Ontology Languages and Tools

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Ontology Languages and Tools
Recent Developments and Research Challenges

• Ian Horrocks
• lthorrocks_at_cs.man.ac.ukgt
• Information Management Group
• School of Computer Science
• University of Manchester

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The Web Ontology Language OWL
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OWL History

• Semantic Web led to requirement for a web
  ontology language
• set up Web-Ontology (WebOnt) Working
  Group
• WebOnt developed OWL language
• OWL based on earlier languages RDF, OIL and
  DAMLOIL
• OWL now a W3C recommendation (i.e., a standard)
• OWL is a family of 3 languages OWL Lite, OWL DL
  and OWL Full
• OIL, DAMLOIL and OWL (DL Lite) based on
  Description Logics
• Has facilitated development of wide range of high
  quality tools infrastructure
• OWL now used in an increasing range of
  applications

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OWL Experiences and Directions

• Workshop at ESWC07 (Innsbruck, Austria, 6-7
  June)
• Brings together users, implementors and
  researchers
• Submissions include
• Enterprise Integration (Mitre)
• Product development (Lockheed Martin)
• Role based access control (NASA)
• Healthcare (SNOMED)
• Agriculture and fisheries (UN Food Agriculture
  Organization)
• Oral Medicine (Chalmers)

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What Are Description Logics?

• A family of logic based Knowledge Representation
  formalisms
• Descendants of semantic networks and KL-ONE
• Describe domain in terms of concepts (classes),
  roles (properties, relationships) and individuals
• Operators allow for composition of complex
  concepts
• Names can be given to complex concepts, e.g.

HappyParent Parent u 8hasChild.(Intelligent t
Athletic)
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Why (Description) Logic?

• OWL exploits results of 15 years of DL research
• Well defined (model theoretic) semantics

Quillian, 1967
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Why (Description) Logic?

• OWL exploits results of 15 years of DL research
• Well defined (model theoretic) semantics
• Formal properties well understood (complexity,
  decidability)

I cant find an efficient algorithm, but neither
can all these famous people.
Garey Johnson. Computers and Intractability A
Guide to the Theory of NP-Completeness. Freeman,
1979.
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Why (Description) Logic?

• OWL exploits results of 15 years of DL research
• Well defined (model theoretic) semantics
• Formal properties well understood (complexity,
  decidability)
• Known reasoning algorithms

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Why (Description) Logic?

• OWL exploits results of 15 years of DL research
• Well defined (model theoretic) semantics
• Formal properties well understood (complexity,
  decidability)
• Known reasoning algorithms
• Implemented systems (highly optimised)

KAON2
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Why the Strange Names?

• Description Logics are a family of KR formalisms
• Mainly distinguished by available operators
• Available operators indicated by letters in name,
  e.g.,
• S basic DL (ALC) plus transitive roles (e.g.,
  ancestor ? R)
• H role hierarchy (e.g., hasDaughter v hasChild)
• O nominals/singleton classes (e.g., Italy)
• I inverse roles (e.g., isChildOf hasChild)
• N number restrictions (e.g., gt2hasChild,
  63hasChild)
• Basic DL role hierarchy nominals inverse
  NR SHOIN
• The basis for OWL-DL
• SHOIN is very expressive, but still decidable
  (just)
• Decidable ? we can build reliable tools and
  reasoners

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Class/Concept Constructors

• C is a concept (class) P is a role (property) x
  is an individual name
• XMLS datatypes as well as classes in 8P.C and
  9P.C
• Restricted form of DL concrete domains

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Knowledge Base / Ontology Axioms
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OWL RDF/XML Exchange Syntax
E.g., Parent u 8hasChild.(Intelligent t Athletic)

• ltowlClassgt
• ltowlintersectionOf rdfparseType"
  collection"gt
• ltowlClass rdfabout"Parent"/gt
• ltowlRestrictiongt
• ltowlonProperty rdfresource"hasChild"/gt
• ltowlallValuesFromgt
• ltowlunionOf rdfparseType" collection"gt
• ltowlClass rdfabout"Intelligent"/gt
• ltowlClass rdfabout"Athletic"/gt
• lt/owlunionOfgt
• lt/owlallValuesFromgt
• lt/owlRestrictiongt
• lt/owlintersectionOfgt
• lt/owlClassgt

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Ontology Engineering
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Ontology Engineering Tasks

• Typical tasks in Ontology Engineering
• author concept descriptions
• refine the ontology
• manage errors
• integrate different ontologies
• (partially) reuse ontologies
• These tasks are highly challenging need for
• tool infrastructure support
• design methodologies

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Tools and Infrastructure

• Editors/environments
• Oiled, Protégé, Swoop, Construct, Ontotrack,

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Tools and Infrastructure

• Editors/environments
• Oiled, Protégé, Swoop, Construct, Ontotrack,
• Reasoning systems
• Cerebra, FaCT, Kaon2, Pellet, Racer,

Pellet
KAON2
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Tools and Infrastructure

• Editors/environments
• Oiled, Protégé, Swoop, Construct, Ontotrack,
• Reasoning systems
• Cerebra, FaCT, Kaon2, Pellet, Racer,
• Design methodologies
• Modularity, foundational ontologies, etc.

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Why Ontology Reasoning?

• Reasoning is an essential component of tools and
  services that help users and applications to
• Design and maintain high quality ontologies,
  e.g.
• Meaningful all named classes can have instances

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Why Ontology Reasoning?

• Reasoning is an essential component of tools and
  services that help users and applications to
• Design and maintain high quality ontologies,
  e.g.
• Meaningful all named classes can have instances
• Correct captures intuitions of domain experts

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Why Ontology Reasoning?

• Reasoning is an essential component of tools and
  services that help users and applications to
• Design and maintain high quality ontologies,
  e.g.
• Meaningful all named classes can have instances
• Correct captures intuitions of domain experts
• Minimally redundant no unintended synonyms

?
Banana split
Banana sundae
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Why Ontology Reasoning?

• Reasoning is an essential component of tools and
  services that help users and applications to
• Design and maintain high quality ontologies,
  e.g.
• Meaningful all named classes can have instances
• Correct captures intuitions of domain experts
• Minimally redundant no unintended synonyms
• Answer queries, e.g.
• Find more general/specific classes
• Retrieve individuals/tuples matching
  a given query

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Recent DevelopmentsLanguages
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OWL 1.1

• Is an extension of OWL
• community effort users and developers from OWLED
  workshop
• Is based on more expressive DL SROIQ
• (OWL is based on SHOIN)
• Now a W3C member submission
• See http//webont.org/owl/1.1/
• Is backwards compatible with OWL
• every OWL ontology is a valid OWL 1.1 ontology
• Every OWL 1.1 ontology not using new features is
  a valid OWL ontology
• Already supported by most popular OWL tools
• Protégé, Swoop, TopBraid, FaCT, Pellet

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Whats New in SROIQ?

• Q stands for qualifying number restrictions
• SROIQ allows for concepts (gtn R.C) and (6n R.C),
  e.g
• Person v Animal u 2 hasPart.Legs
• Car v 4 hasComponent.Wheel
• Person v 6 1 bio-parent.Male
• (SHOIN only allows for concepts (gtn R), and (6n
  R))

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Whats New in SROIQ?

• R stands for expressive role assertions
• new role inclusion assertions
• R1 o o Rn v S
• R1 o o Rn o S v S
• S o R1 o o Rn v S
• (with some restrictions on cycles)
• useful, e.g., for
• owns o hasPart v owns implies
  9owns.Bicycle v 9owns.WheelspartOf o locatedIn v
  locatedIn implies Fracture u 9locatedIn.FemurShaf
  t v Fracture u 9locatedIn.Femur
  hasParent o hasBrother v hasUncle

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Whats New in SROIQ?

• R stands for expressive role assertions
• Tra(R) (supported by SHOIN )
• Asy(R) e.g., Asy(properpartOf), Asy(hasParent)
• Sym(R) (supported by SHOIN )
• Refl(R) e.g., Refl(knows)
• Irrefl(R) e.g., Irrefl(properPartOf),
  Asy(hasParent)
• Disj(R S) e.g., Disj(hasParent hasSibling)

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What Else is New in OWL 1.1?

• Useful syntactic sugar
• DisjointUnion(C1 .... Cn)
• valueNot(marriedTo John)

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What Else is New in OWL 1.1?

• Extended datatype expressivity
• OWL 1.1 allows for user-defined datatypes
• Datatype(over18 base(xsdinteger)
  minInclusive("18"xsdinteger)),
• Class(Adult complete super(Person)
  restriction(age someValuesFrom(xsdinteger
  minInclusive("18"xsdinteger)))).
• n-ary datatype predicates e.g. greaterThan
• e.g., to define people who spend more than they
  earn
• BUT, we still cannot
• define complex relationships between data
  properties Women who earn more than their
  husbands.
• declare a datatype property as inverse-functional
  (keys).

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Tractable Fragments of OWL

• Why define fragments of OWL?
• Ease of use.
• Ease of implementation in tools.
• Trade-off between expressive power and
  computational properties
• Rule of thumb the more expressive power, the
  harder the reasoning.
• OWL 1.1 defines several different fragments with
  useful computational properties
• Reasoning complexity in range LOGSPACE to PTIME

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Recent DevelopmentsTools and Methodologies
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Tools and Methodologies

• OWL 1.1 support already added to several tools
• Protégé, Swoop, TopBraid Composer, FaCT, Pellet
• New features available (soon) in OWL tools
• Incremental classification (addition and
  retraction)
• Conjunctive query answering
• Semi-automatic repair of errors
• Support for integration and modular design

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Modularity in Software Engineering

• Typically referred to as the extent to which
  software is divided into components with
• high internal cohesion
• controlled coupling between each other through
  simple interfaces (encapsulation)
• Benefits of modular software design
• software maintainability
• software understandability

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Modularity in Ontology Engineering

• Benefits of a modular ontology design to
  simplify
• ontology refinement/update
• modifying a module should not lead to
  modifications in parts of the ontology that are
  not conceptually related
• understanding
• relationships between different modules in an
  ontology controlled and well-understood
• integration with other ontologies
• no unexpected consequences
• partial reuse
• reuse only the relevant part/module of an
  ontology

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Tool Support for Modular Design

• Extract smaller modules from large ontologies
• E.g., starting with FMA, extract module for
  Heart
• Tool ensures that module
• Is as small as possible, but
• Still contains all relevant knowledge
• Check when integration of modules is safe
• Interface via exported vocabulary
• Information flows from imported to importing
  ontology
• No information flows back the other way

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Q 1 CysticFibrosis v Fibrosis u
9locatedIn.Pancreas u 9hasOrigin.GeneticOr
igin 2 GeneticFibrosis v Fibrosis u
9hasOrigin.GeneticOrigin 3 Fibrosis u 9
locatedIn. Pancreas v GeneticFibrosis 4
GeneticFibrosis v GeneticDisorder
Q ² CysticFibrosis v Genetic Disorder
P Q ² gt v Project
P Q ² gt v 9 hasFocus.gt
P Q ² GeneticFibrosis t GeneticDisorder v ?
P Q ² CysticFibProject v GenDisorderProject
P 1 GenDisorderProject Project u
9hasFocus.GeneticDisorder 2 CysticFibProject
Project u 9hasFocus.CysticFibrosis 3 9hasFocus.gt
v Project 4 Project u (GeneticFibrosis u
GeneticDisorder) v ? 5 8 hasFocus.CysticFibrosis
v 9hasFocus.GeneticDisorder
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Research Challenges
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Increasing Expressive Power

• Database style keys Lutz et al, JAIR 2004
• E.g., make model chassis-number is a key for
  Vehicles
• Rule language extensions
• W3C RIF WG (see http//www.w3.org/2005/rules/)
• First order extensions (e.g., SWRL) Horrocks et
  al, JWS, 2005
• Hybrid language extensions, e.g., Eiter et al,
  KR-04 Motik et al, ISWC-04 Rosati, JoWS, 2005
• LP/F-Logic/Common Logic Chen et al, JLP, 1993
  de Bruijn et al, WWW-05
• Other extensions
• Extended annotation framework
• Macro language
• Temporal
• Fuzzy

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Improving Scalability

• Optimisation techniques
• Improve performance of DL reasoners, e.g., Sirin
  et al, KR-06
• Reduction to disjunctive Datalog Motik et at,
  KR-04
• Transform SHOIN ontology to DatalogÇ rules
• Use LP techniques to deal with large numbers of
  ground facts
• Hybrid DL-DB systems Horrocks et al, CADE-05
• Use DB to store Abox (individual) axioms
• Cache inferences and use DB queries to
  answer/scope logical queries
• Polynomial time algorithms for sub-ALC logics
• Graph based techniques for EL Baader et al,
  IJCAI-05
• Database techniques for DL-Lite Calvanese et al,
  AAAI-05

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Summary

• OWL now being used in a wide range of
  applications
• e-Science, medicine, geography, geology,
• Reasoning enabled tools are of crucial importance
  
• For both design and deployment of ontologies
• Large and extremely active RD area
• Language extensions (OWL 1.1)
• New and improved tools methodologies
• Research challenges remain
• But tools now mature enough for prime time
  applications

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Acknowledgements

• Thanks to
• Bernardo Cuenca Grau
• Bijan Parsia

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Resources
Thank you for listening
Any questions?

• FaCT system (open source)
• http//owl.man.ac.uk/factplusplus/
• OWL
• http//www.w3.org/TR/owl-features/
• OWLED Workshop
• http//owled2007.iut-velizy.uvsq.fr/

• Protégé
• http//protege.stanford.edu/plugins/owl/
• OWL 1.1 Proposal
• http//webont.org/owl/1.1/
• Slides Tutorial
• http//www.cs.man.ac.uk/horrocks/nsd07/