ONTOLOGY AND ITS VARIOUS ASPECTS

COLLOQUIUM 3Date : 26.03.2014.

CONDUCTED BY

STUDENTS OF MSLIS,

SECOND SEMESTER,

SESSION 2013-2015,

DRTC, ISIBC.

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

IntroductionSamhati Soor (MLIB 08)

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.

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.

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

PERSPECTIVE● Philosophy● Library and

Information Science● Artificial Intelligence● Linguistics● Natural Language

Processing● The Semantic Web

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

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.

ONTOLOGY LANGUAGESat 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).

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

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

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

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

Ontology EvolutionDibakar Sen (MLIB 02)

Lexicon – It gives all the possible forms possible 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 definition definition 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

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).

It provides associative relationship associative 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

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

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.

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 ofNT - Instance ofRT – Related to

Comparison between Taxonomy, Thesaurus and Ontology

Evolution Process

4. AddComplexRelation-

ship

4. AddComplexRelation-

ship

Taxonomy

Mechanical Device

Pump Engine

Hydrulic Pump Fuel Pump

Aircraft Engine Driven Pump

Jet Engine

Thesaurus

Mechanical Device

Pump Engine

Hydrulic Pump Fuel Pump

Aircraft Engine Driven Pump

Jet Engine

Pumping

Hydrulic System

Fuel Filter

= Boarder / NarrowerTerm = Related Term

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

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/PidcockArticle

http://protege.stanford.edu/conference/2006/submissions/slides/1.2_Uschold.pdf

Ontology Classification(Part-I)

Jayanta Kr. Nayek (MLIB 03)

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.

Level of Formality : Example

Ontology classification based on domain scope

Top-levelor Foundational Ontology

Application or LocalOntology

General OntologyCore Reference

Ontology

Task OntologyDomainOntology

Catherine Roussey, Francois Pinet, Myoung Ah Kang, and Oscar Corcho.

Source: An Introduction to Ontologies and Ontology Engineering by

Specialize

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.

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.

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.

General Ontologies

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

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.

Complexity of OntologiesDepending 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)

ExamplesLocal/Application 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.

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.

Ontology Classification(Part-II)

Manasa Rath (MLIB 04)

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

THING

INDIVIDUALOBJECT INTANGIBLE REPRESENTED

THING

STUFF INTANGIBLEOBJECT COLLECTION

EVENTINTANGIBLE

STUFFRELATIONSHIP

Top level categories in Cyc

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

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 

Applications of Cyc:

Cleveland Clinic, Ohio,US

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

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

Structure of Gene Ontology

CELLULARCOMPONENT

BIOLOGICALCOMPONENT

MOLECULARCOMPONENT

PIGMENTMETABOLICPROCESS

PIGMENTATION

PIGMENTATIONDURING

DEVELOPMENT

POSITIVEREGULATION

DURINGBIOLOGICAL

PROCESS

NEGATIVEREGULATION

DURINGBIOLOGICAL

PROCESS

PIGMENTATIONREGULATION OF

BIOLOGICALPROCESS

Source: www.geneontology.org

EXAMPLE of a GO term:

AnnotationGO 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 basedThe date and the creator of the annotation

References1.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/ontology/toplevel.htm (August 8, 2006)

Ontology Development Methodology

Tanmay Mondal (MLIB 10)

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.

Construction of ontologies is very much an art rather than a science .

Good methodology for developing ontologies .

Quality of the ontologies .

Why?

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

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.

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

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

Management Activities- Plan, control, QualityDevelopment-oriented-Specification,Coceptualization,Formalization, implementationSuppport Activites-Knowledgeacquisition,Evaluation,integration,Documentation Ontology Life Cycle-How the stages are related.

Development Process of Methontology

Developed Ontology

CHEMICALS

ENVIRONMNT POLLUTANTS

REFERENCE

(KA)^2

Applications Using Ontologies DevelopedWith 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.

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.

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

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

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

Ontology Development ToolsSandip Das (MLIB 07)

Why Tools????

An ontology can be expressed in structured languages.

Maintaining the structure is difficult.

Tools for building ontologies attempts to simplify the task.

Tools for Ontology Construction

Open Source Commercial FreeProtégé

http://protege.stanford.edu/

TopBraid Composerhttp://www.topquadrant.com/tools/IDE-topbraid-

composer-maestro-edition/

Knoodlhttp://www.knoodl.com/ui/home.html;jsessionid=E1EAD

42A4DC8BAA6F3E2FE747608012D

KAONhttp://sourceforge.net/projects/kaon/

a.k.a. softwarehttp://www.a-k-a.com.au//?page_id=43

Fluent Editor 2http://www.cognitum.eu/semantics/FluentEditor2/Defaul

t.aspx

Neologismhttp://neologism.deri.ie/

Onto Studiohttp://www.ontoprise.de/en/products/ontostudio/

Java Ontology Editor

http://cit.cse.sc.edu/demos/java/joe/joeBeta-jar.html

Swoophttp://www.mindswap.org/2004/SWOOP

OWL GrEdhttp://owlgred.lumii.lv/

KMgenhttp://www.algo.be/ref-projects.htm#KMgen

NeOn Toolkithttp://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_Syntax_Zoo_OWL2_Manchester

Anzo expresshttp://www.cambridgesemantics.com/products/anzo-

express

Model Future OWL Editor

http://www.modelfutures.com/owl/

OBO-Edithttp://geneontology.sourceforge.net/

Be Informed Suitehttp://www.beinformed.com/BeInformed/website/en?

init=true&tempregion=US

Mywebhttp://www.ontologyonline.org/main.html

TODEhttp://en.wikipedia.org/wiki/Tool_for_Ontology_Develop

ment_and_Editing_(TODE)

Semaphore Ontology Manager

http://www.smartlogic.com/

Semantic Turkeyhttp://semanticturkey.uniroma2.it/

TwoUse Toolkithttps://code.google.com/p/twouse/

Enterprise architecthttp://www.sparxsystems.com/

Synapticahttp://www.synaptica.com/

Open source software.

Developed by Standford Center for Biomedical Informatics Research.

Protégé FeaturesSupport 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.

Figure1 : Protégé Screenshot

Figure2 : Protégé screenshot

• Developed by TopQuadrant Co.

• Comes in three editions : FE , SE , ME

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.

Figure3 : TopBraid Composer (FE) screenshot

Comparison of Protégé and TopBraid

TABLE 2 : InteroperabilityFeatures Protégé TopBraid

Withother

Ontology tools

PROMPT,OKBC, JESS,

FaCT and Jena

Sesame ,Jena and

Allegro Graph

ImportsFrom 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

newTDB, Emailand

Excel

Exports tolanguages

XML(S), RDF(S), OWL,Clips, SWRL-IQ, InstanceSelection, MetaAnalysis,

OWLDoc, Queries and(RDF,UML, XML)backend

RDF(S)

TABLE 1: Software Architecture and Tool Evolution

Features Protégé TopBraid

Semanticweb

architecture

Standaloneand Client server

Standalone Eclipseplug-in

Backupmanagement

No No

Ontologystorage

File andDBMS

DBMS

TABLE 3: Inference Services

Features Protégé TopBraid

Built-inInference engine

PAL SPARQL

Other attachedInference Engine

RACER, FACT,FACT++, F-logic

and Pellet

OWLIM,Pellet and SPARQL

consistency checking

mechanisms

Yes Yes

Automaticclassifications

No No

TABLE 4 : Usability of ToolsFeatures Protégé TopBraid

Graphical taxonomy

Yes No

Zooms Yes No

Collaborative working

Yes Yes

Ontology libraries Yes Yes

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/

Ontology Evaluation TechniquesAnwesha Bhattacharya (MLIB 01)

Ontology Evaluation

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

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.

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;

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).

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

Current Approaches in Ontology Evaluation & Validation

Evolution-Based

Logical (Rule-based)

Metric-based (Feature-based)

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.

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.

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.

Technical characteristics of ontology evaluation tools

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

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

Technique Approach Users Automatic/Manual

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

References1) 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.

Ontology ApplicationsManash Kumar (MLIB 05)

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

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

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

System EngineeringSpecification 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.

Application Areas of Ontology1.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)

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

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

Example of Practical Application of Ontology

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).

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

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

Ontology Mapping and AlignmentShiv Shakti Ghosh (MLIB 09)

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.

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)

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.

• 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.

Ontology 1 Ontology 2

Object Thing

Car Automobile

Porsche KA-123 Marc’s Porsche

Speed Characteristic

250km/h fast

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).

• 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.

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.

• 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.

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

• 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

Using Alignment and Mapping

• Data transformation

• Query answering (query reformulation)

• Reasoning with mappings

• Generation of ontology extensions

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.

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

Existing Projects and ConclusionMohit Garg (MLIB 06)

Ontology Projects

SOUPA Gaia GLOSS ASC SOCOM

SOUPAStandard 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

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.

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.

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.

SOUPA Core

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

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.

Surveyed Ontologies Using Ontology Design Principles

OntologyBasedModels

CoBrA

SOUPA

Gaia GLOSS ASC

CoOL

SOCAM

CONON

Coherence Y

Clarity Y Y

Extensibility Y Y Y Y Y

Ontologicalcommitment

Y Y Y

Orthogonality Y Y

EncodingBias

Y Y Y Y

SOUPA Applications

CoBrA MoGATU

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.

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

A BIG THANK YOU!!!