasks and requirements for
applications
• Different conventions, etc.

115. Types of Mismatches
• Language-level mismatches(Difference in
expressiveness or semantics of ontology
language)
• Ontology-level mismatches(Difference in
the structure of semantics of the
ontology)

116. Ontology Alignment
• Definition- Given two ontologies O1
and O2, aligning one ontology with
another means that for each entity
(concept C, relation R, or instance I) in
ontology O1, we try to find a
corresponding entity, which has the
same intended meaning, in ontology O2.

117. • we define an ontology alignment
function, align, based on the vocabulary,
E, of all entities e ∈ E and based on the
set of possible ontologies, O, as follows:
align : E × O × O  E, with ∀e ∈ O1(∃f
∈ O2 : align(e,O1,O2) = f ). A entity e
interpreted in an ontology O is either a
concept, a relation or an instance, i.e., e|O
∈ C ∪ R ∪ I.

118. Ontology 1 Ontology 2
Object Thing
Car Automobile
Porsche KA-123 Marc’s Porsche
Speed Characteristic
250km/h fast

119. Alignment Methods
• 1. Feature Engineering: Select small
excerpts of the overall ontology definition
to describe a specific entity (e.g., the
label to describe the concept o1:car).
• 2. Search Step Selection: Choose two
entities from the two ontologies to
compare (e.g., o1:car and o2:automobile).
• 3. Similarity Computation: Indicate a
similarity for a given description of two
entities
(e.g.,simillabel(o1:car,o2:automobile).

120. • 4. Similarity Aggregation: Aggregate multiple
similarity assessments for one pair of entities into
a single measure (e.g.,
simillabel(o1:car,o2:automobile)
+similinstances(o1:car,o2:automobile).
• 5. Interpretation: Use all aggregated numbers,
some threshold and some interpretation strategy
to propose the equality for the selected entity
pairs (eg.,align(o1:car)).
• 6. Iteration: As the similarity of one entity pair
influences the similarity of neighbouring entity
pairs, the equality is propagated through the
ontologies (e.g., it may lead to a new
simil(o1:car,o2:automobile), subsequently
resulting in align(o1:car)=o2:automobile.

121. Ontology Mapping
• Definition-Given two ontologies A and
B, mapping one ontology with another
means that for each concept (node) in
ontology A, we try to find a
corresponding concept (node), which has
the same or similar semantics, in
ontology B and vice verse.

122. • Formally an ontology mapping function
can be defined the following way:
• Map: O1  O2
• Map(ei1j1 ) = ei2j2 , if sim(ei1j1 ei2j2) > t , t
being the threshold.

123. Mapping methods
• Heuristic and Rule-based methods -
Most structure-analysis and lexical
analysis methods.
• Graph analysis - Treat ontologies as
graphs and compare the corresponding
subgraphs
• Machine-learning - Statistics of data
content, Using multiple learners, Using
instance and values information

124. • Probabilistic approaches - Combining
results produced by heuristic-based
mappings , more in data matching.
• Reasoning, theorem proving - Start
with a combination of matchers using
lexical information and external
resources , Use a SAT solver to find
equivalence, generalization, and
specialization mappings

126. Using Alignment and Mapping
• Data transformation
• Query answering (query reformulation)
• Reasoning with mappings
• Generation of ontology extensions

127. Alignment & Mapping Tools
• PROMPT: PROMPT uses labels to propose alignments
between two ontologies . These are then merged.
• Anchor-PROMPT: Anchor-PROMPT adds structural
evidence to this .
• GLUE: Uses machine learning techniques to build
instance classifiers. Further they rely on the structures.
• Chimera: Analysis of molecular structures and related
data, including density maps, supramolecular
assemblies, sequence alignments, docking results,
trajectories, and conformational ensembles.
• FCA-Merge: merges ontologies.

128. References
● Ontology Mapping – An Integrated
Approach: Marc Ehrig, York Sure
● Computing minimal mappings between
lightweight ontologies: Fausto Giunchiglia ·
Vincenzo Maltese · Aliaksandr Autayeu
● Ontology Mapping and Alignment:Natasha
Noy(Stanford University)
● Framework for Ontology Alignment and
Mapping:Marc Ehrig, Steffen Staab and York
Sure

129. Existing Projects and Conclusion
Mohit Garg (MLIB 06)

130. Ontology Projects

SOUPA

Gaia

GLOSS

ASC

SOCOM

131. SOUPA
Standard Ontology for Ubiquitous and
Pervasive Applications

Designed to model and support pervasive
computing applications.

Expressed using the Web Ontology Language
OWL and includes modular component
vocabularies to represent intelligent agents with
associated beliefs, desire, and intentions, time,
space, events, user profiles, actions, and
policies for security and privacy

132. 
Pervasive Computing Environments consist of
a large number of independent entities that help
transform physical spaces into computationally
active and intelligent spaces.

These entities could be devices, applications,
services or users.

133. Impulse

Different entities to interact with each other.

Difficult to assure that independent entities can
understand the “semantics” of the environment
and other entities when they interact with each
other.

Developers who are inexperience in knowledge
representation to quickly begin building
ontology-driven applications without needing to
define ontologies from scratch.

134. 
Ontologies have been used to make
information systems more usable. They allow
different entities to have a common
understanding of various terms and concepts
and smoothen their interaction.

SOUPA vocabularies are adopted from a
number of different consensus ontologies. The
strategy for developing SOUPA is to borrow
terms from these ontologies but not to import
them directly.SOUPA consists of two distinctive
but related set of ontologies: SOUPA Core and
SOUPA Extension.

135. SOUPA Core
consists of vocabularies for expressing concepts
that are associated with person, agent,belief-
desire-intention (BDI), action, policy, time,
space,and event.

137. SOUPA Extension
The SOUPA Extension ontologies are defined
with two purposes:
(i) define an extended set of vocabularies for
supporting specific types pervasive application
domains.
(ii) demonstrate how to define new ontologies by
extending the SOUPA Core ontologies.

138. Surveyed Ontologies Using Ontology Design
Principles
Ontology
Based
Models
CoBrA
SOUPA
Gaia GLOSS ASC
CoOL
SOCAM
CONON
Coherence Y
Clarity Y Y
Extensibility Y Y Y Y Y
Ontological
commitment
Y Y Y
Orthogonality Y Y
Encoding
Bias
Y Y Y Y

139. SOUPA Applications

CoBrA

MoGATU

140. Conclusion

The use of ontologies is a key requirement for
realizing the vision of pervasive computing.

We believe by defining a shared pervasive
computing ontology, we can help system
developers to reduce their efforts in creating
ontologies and to be more focused on the
actual system implementations.

The SOUPA project is a step towards the
standardization of a shared ontology for
ubiquitous and pervasive computing
applications.

141. References

Handbook on Ontologies SE - S.Staab, R.Studer.

SOUPA: Standard Ontology for Ubiquitous and Pervasive Applications Harry Chen,∗
Filip Perich, Tim Finin, Anupam JoshiDepartment of Computer Science & Electrical
EngineeringUniversity of Maryland, Baltimore County{hchen4, fperic1, finin,
joshi}@csee.umbc.edu

Ontology Based Service Discovery for ContextAware ComputingVanathi.B#1,
Rhymend Uthariaraj.V#2Ramanujan Computing CentreAnna University
ChennaiChennai – 600 025, Tamil Nadu, hymend@annauniv.edu

IEEE INTERNET COMPUTING 1089-7801/04/$20.00 © 2004 IEEE Published by the
IEEE Computer Society NOVEMBER DECEMBER 2004 69Agent TrackEditor:
Michael N. Huhnshuhns@sc.eduHarryChen,Tim Finin,Anupam Joshi,and Lalana
KagalUniversity of Maryland,Baltimore CountyFilip PerichCougaar SoftwareDipanjan
ChakrabortyIBM India Research LaboratoryIntelligent AgentsMeet the Semantic
Webin Smart Spaces

Using Ontologies to Address Key Issues in Ubiquitous Computing Systems Eleni
Christopoulou and Achilles Kameas Research Academic Computer Technology
Institute, Research Unit 3, Design of Ambient Intelligent Systems Group, 61 Riga
Feraiou str. 26221, Patras, Greece{hristope, kameas}@cti.gr

142. A BIG THANK YOU!!!