SlideShare a Scribd company logo
1 of 19
Download to read offline
OWL Web Ontology
Language Overview
Igor Myroshnichenko, M.S.
myrosh.com/owl-web-ontology-language-overview
Introduction
• OWL is a language for defining ontologies of
domain knowledge which can be queried and
reasoned over by software agents.
• OWL facilitates greater machine interpretability of
content than that supported by XML, RDF, and RDF
Schema by providing additional vocabulary along
with formal semantics.
Versions and Sublanguages
• OWL has 2 versions: OWL 1 and OWL 2. They both
have several sublanguages/profiles with varying
expressiveness and computational complexity.
• OWL 1 - the original version of the language
introduced in 2004.
• OWL 2 - the current version of the language
introduced in 2009. OWL 2 builds on top of OWL 1
by adding new features. All OWL 1 ontologies are
valid OWL 2 ontologies.
Human-readable Notation
Human-readable Notation:
Explanation
• Classes are blue boxes.
• Properties are green double ovals (top oval - type,
lower oval - name). Properties are tied to classes
and values by domain and range connectors.
• Property restrictions are red boxes with
corresponding connectors.
Ontologies
• Ontology – a structured representation of a domain
of knowledge. An OWL ontology is made up of
classes, properties, individuals and axioms
describing how they are interrelated.
Classes
• Class – a type of individuals that share properties. For example, a
Car class represents all Car individuals. Built-in classes: Thing and
Nothing.
• subClassOf – declares a class to be a subclass of another class.
For example, a Car is subClassOf Vehicle. Therefore, a Car is also
a Vehicle.
• equivalentClass – states that two classes are the same and have
the same individuals. For example, a Car is equivalentClass to
Automobile.
• hasKey (OWL 2 only) – states that each individual of a class is
uniquely identified by a property or a set of properties. For
example, a Car hasKey hasVIN.
Individuals
• Individual – an instance/object of a class. For
example, a FordMustang is an individual of the Car
class. Individuals may be interrelated via properties.
• sameAs – states that two individuals are the same.
• differentFrom – states that an individual is different
from other individuals.
• allDifferent – declares a set of individuals to be
mutually distinct.
Properties
• ObjectProperty – a relationship between individuals. For
example, a Person hasCar.
• DataProperty – a relationship between an individual and data
values. For example, a Person hasSSN.
• domain – states that a property belongs to a class. For example,
the Person class is the domain of the hasName property.
• range – for ObjectProperties, limits the class of individuals a
property may have as its values. For example, the range of
hasCar is Car. For DataProperties, declares the data type of
possible values. For example, the range of hasName is string.
Properties (continued)
• subPropertyOf – declares a property to be a subproperty of
another property. For example, isChildOf is subPropertyOf
isRelativeOf.
• inverseOf – declares a property to be the “mirror image” of
another property. For example, isCarOf is inverseOf hasCar.
• equivalentProperty – states that two properties are the same.
• PropertyChain (OWL 2 only) – declares a chain of properties
to be a named property. For example, a chain of two
hasParent properties can be declared to be the
hasGrandparent property.
Property Characteristics
• FunctionalProperty – states that a property has no
more than one value for each individual.
• InverseFunctionalProperty – declares a property to
be inverseOf a FunctionalProperty.
• TransitiveProperty – states that a property is
transitive. For example, if SantaClaraCounty
isRegionOf California and California isRegionOf
UnitedStates, then SantaClaraCounty isRegionOf
UnitedStates.
Property Characteristics
(continued)
• SymmetricProperty – declares a property to be
symmetric. For example, if John isFriendOf Ann,
then Ann isFriendOf John.
• ReflexiveProperty (OWL 2 only) – states that a
property relates an individual to itself.
• IrreflexiveProperty (OWL 2 only) – states that a
property cannot relate an individual to itself.
Property Restrictions
• allValuesFrom – adds a local restriction on the property’s range. For
example, hasCar allValuesFrom SportCar narrows the range of
hasCar from Car to SportsCar.
• someValuesFrom – adds a local restriction on the property’s range.
For example, hasCar someValuesFrom SportCar updates the range
of hasCar to include SportsCar individuals.
• minCardinality n – states that a property can relate an individual to
at least n other individuals or data values. For example, a Person
must have at least 1 Car.
• maxCardinality n -states that a property can relate an individual to
at most n other individuals or data values. For example, a Person
must have at most 1 SSN value.
Property Restrictions
(continued)
• cardinality n – states that a property’s both min and max
cardinality is n.
• minQualifiedCardinality n (OWL 2 only) – extends
minCardinality by restricting the class or data type of at
least n individuals a property can relate an individual to.
• maxQualifiedCardinality n (OWL 2 only) – extends
maxCardinality by restricting the class or data type of at
most n individuals a property can relate an individual to.
Syntaxes: RDF/XML
<owl:ObjectProperty rdf:about="#hasCar">
<owl:inverseOf rdf:resource="#isCarOf"/>
<rdf:type rdf:resource="http://www.w3.org/2002/07/
owl#InverseFunctionalProperty"/>
<rdfs:domain rdf:resource="#Person"/>
<rdfs:range rdf:resource="#Car"/>
</owl:ObjectProperty>
Syntaxes: OWL/XML
<Declaration>
<ObjectProperty IRI="#hasCar"/>
</Declaration>
<InverseObjectProperties>
<ObjectProperty IRI="#hasCar"/>
<ObjectProperty IRI="#isCarOf"/>
</InverseObjectProperties>
<InverseFunctionalObjectProperty>
<ObjectProperty IRI="#hasCar"/>
</InverseFunctionalObjectProperty>
<ObjectPropertyDomain>
<ObjectProperty IRI="#hasCar"/>
<Class IRI="#Person"/>
</ObjectPropertyDomain>
<ObjectPropertyRange>
<ObjectProperty IRI="#hasCar"/>
<Class IRI="#Car"/>
</ObjectPropertyRange>
Syntaxes: Functional
InverseObjectProperties(:hasCar :isCarOf)
InverseFunctionalObjectProperty(:hasCar)
ObjectPropertyDomain(:hasCar :Person)
ObjectPropertyRange(:hasCar :Car)
Syntaxes: Manchester
ObjectProperty: hasCar
Characteristics:
InverseFunctional
Domain:
Person
Range:
Car
InverseOf:
isCarOf
Thank you!
• Please refer to myrosh.com/owl-web-ontology-
language-overview for more details on OWL Web
Ontology Language, examples, info on OWL tools,
real world use cases, references and more.

More Related Content

What's hot

What's hot (20)

Owl web ontology language
Owl  web ontology languageOwl  web ontology language
Owl web ontology language
 
Understanding RDF: the Resource Description Framework in Context (1999)
Understanding RDF: the Resource Description Framework in Context  (1999)Understanding RDF: the Resource Description Framework in Context  (1999)
Understanding RDF: the Resource Description Framework in Context (1999)
 
Introduction to RDF
Introduction to RDFIntroduction to RDF
Introduction to RDF
 
Semantic web
Semantic webSemantic web
Semantic web
 
Introduction to RDF
Introduction to RDFIntroduction to RDF
Introduction to RDF
 
Rdf
RdfRdf
Rdf
 
RDF and OWL
RDF and OWLRDF and OWL
RDF and OWL
 
Ontologies in RDF-S/OWL
Ontologies in RDF-S/OWLOntologies in RDF-S/OWL
Ontologies in RDF-S/OWL
 
Semantic Web: From Representations to Applications
Semantic Web: From Representations to ApplicationsSemantic Web: From Representations to Applications
Semantic Web: From Representations to Applications
 
Sparql a simple knowledge query
Sparql  a simple knowledge querySparql  a simple knowledge query
Sparql a simple knowledge query
 
Semantic Web in Action
Semantic Web in ActionSemantic Web in Action
Semantic Web in Action
 
semanticweb
semanticwebsemanticweb
semanticweb
 
Data in RDF
Data in RDFData in RDF
Data in RDF
 
sw owl
 sw owl sw owl
sw owl
 
Ist16-04 An introduction to RDF
Ist16-04 An introduction to RDF Ist16-04 An introduction to RDF
Ist16-04 An introduction to RDF
 
Rdf And Rdf Schema For Ontology Specification
Rdf And Rdf Schema For Ontology SpecificationRdf And Rdf Schema For Ontology Specification
Rdf And Rdf Schema For Ontology Specification
 
semantic web & natural language
semantic web & natural languagesemantic web & natural language
semantic web & natural language
 
OWL briefing
OWL briefingOWL briefing
OWL briefing
 
SHACL: Shaping the Big Ball of Data Mud
SHACL: Shaping the Big Ball of Data MudSHACL: Shaping the Big Ball of Data Mud
SHACL: Shaping the Big Ball of Data Mud
 
General Introduction for Semantic Web and Linked Open Data
General Introduction for Semantic Web and Linked Open DataGeneral Introduction for Semantic Web and Linked Open Data
General Introduction for Semantic Web and Linked Open Data
 

Viewers also liked

The Legal Rdf Ontology A Generic Model For Legal Documents
The Legal Rdf Ontology A Generic Model For Legal DocumentsThe Legal Rdf Ontology A Generic Model For Legal Documents
The Legal Rdf Ontology A Generic Model For Legal Documentslegalwebsite
 
Lean ontology development
Lean ontology developmentLean ontology development
Lean ontology developmentLieke Verhelst
 
Pal gov.tutorial4.session7.lab legalpersonontology
Pal gov.tutorial4.session7.lab legalpersonontologyPal gov.tutorial4.session7.lab legalpersonontology
Pal gov.tutorial4.session7.lab legalpersonontologyMustafa Jarrar
 
Pal gov.tutorial4.session8 2.stepwisemethodologies
Pal gov.tutorial4.session8 2.stepwisemethodologiesPal gov.tutorial4.session8 2.stepwisemethodologies
Pal gov.tutorial4.session8 2.stepwisemethodologiesMustafa Jarrar
 
Modular Ontologies - A Formal Investigation of Semantics and Expressivity
Modular Ontologies - A Formal Investigation of Semantics and ExpressivityModular Ontologies - A Formal Investigation of Semantics and Expressivity
Modular Ontologies - A Formal Investigation of Semantics and ExpressivityJie Bao
 
ABSTAT: Ontology-driven Linked Data Summaries with Pattern Minimalization
ABSTAT: Ontology-driven Linked Data Summaries with Pattern MinimalizationABSTAT: Ontology-driven Linked Data Summaries with Pattern Minimalization
ABSTAT: Ontology-driven Linked Data Summaries with Pattern MinimalizationBlerina Spahiu
 
The General Ontology Evaluation Framework (GOEF) & the I-Choose Use Case A ...
The General Ontology Evaluation Framework (GOEF) & the I-Choose Use CaseA ...The General Ontology Evaluation Framework (GOEF) & the I-Choose Use CaseA ...
The General Ontology Evaluation Framework (GOEF) & the I-Choose Use Case A ...Joanne Luciano
 
IAS 16 Ontology Dojo
IAS 16 Ontology DojoIAS 16 Ontology Dojo
IAS 16 Ontology DojoRen Pope
 
Schema.org: Where did that come from!
Schema.org: Where did that come from!Schema.org: Where did that come from!
Schema.org: Where did that come from!Richard Wallis
 
On Beyond OWL: challenges for ontologies on the Web
On Beyond OWL: challenges for ontologies on the WebOn Beyond OWL: challenges for ontologies on the Web
On Beyond OWL: challenges for ontologies on the WebJames Hendler
 
Jarrar: Introduction to Ontology
Jarrar: Introduction to OntologyJarrar: Introduction to Ontology
Jarrar: Introduction to OntologyMustafa Jarrar
 
Semantic Web - Ontology 101
Semantic Web - Ontology 101Semantic Web - Ontology 101
Semantic Web - Ontology 101Luigi De Russis
 
Structured Data & the Future of Educational Material
Structured Data & the Future of Educational MaterialStructured Data & the Future of Educational Material
Structured Data & the Future of Educational MaterialPaul Groth
 
DBpedia Tutorial - Feb 2015, Dublin
DBpedia Tutorial - Feb 2015, DublinDBpedia Tutorial - Feb 2015, Dublin
DBpedia Tutorial - Feb 2015, Dublinm_ackermann
 

Viewers also liked (20)

The Legal Rdf Ontology A Generic Model For Legal Documents
The Legal Rdf Ontology A Generic Model For Legal DocumentsThe Legal Rdf Ontology A Generic Model For Legal Documents
The Legal Rdf Ontology A Generic Model For Legal Documents
 
Ontology
OntologyOntology
Ontology
 
Lean ontology development
Lean ontology developmentLean ontology development
Lean ontology development
 
Pal gov.tutorial4.session7.lab legalpersonontology
Pal gov.tutorial4.session7.lab legalpersonontologyPal gov.tutorial4.session7.lab legalpersonontology
Pal gov.tutorial4.session7.lab legalpersonontology
 
Pal gov.tutorial4.session8 2.stepwisemethodologies
Pal gov.tutorial4.session8 2.stepwisemethodologiesPal gov.tutorial4.session8 2.stepwisemethodologies
Pal gov.tutorial4.session8 2.stepwisemethodologies
 
Deliverable_5.1.2
Deliverable_5.1.2Deliverable_5.1.2
Deliverable_5.1.2
 
Modular Ontologies - A Formal Investigation of Semantics and Expressivity
Modular Ontologies - A Formal Investigation of Semantics and ExpressivityModular Ontologies - A Formal Investigation of Semantics and Expressivity
Modular Ontologies - A Formal Investigation of Semantics and Expressivity
 
ABSTAT: Ontology-driven Linked Data Summaries with Pattern Minimalization
ABSTAT: Ontology-driven Linked Data Summaries with Pattern MinimalizationABSTAT: Ontology-driven Linked Data Summaries with Pattern Minimalization
ABSTAT: Ontology-driven Linked Data Summaries with Pattern Minimalization
 
The General Ontology Evaluation Framework (GOEF) & the I-Choose Use Case A ...
The General Ontology Evaluation Framework (GOEF) & the I-Choose Use CaseA ...The General Ontology Evaluation Framework (GOEF) & the I-Choose Use CaseA ...
The General Ontology Evaluation Framework (GOEF) & the I-Choose Use Case A ...
 
IAS 16 Ontology Dojo
IAS 16 Ontology DojoIAS 16 Ontology Dojo
IAS 16 Ontology Dojo
 
Schema.org: Where did that come from!
Schema.org: Where did that come from!Schema.org: Where did that come from!
Schema.org: Where did that come from!
 
Decision tree
Decision treeDecision tree
Decision tree
 
On Beyond OWL: challenges for ontologies on the Web
On Beyond OWL: challenges for ontologies on the WebOn Beyond OWL: challenges for ontologies on the Web
On Beyond OWL: challenges for ontologies on the Web
 
Jarrar: Introduction to Ontology
Jarrar: Introduction to OntologyJarrar: Introduction to Ontology
Jarrar: Introduction to Ontology
 
Owls Power Point
Owls Power PointOwls Power Point
Owls Power Point
 
Wither OWL
Wither OWLWither OWL
Wither OWL
 
Semantic Web - Ontology 101
Semantic Web - Ontology 101Semantic Web - Ontology 101
Semantic Web - Ontology 101
 
2016 bmdid-mappings
2016 bmdid-mappings2016 bmdid-mappings
2016 bmdid-mappings
 
Structured Data & the Future of Educational Material
Structured Data & the Future of Educational MaterialStructured Data & the Future of Educational Material
Structured Data & the Future of Educational Material
 
DBpedia Tutorial - Feb 2015, Dublin
DBpedia Tutorial - Feb 2015, DublinDBpedia Tutorial - Feb 2015, Dublin
DBpedia Tutorial - Feb 2015, Dublin
 

Similar to OWL Web Ontology Language Overview

Using OWL for the RESO Data Dictionary
Using OWL for the RESO Data DictionaryUsing OWL for the RESO Data Dictionary
Using OWL for the RESO Data DictionaryChimezie Ogbuji
 
Ontologies and Vocabularies
Ontologies and VocabulariesOntologies and Vocabularies
Ontologies and Vocabulariesseanb
 
Introduction to Ontology Engineering with Fluent Editor 2014
Introduction to Ontology Engineering with Fluent Editor 2014Introduction to Ontology Engineering with Fluent Editor 2014
Introduction to Ontology Engineering with Fluent Editor 2014Cognitum
 
CS6010 Social Network Analysis Unit II
CS6010 Social Network Analysis   Unit IICS6010 Social Network Analysis   Unit II
CS6010 Social Network Analysis Unit IIpkaviya
 
Publishing Data Using Semantic Web Technologies
Publishing Data Using Semantic Web TechnologiesPublishing Data Using Semantic Web Technologies
Publishing Data Using Semantic Web TechnologiesNikolaos Konstantinou
 
Abstract class and Interface
Abstract class and InterfaceAbstract class and Interface
Abstract class and InterfaceHaris Bin Zahid
 
Introduction to object oriented programming
Introduction to object oriented programmingIntroduction to object oriented programming
Introduction to object oriented programmingAbzetdin Adamov
 
Abstraction1
Abstraction1Abstraction1
Abstraction1zindadili
 
Semantic web final assignment
Semantic web final assignmentSemantic web final assignment
Semantic web final assignmentBarryK88
 
Chapter 4 semantic web
Chapter 4 semantic webChapter 4 semantic web
Chapter 4 semantic webR A Akerkar
 
RDFa Semantic Web
RDFa Semantic WebRDFa Semantic Web
RDFa Semantic WebRob Paok
 
Semantic Web languages: Expressivity vs scalability
Semantic Web languages: Expressivity vs scalabilitySemantic Web languages: Expressivity vs scalability
Semantic Web languages: Expressivity vs scalabilitynvitucci
 

Similar to OWL Web Ontology Language Overview (20)

Using OWL for the RESO Data Dictionary
Using OWL for the RESO Data DictionaryUsing OWL for the RESO Data Dictionary
Using OWL for the RESO Data Dictionary
 
Ontologies and Vocabularies
Ontologies and VocabulariesOntologies and Vocabularies
Ontologies and Vocabularies
 
OWL Simple Properties.pptx
OWL Simple Properties.pptxOWL Simple Properties.pptx
OWL Simple Properties.pptx
 
Introduction to Ontology Engineering with Fluent Editor 2014
Introduction to Ontology Engineering with Fluent Editor 2014Introduction to Ontology Engineering with Fluent Editor 2014
Introduction to Ontology Engineering with Fluent Editor 2014
 
CS6010 Social Network Analysis Unit II
CS6010 Social Network Analysis   Unit IICS6010 Social Network Analysis   Unit II
CS6010 Social Network Analysis Unit II
 
07 intro2 oop
07 intro2 oop07 intro2 oop
07 intro2 oop
 
OOPS in Java
OOPS in JavaOOPS in Java
OOPS in Java
 
Publishing Data Using Semantic Web Technologies
Publishing Data Using Semantic Web TechnologiesPublishing Data Using Semantic Web Technologies
Publishing Data Using Semantic Web Technologies
 
Abstract class and Interface
Abstract class and InterfaceAbstract class and Interface
Abstract class and Interface
 
Introduction to object oriented programming
Introduction to object oriented programmingIntroduction to object oriented programming
Introduction to object oriented programming
 
Abstraction1
Abstraction1Abstraction1
Abstraction1
 
Semantic web final assignment
Semantic web final assignmentSemantic web final assignment
Semantic web final assignment
 
Intro tooop
Intro tooopIntro tooop
Intro tooop
 
Unit 3
Unit 3Unit 3
Unit 3
 
RDF briefing
RDF briefingRDF briefing
RDF briefing
 
OWL 2 Overview
OWL 2 OverviewOWL 2 Overview
OWL 2 Overview
 
Chapter 4 semantic web
Chapter 4 semantic webChapter 4 semantic web
Chapter 4 semantic web
 
RDFa Semantic Web
RDFa Semantic WebRDFa Semantic Web
RDFa Semantic Web
 
Semantic Web - OWL
Semantic Web - OWLSemantic Web - OWL
Semantic Web - OWL
 
Semantic Web languages: Expressivity vs scalability
Semantic Web languages: Expressivity vs scalabilitySemantic Web languages: Expressivity vs scalability
Semantic Web languages: Expressivity vs scalability
 

Recently uploaded

DNT_Corporate presentation know about us
DNT_Corporate presentation know about usDNT_Corporate presentation know about us
DNT_Corporate presentation know about usDynamic Netsoft
 
EY_Graph Database Powered Sustainability
EY_Graph Database Powered SustainabilityEY_Graph Database Powered Sustainability
EY_Graph Database Powered SustainabilityNeo4j
 
Hand gesture recognition PROJECT PPT.pptx
Hand gesture recognition PROJECT PPT.pptxHand gesture recognition PROJECT PPT.pptx
Hand gesture recognition PROJECT PPT.pptxbodapatigopi8531
 
Project Based Learning (A.I).pptx detail explanation
Project Based Learning (A.I).pptx detail explanationProject Based Learning (A.I).pptx detail explanation
Project Based Learning (A.I).pptx detail explanationkaushalgiri8080
 
Salesforce Certified Field Service Consultant
Salesforce Certified Field Service ConsultantSalesforce Certified Field Service Consultant
Salesforce Certified Field Service ConsultantAxelRicardoTrocheRiq
 
Professional Resume Template for Software Developers
Professional Resume Template for Software DevelopersProfessional Resume Template for Software Developers
Professional Resume Template for Software DevelopersVinodh Ram
 
Cloud Management Software Platforms: OpenStack
Cloud Management Software Platforms: OpenStackCloud Management Software Platforms: OpenStack
Cloud Management Software Platforms: OpenStackVICTOR MAESTRE RAMIREZ
 
Der Spagat zwischen BIAS und FAIRNESS (2024)
Der Spagat zwischen BIAS und FAIRNESS (2024)Der Spagat zwischen BIAS und FAIRNESS (2024)
Der Spagat zwischen BIAS und FAIRNESS (2024)OPEN KNOWLEDGE GmbH
 
Introduction to Decentralized Applications (dApps)
Introduction to Decentralized Applications (dApps)Introduction to Decentralized Applications (dApps)
Introduction to Decentralized Applications (dApps)Intelisync
 
Asset Management Software - Infographic
Asset Management Software - InfographicAsset Management Software - Infographic
Asset Management Software - InfographicHr365.us smith
 
Advancing Engineering with AI through the Next Generation of Strategic Projec...
Advancing Engineering with AI through the Next Generation of Strategic Projec...Advancing Engineering with AI through the Next Generation of Strategic Projec...
Advancing Engineering with AI through the Next Generation of Strategic Projec...OnePlan Solutions
 
(Genuine) Escort Service Lucknow | Starting ₹,5K To @25k with A/C 🧑🏽‍❤️‍🧑🏻 89...
(Genuine) Escort Service Lucknow | Starting ₹,5K To @25k with A/C 🧑🏽‍❤️‍🧑🏻 89...(Genuine) Escort Service Lucknow | Starting ₹,5K To @25k with A/C 🧑🏽‍❤️‍🧑🏻 89...
(Genuine) Escort Service Lucknow | Starting ₹,5K To @25k with A/C 🧑🏽‍❤️‍🧑🏻 89...gurkirankumar98700
 
What is Binary Language? Computer Number Systems
What is Binary Language?  Computer Number SystemsWhat is Binary Language?  Computer Number Systems
What is Binary Language? Computer Number SystemsJheuzeDellosa
 
HR Software Buyers Guide in 2024 - HRSoftware.com
HR Software Buyers Guide in 2024 - HRSoftware.comHR Software Buyers Guide in 2024 - HRSoftware.com
HR Software Buyers Guide in 2024 - HRSoftware.comFatema Valibhai
 
Alluxio Monthly Webinar | Cloud-Native Model Training on Distributed Data
Alluxio Monthly Webinar | Cloud-Native Model Training on Distributed DataAlluxio Monthly Webinar | Cloud-Native Model Training on Distributed Data
Alluxio Monthly Webinar | Cloud-Native Model Training on Distributed DataAlluxio, Inc.
 
Optimizing AI for immediate response in Smart CCTV
Optimizing AI for immediate response in Smart CCTVOptimizing AI for immediate response in Smart CCTV
Optimizing AI for immediate response in Smart CCTVshikhaohhpro
 
Russian Call Girls in Karol Bagh Aasnvi ➡️ 8264348440 💋📞 Independent Escort S...
Russian Call Girls in Karol Bagh Aasnvi ➡️ 8264348440 💋📞 Independent Escort S...Russian Call Girls in Karol Bagh Aasnvi ➡️ 8264348440 💋📞 Independent Escort S...
Russian Call Girls in Karol Bagh Aasnvi ➡️ 8264348440 💋📞 Independent Escort S...soniya singh
 
BATTLEFIELD ORM: TIPS, TACTICS AND STRATEGIES FOR CONQUERING YOUR DATABASE
BATTLEFIELD ORM: TIPS, TACTICS AND STRATEGIES FOR CONQUERING YOUR DATABASEBATTLEFIELD ORM: TIPS, TACTICS AND STRATEGIES FOR CONQUERING YOUR DATABASE
BATTLEFIELD ORM: TIPS, TACTICS AND STRATEGIES FOR CONQUERING YOUR DATABASEOrtus Solutions, Corp
 
chapter--4-software-project-planning.ppt
chapter--4-software-project-planning.pptchapter--4-software-project-planning.ppt
chapter--4-software-project-planning.pptkotipi9215
 

Recently uploaded (20)

DNT_Corporate presentation know about us
DNT_Corporate presentation know about usDNT_Corporate presentation know about us
DNT_Corporate presentation know about us
 
EY_Graph Database Powered Sustainability
EY_Graph Database Powered SustainabilityEY_Graph Database Powered Sustainability
EY_Graph Database Powered Sustainability
 
Hand gesture recognition PROJECT PPT.pptx
Hand gesture recognition PROJECT PPT.pptxHand gesture recognition PROJECT PPT.pptx
Hand gesture recognition PROJECT PPT.pptx
 
Project Based Learning (A.I).pptx detail explanation
Project Based Learning (A.I).pptx detail explanationProject Based Learning (A.I).pptx detail explanation
Project Based Learning (A.I).pptx detail explanation
 
Salesforce Certified Field Service Consultant
Salesforce Certified Field Service ConsultantSalesforce Certified Field Service Consultant
Salesforce Certified Field Service Consultant
 
Professional Resume Template for Software Developers
Professional Resume Template for Software DevelopersProfessional Resume Template for Software Developers
Professional Resume Template for Software Developers
 
Cloud Management Software Platforms: OpenStack
Cloud Management Software Platforms: OpenStackCloud Management Software Platforms: OpenStack
Cloud Management Software Platforms: OpenStack
 
Der Spagat zwischen BIAS und FAIRNESS (2024)
Der Spagat zwischen BIAS und FAIRNESS (2024)Der Spagat zwischen BIAS und FAIRNESS (2024)
Der Spagat zwischen BIAS und FAIRNESS (2024)
 
Exploring iOS App Development: Simplifying the Process
Exploring iOS App Development: Simplifying the ProcessExploring iOS App Development: Simplifying the Process
Exploring iOS App Development: Simplifying the Process
 
Introduction to Decentralized Applications (dApps)
Introduction to Decentralized Applications (dApps)Introduction to Decentralized Applications (dApps)
Introduction to Decentralized Applications (dApps)
 
Asset Management Software - Infographic
Asset Management Software - InfographicAsset Management Software - Infographic
Asset Management Software - Infographic
 
Advancing Engineering with AI through the Next Generation of Strategic Projec...
Advancing Engineering with AI through the Next Generation of Strategic Projec...Advancing Engineering with AI through the Next Generation of Strategic Projec...
Advancing Engineering with AI through the Next Generation of Strategic Projec...
 
(Genuine) Escort Service Lucknow | Starting ₹,5K To @25k with A/C 🧑🏽‍❤️‍🧑🏻 89...
(Genuine) Escort Service Lucknow | Starting ₹,5K To @25k with A/C 🧑🏽‍❤️‍🧑🏻 89...(Genuine) Escort Service Lucknow | Starting ₹,5K To @25k with A/C 🧑🏽‍❤️‍🧑🏻 89...
(Genuine) Escort Service Lucknow | Starting ₹,5K To @25k with A/C 🧑🏽‍❤️‍🧑🏻 89...
 
What is Binary Language? Computer Number Systems
What is Binary Language?  Computer Number SystemsWhat is Binary Language?  Computer Number Systems
What is Binary Language? Computer Number Systems
 
HR Software Buyers Guide in 2024 - HRSoftware.com
HR Software Buyers Guide in 2024 - HRSoftware.comHR Software Buyers Guide in 2024 - HRSoftware.com
HR Software Buyers Guide in 2024 - HRSoftware.com
 
Alluxio Monthly Webinar | Cloud-Native Model Training on Distributed Data
Alluxio Monthly Webinar | Cloud-Native Model Training on Distributed DataAlluxio Monthly Webinar | Cloud-Native Model Training on Distributed Data
Alluxio Monthly Webinar | Cloud-Native Model Training on Distributed Data
 
Optimizing AI for immediate response in Smart CCTV
Optimizing AI for immediate response in Smart CCTVOptimizing AI for immediate response in Smart CCTV
Optimizing AI for immediate response in Smart CCTV
 
Russian Call Girls in Karol Bagh Aasnvi ➡️ 8264348440 💋📞 Independent Escort S...
Russian Call Girls in Karol Bagh Aasnvi ➡️ 8264348440 💋📞 Independent Escort S...Russian Call Girls in Karol Bagh Aasnvi ➡️ 8264348440 💋📞 Independent Escort S...
Russian Call Girls in Karol Bagh Aasnvi ➡️ 8264348440 💋📞 Independent Escort S...
 
BATTLEFIELD ORM: TIPS, TACTICS AND STRATEGIES FOR CONQUERING YOUR DATABASE
BATTLEFIELD ORM: TIPS, TACTICS AND STRATEGIES FOR CONQUERING YOUR DATABASEBATTLEFIELD ORM: TIPS, TACTICS AND STRATEGIES FOR CONQUERING YOUR DATABASE
BATTLEFIELD ORM: TIPS, TACTICS AND STRATEGIES FOR CONQUERING YOUR DATABASE
 
chapter--4-software-project-planning.ppt
chapter--4-software-project-planning.pptchapter--4-software-project-planning.ppt
chapter--4-software-project-planning.ppt
 

OWL Web Ontology Language Overview

  • 1. OWL Web Ontology Language Overview Igor Myroshnichenko, M.S. myrosh.com/owl-web-ontology-language-overview
  • 2. Introduction • OWL is a language for defining ontologies of domain knowledge which can be queried and reasoned over by software agents. • OWL facilitates greater machine interpretability of content than that supported by XML, RDF, and RDF Schema by providing additional vocabulary along with formal semantics.
  • 3. Versions and Sublanguages • OWL has 2 versions: OWL 1 and OWL 2. They both have several sublanguages/profiles with varying expressiveness and computational complexity. • OWL 1 - the original version of the language introduced in 2004. • OWL 2 - the current version of the language introduced in 2009. OWL 2 builds on top of OWL 1 by adding new features. All OWL 1 ontologies are valid OWL 2 ontologies.
  • 5. Human-readable Notation: Explanation • Classes are blue boxes. • Properties are green double ovals (top oval - type, lower oval - name). Properties are tied to classes and values by domain and range connectors. • Property restrictions are red boxes with corresponding connectors.
  • 6. Ontologies • Ontology – a structured representation of a domain of knowledge. An OWL ontology is made up of classes, properties, individuals and axioms describing how they are interrelated.
  • 7. Classes • Class – a type of individuals that share properties. For example, a Car class represents all Car individuals. Built-in classes: Thing and Nothing. • subClassOf – declares a class to be a subclass of another class. For example, a Car is subClassOf Vehicle. Therefore, a Car is also a Vehicle. • equivalentClass – states that two classes are the same and have the same individuals. For example, a Car is equivalentClass to Automobile. • hasKey (OWL 2 only) – states that each individual of a class is uniquely identified by a property or a set of properties. For example, a Car hasKey hasVIN.
  • 8. Individuals • Individual – an instance/object of a class. For example, a FordMustang is an individual of the Car class. Individuals may be interrelated via properties. • sameAs – states that two individuals are the same. • differentFrom – states that an individual is different from other individuals. • allDifferent – declares a set of individuals to be mutually distinct.
  • 9. Properties • ObjectProperty – a relationship between individuals. For example, a Person hasCar. • DataProperty – a relationship between an individual and data values. For example, a Person hasSSN. • domain – states that a property belongs to a class. For example, the Person class is the domain of the hasName property. • range – for ObjectProperties, limits the class of individuals a property may have as its values. For example, the range of hasCar is Car. For DataProperties, declares the data type of possible values. For example, the range of hasName is string.
  • 10. Properties (continued) • subPropertyOf – declares a property to be a subproperty of another property. For example, isChildOf is subPropertyOf isRelativeOf. • inverseOf – declares a property to be the “mirror image” of another property. For example, isCarOf is inverseOf hasCar. • equivalentProperty – states that two properties are the same. • PropertyChain (OWL 2 only) – declares a chain of properties to be a named property. For example, a chain of two hasParent properties can be declared to be the hasGrandparent property.
  • 11. Property Characteristics • FunctionalProperty – states that a property has no more than one value for each individual. • InverseFunctionalProperty – declares a property to be inverseOf a FunctionalProperty. • TransitiveProperty – states that a property is transitive. For example, if SantaClaraCounty isRegionOf California and California isRegionOf UnitedStates, then SantaClaraCounty isRegionOf UnitedStates.
  • 12. Property Characteristics (continued) • SymmetricProperty – declares a property to be symmetric. For example, if John isFriendOf Ann, then Ann isFriendOf John. • ReflexiveProperty (OWL 2 only) – states that a property relates an individual to itself. • IrreflexiveProperty (OWL 2 only) – states that a property cannot relate an individual to itself.
  • 13. Property Restrictions • allValuesFrom – adds a local restriction on the property’s range. For example, hasCar allValuesFrom SportCar narrows the range of hasCar from Car to SportsCar. • someValuesFrom – adds a local restriction on the property’s range. For example, hasCar someValuesFrom SportCar updates the range of hasCar to include SportsCar individuals. • minCardinality n – states that a property can relate an individual to at least n other individuals or data values. For example, a Person must have at least 1 Car. • maxCardinality n -states that a property can relate an individual to at most n other individuals or data values. For example, a Person must have at most 1 SSN value.
  • 14. Property Restrictions (continued) • cardinality n – states that a property’s both min and max cardinality is n. • minQualifiedCardinality n (OWL 2 only) – extends minCardinality by restricting the class or data type of at least n individuals a property can relate an individual to. • maxQualifiedCardinality n (OWL 2 only) – extends maxCardinality by restricting the class or data type of at most n individuals a property can relate an individual to.
  • 15. Syntaxes: RDF/XML <owl:ObjectProperty rdf:about="#hasCar"> <owl:inverseOf rdf:resource="#isCarOf"/> <rdf:type rdf:resource="http://www.w3.org/2002/07/ owl#InverseFunctionalProperty"/> <rdfs:domain rdf:resource="#Person"/> <rdfs:range rdf:resource="#Car"/> </owl:ObjectProperty>
  • 16. Syntaxes: OWL/XML <Declaration> <ObjectProperty IRI="#hasCar"/> </Declaration> <InverseObjectProperties> <ObjectProperty IRI="#hasCar"/> <ObjectProperty IRI="#isCarOf"/> </InverseObjectProperties> <InverseFunctionalObjectProperty> <ObjectProperty IRI="#hasCar"/> </InverseFunctionalObjectProperty> <ObjectPropertyDomain> <ObjectProperty IRI="#hasCar"/> <Class IRI="#Person"/> </ObjectPropertyDomain> <ObjectPropertyRange> <ObjectProperty IRI="#hasCar"/> <Class IRI="#Car"/> </ObjectPropertyRange>
  • 19. Thank you! • Please refer to myrosh.com/owl-web-ontology- language-overview for more details on OWL Web Ontology Language, examples, info on OWL tools, real world use cases, references and more.